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Author Archives: Micaela Mcpadden

  1. Vertical AI Platforms Are Turning Horizontal — Here’s Why It Matters

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    Over the past year, everyone’s been talking about the “verticalization of AI.” And honestly, it makes sense. Big, powerful language models are impressive, but the real magic happens when AI knows your industry inside out.

    At Aquant, we see this all the time in service and manufacturing. Vertical AI tools beat generic models because they speak the industry’s language, understand the nitty-gritty of daily workflows, and can handle complex rules and regulations without breaking a sweat.

    But that’s not where the story stops. Actually, we’re just getting started. We’re entering a new era where vertical AI platforms are turning into horizontal systems within their industries, thanks to multi-agent architectures.

    Towards the end of the year and into next year, we’ll start to see more of this. Let me break that down.

    From Vertical Point Solutions to Horizontal Industry Platforms

    Many vertical AI startups began with a single purpose: generate legal briefs, summarize medical notes, analyze machine failures, or optimize a sales quote. Many of these tools delivered immediate ROI because they solved well-defined pain points.

    But over time, businesses demand more. They want:

    • A single platform where all their knowledge lives
    • Seamless orchestration of multi-step workflows
    • AI agents that don’t just answer questions but take action
    • Insights delivered proactively, not merely on request

    In other words, they want horizontal capabilities…but built specifically for their vertical.

    Think of it this way: a legal AI platform isn’t just a chatbot anymore. It’s becoming an entire environment where:

    • One agent researches case law
    • Another drafts documents
    • Another handles compliance checks
    • Another manages billing or time-tracking
    • All working in harmony under one secure roof

    This is horizontal functionality, but laser-focused on the legal domain.

    Why Multi-Agent Architectures Are the Key

    Why not just use one giant AI model to handle everything? Because single models, even powerful ones, hit limits:

    • They can’t juggle complex workflows
    • They struggle to maintain context over long business processes
    • They’re inefficient at specialized tasks that require different reasoning or domain expertise

    Enter multi-agent systems. Instead of one AI doing everything, you have specialized “agents,” each optimized for a narrow function. These agents collaborate, share context, and execute complex sequences of tasks, just like teams of human experts. And when built into vertical platforms, they transform industry-specific workflows in ways a single general-purpose AI never could.

    The Rise of the Vertical Operating System

    This shift is leading to what I’d call vertical operating systems. We’re seeing it everywhere:

    Healthcare: Epic started as an electronic health records (EHR) system, a vertical point solution. Today, it’s transforming into a horizontal healthcare platform. It connects patient records, billing, scheduling, telehealth, clinical decision support, population health analytics, and even patient engagement apps, all working together across the healthcare ecosystem. It’s no longer just records; it’s the backbone of operations for many hospitals and clinics.

    Finance: Originally a vertical point solution for document review, Eigen Technologies started out focused on extracting key data from financial contracts. Over time, they’ve evolved into a horizontal platform for financial institutions, offering tools for data extraction, risk analysis, compliance monitoring, regulatory reporting, and integration across multiple business processes, turning their initial vertical solution into a broader platform that supports diverse financial workflows.

    Manufacturing and Service: At Aquant, we’re building a system where:

    • One agent analyzes service tickets for early signals of equipment failure
    • Another recommends troubleshooting steps
    • Another assists in parts ordering
    • Another trains new technicians based on real-world data

    All these specialized agents are interconnected inside a single platform designed for the complex world of service and manufacturing. Instead of scattered tools solving isolated problems, we’re creating a horizontal ecosystem that covers the entire service lifecycle, from diagnosis to repair to workforce development, tailored specifically for this industry’s unique challenges.

    These platforms are horizontal because they span many business functions, but vertical because they’re engineered for the unique language, data, and regulations of their industry.

    This evolution isn’t theoretical. It’s transforming how businesses plan their AI investments. Here’s why:

    Better ROI: Multi-agent vertical platforms can tackle entire workflows, not just single tasks, delivering exponential value.

    Higher Trust: Vertical systems are built on industry-specific data and logic, increasing accuracy and reducing hallucinations.

    Seamless Integration: Businesses don’t want twenty disconnected AI tools. They want a unified platform with agents that work together.

    Competitive Advantage: Companies who adopt vertical operating systems will leap ahead, because they’ll turn domain expertise into automated, scalable intelligence.

    What’s to Come?

    The future of AI isn’t purely horizontal or purely vertical. It’s horizontally integrated systems built for vertical excellence. At Aquant, this is exactly the future we’re building toward. We believe the real power of AI lies not only in answering questions, but in orchestrating the entire flow of work across the enterprise, guided by the unique context of each industry.

  2. How Businesses Can Avoid Becoming Part of Gartner’s Predicted 40% Failure Rate in Agentic AI Projects

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    Agentic AI is rapidly moving from buzzword to boardroom priority. These autonomous systems, capable of making decisions and acting independently, promise to transform how businesses operate. But alongside the excitement comes a stark warning. Gartner recently predicted that nearly 40% of agentic AI projects will fail by 2027.

    That’s a pretty stark number!

    A lot of the reasons are the usual suspects we’ve seen in past tech hype cycles: high costs, unclear ROI, and shaky risk management. But there’s another, deeper reason so many agentic AI projects hit a wall, there’s often a gap between the huge potential of the technology and the messy, industry-specific challenges companies actually face day to day.

    I’ve been building an AI platform for service organizations for years, and I’d like to share some insights into why these projects often stumble, and what businesses can do to avoid ending up as another Gartner statistic.

    Why Agentic AI Projects Fail

    Many companies underestimate how deeply AI needs to understand a business’s unique language, workflows, and edge cases. It’s tempting to bolt a large language model (LLM) onto a vector database and call it a day. But real-world problems demand much more than generic chatbots.

    Too often, businesses try to build “one-size-fits-all” agents without domain expertise. The result? Tools that sound impressive in demos but fall apart when faced with the nuances of actual operations. Costs quickly spiral out of control, and trust erodes fast when AI makes the wrong call in a critical workflow.

    Proving ROI Starts Small

    Agentic AI’s potential is enormous, but that doesn’t mean you should try to automate everything at once.

    My advice: start small and focused.

    Pick one high-pain process, like troubleshooting equipment failures, and pilot an agent there. Measure tangible outcomes: time saved, costs reduced, and, crucially, whether people actually use the solution. ROI isn’t purely financial; it’s also about adoption and trust.

    Equally important is designing AI to fit real-world conditions. If technicians have greasy hands, voice or call-based interfaces might work better than touchscreens. If they’re working underground or offline, the AI needs to function without connectivity. Successful ROI hinges on practical, accessible tools that integrate seamlessly into daily workflows.

    Keep Humans in the Loop

    One of the biggest risks with agentic AI is letting it operate autonomously in complex scenarios without oversight.

    Businesses must design human-in-the-loop processes where experts can review, override, or validate AI decisions. This isn’t just a safety net, it’s essential for building trust and accountability.

    Industries Poised for Agentic AI Success

    At Aquant, we see massive potential for agentic AI in service organizations, particularly those supporting complex machinery — medical devices, industrial machinery, and more.

    These industries generate huge volumes of data. Service teams are under relentless pressure to keep critical equipment running. Here, agentic AI can:

    • Troubleshoot equipment issues
    • Recommend solutions
    • Preserve institutional knowledge as experienced technicians retire (a whole lot of them are)

    When paired with domain-specific expertise, agentic AI isn’t just helpful, it’s transformative. Beyond troubleshooting, it can automate many manual tasks, freeing professionals to focus on what matters most: keeping machines running and customers satisfied.

    Balancing Innovation with Ethics and Compliance

    The excitement around agentic AI can sometimes overshadow important ethical and regulatory considerations.

    Businesses must be transparent about what AI knows, and what it doesn’t. Don’t deploy agents into critical processes without clear guardrails. Document data sources, decision logic, and who’s ultimately accountable. And make sure your agents can explain how they reach conclusions.

    In regulated industries, explainability and traceability are non-negotiable. AI should never hallucinate answers, it should always cite its sources.

    Culture and Leadership Matter More Than Technology

    Here’s a truth I’ve seen repeatedly: technology alone doesn’t determine success. Culture does.

    AI projects fail when leadership treats them like plug-and-play tools. Change management is critical. Buy-in from leadership and a culture open to change are essential. Frontline teams must trust the AI, and that trust is earned by involving them early and ensuring the solution fits how they actually work.

    The Future of Agentic AI is About Governance

    Fortunately, new tools are emerging to help businesses plan, monitor, and govern agentic AI initiatives responsibly. At Aquant, we’re part of this shift.

    Modern platforms now let companies build agents without starting from scratch, using pre-trained domain knowledge and built-in guardrails. Tools for monitoring prompt drift, managing knowledge sources, and auditing AI decisions are evolving quickly.

    Agentic AI has the potential to revolutionize industries. But realizing that potential requires more than technology. it demands focus, domain expertise, risk management, and a culture ready to embrace change.

    Don’t let your business become part of Gartner’s predicted 40% failure rate. Start small, stay focused, and build solutions that truly fit your industry’s needs.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  3. Aquant Introduces Retrieval-Augmented Conversation (RAC), Ushering in a New Era for Outcome-Oriented Enterprise AI

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    Aquant today announced the official launch and unveiling of Retrieval-Augmented Conversation (RAC). This groundbreaking AI model leaps beyond conventional enterprise problem-solving by going beyond brief one-shot answers to fully guided, context-aware dialogue.

    “RAG explains a solution, but RAC guides you towards an outcome,” said Indresh Satyanarayana, VP of Product Technology & Labs at Aquant. “We created RAC to mimic the minds of the best technicians in the business: asking clarifying questions, taking context from work history, parts data, company-specific objectives, and real-time telemetry, then guiding each user through the next best step until the root cause of the problem is solved.”

    RAC is the subsequent development in the evolution of Retrieval-Augmented Generation (RAG), which changed enterprise AI by grounding outputs on trusted documentation. However, in advanced, high-stress environments like field service, customer experience, and complex machinery maintenance, RAG-based solutions are beginning to show their faults more and more, failing to resolve real-world issues that require conversation, complexity, and multi-step action.

    “Enterprise teams don’t need another smart search bar; they need an expert partner by their side in every conversation,” said Assaf Melochna, President and Co-Founder of Aquant. “RAC transforms AI from a passive responder to a proactive problem-solver. It’s not just about reducing resolution times. It’s about allowing frontline service teams to fix issues faster, with more confidence, and with less escalation.”

    In contrast to traditional RAG systems that return long, static answers from manuals or FAQs, RAC actually engages in multi-turn dialogues. It retrieves and makes sense of real-time data such as IoT readings, ERP history, and job logs, and adjusts its communication according to user experience level and operational context.

    Today, Aquant launched RAC to the public on a live webinar, where leaders walked through the technology’s architecture, real-world use cases, and the quantifiable results RAC will deliver. To learn about RAC in detail, you can read Aquant’s whitepaper, Beyond RAG: Why Enterprises Need Retrieval-Augmented Conversation (RAC), by RAC creator Indresh Satyanarayana.

  4. Beyond RAG: Why Enterprises Need Retrieval-Augmented Conversation (RAC)

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    Why Conversational AI Is the Next Frontier for Field Service

    Retrieval-Augmented Generation (RAG) became the first practical method for applying generative AI—powered by large language models (LLMs)—within enterprise workflows. It grounds every answer in a live document so hallucinations drop and auditors can click the source. But RAG is still one-shot intelligence: you ask, it answers, full stop.

    Real-world service and support workflows seldom end in one volley. Diagnostics, troubleshooting and compliance reviews are inherently conversational:

    “Which firmware are you on?”

    “Did tightening the bolts clear the sensor fault?”

    “Let’s confirm the client’s region before we quote that regulation.”

    A system that cannot probe, remember and confirm leaves humans to stitch the gaps. Aquant’s Retrieval-Augmented Conversation (RAC) design wraps the same retrieval engine inside a multi-turn, memory-aware loop that keeps asking, keeps learning and keeps refining until the job is done—or hands off to a human with full context.

    The sections below explain RAG’s value, diagnose its limitations, introduce Aquant RAC, illustrate it in action, lay out the reference architecture Aquant deploys in production, walk through a staged rollout on the Aquant platform, and—new in this edition—show how RAC unlocks true multichannel AI across web, mobile, voice and collaboration tools.

    Understanding RAG: How Retrieval-Augmented Generation Delivers Fast, Trustworthy Answers

    1.1 How Retrieval-Augmented Generation (RAG) Works—Explained Simply

    Imagine walking into a factory’s expert tool room. A technician says:

    “I’m about to service the hydraulic press—what torque spec do I need?”

    The tool clerk:

    • Heads straight to the drawer with specs for that family of machines

    • Flips to the exact sheet for the technician’s configuration

    • Reads off the torque requirement and jots it down

    • Hands over both the note and the reference so the tech can double-check

    That’s RAG—in human form. In software, it works like this:

    Step What Happens in Software
    Encode The user’s question is converted into a vector that captures its meaning.
    Retrieve A vector search engine finds the most relevant passages from the enterprise knowledge base.
    Generate Those passages are added to a prompt and passed to an LLM, which crafts a natural-language answer.
    Return The final response—plus clickable source citations—is delivered to the user.

    (Figure 1 illustrates the flow.)

    1.2 Four Proven Benefits of RAG That Win Over CIOs

    Benefit Why It Matters
    Grounded, Trusted Answers Results are backed by real documents—hallucinations drop significantly.
    Instant Knowledge Updates Update a PDF or policy doc, re-index it, and the AI reflects the change immediately.
    Auditability & Compliance Every answer includes a citation—perfect for QA teams and regulators.
    Low MLOps Overhead No retraining needed—just maintain your document source. A single retrieval engine supports multiple LLMs.

    1.3 Best-Fit Use Cases for RAG: When a Quick Answer Is All You Need

    RAG is a perfect fit when users just want accurate information—fast. A few examples:

    • Error code lookup: A field tech sees a common HVAC error code. RAG instantly explains what it means.

    • Diagram retrieval: A telecom engineer asks for a router wiring diagram. RAG surfaces the exact configuration.

    • Calibration help: A maintenance worker needs to verify steps to calibrate a pressure sensor. RAG delivers the procedure.

    In each case, the goal is the same: “Just tell me what I need to know.” Once the answer arrives, the job is done.

    2. The Limits of One-Shot AI: Why RAG Falls Short in Complex, Outcome-Driven Workflows

    Retrieval-Augmented Generation (RAG) delivers fast, grounded answers. But in real-world service and support environments, success isn’t measured by how good the answer looks—it’s measured by whether the problem gets solved.

    Here’s where RAG falls short when outcomes matter most.

    2.1 Ambiguity: When RAG Doesn’t Know What It Doesn’t Know

    A field technician types:

    “Pump 12 overheating—advice?”

    But critical context is missing:

    • What’s the fluid type?

    • What’s the inlet temperature?

    • What’s the pump’s maintenance history?

    RAG doesn’t ask. It just retrieves and responds. The tech may get an answer—one that sounds confident—but it’s likely incomplete or incorrect.

    And there’s no chance for the system to clarify:

    “What’s the current coolant pressure?”

    Without that follow-up, RAG can’t bridge the gap between symptom and solution.

    2.2 RAG’s Conversation Blind Spot: No Follow-Ups, No Fixes

    Most troubleshooting is inherently interactive. Humans solve problems by probing:

    “Do you hear a humming noise?”
    “Which firmware version are you running?”
    “Have you tried a manual reset?”

    But RAG doesn’t ask questions—it just answers once and stops. If that answer doesn’t resolve the issue, users are left to rephrase and try again.

    The result? Friction. And frustrated teams who quickly lose trust in the tool.

    2.3 RAG Isn’t Outcome-Aware: It Doesn’t Know If the Problem Is Solved

    RAG’s success metric is simple: return a well-written answer.

    But businesses care about:

    • MTTR – Mean Time to Repair

    • FTF – First-Time Fix Rate

    • CSAT – Customer Satisfaction

    A great-sounding paragraph that fails to resolve the issue? It doesn’t move any of those needles.
    RAG doesn’t check if the outcome was achieved—because it wasn’t designed to.

    2.4 RAG Can’t Access the Real Answers—Because They’re Not in the Docs

    Many mission-critical answers don’t live in PDFs. They live in:

    • ERP systems

    • Job history records

    • Sensor feeds

    • Asset maintenance logs

    A technician might ask:

    “Can I get this part now?”
    “Was this issue fixed last time?”

    RAG, restricted to static documents, can only guess. Answers may sound convincing but lack operational value—because they miss the real data that drives the right decision.

    2.5 Real-World Risks of RAG-Only Workflows

    When RAG operates without follow-ups or access to real-time context, the downstream consequences add up fast.

    Symptom Business Impact
    Escalation overload Tier-2 engineers are flooded with avoidable tickets.
    Knowledge distrust Users stop trusting the AI and fall back on PDFs and tribal knowledge.
    Shadow workflows Teams turn to Slack, WhatsApp, or phone calls—fragmenting knowledge capture.
    Cognitive overload Users are overwhelmed with information but get no help figuring out what matters.

    Bottom Line: RAG delivers static answers. But support, diagnostics, and compliance are dynamic. Without the ability to ask, clarify, and confirm—RAG falls short.

    3. Introducing RAC: How Retrieval-Augmented Conversation Drives Real Outcomes

    RAG gives you a one-shot answer. But real-world service and support problems are messy. They require back-and-forth, context, and clarification.

    That’s where Retrieval-Augmented Conversation (RAC) comes in. It combines the grounding power of RAG with multi-turn dialogue and persistent memory—closing the gap between question and resolution.

    3.1 From Answer to Action: Upgrading the Tool Room Clerk with RAC

    Let’s return to our tool-room analogy.

    Now imagine a conversation coach stands beside the clerk.

    When the technician’s request is vague, the coach nudges:

    “Which press model are you working on? What symptoms are you seeing?”

    If the first reference doesn’t solve the issue, the coach digs deeper:

    “Did you already bleed the hydraulic line? Let’s check the troubleshooting bulletin.”

    And when the tech says, “Still jammed,” the coach keeps going:

    “Try this. Let’s rule out that sensor next.”

    This loop—clarify → retrieve → guide—continues until:

    • The issue is resolved, or

    • A senior engineer takes over, fully briefed

    That’s what RAC does: wraps each retrieval in a smart, memory-aware conversation that actually drives outcomes.

    3.2 Inside the RAC Loop: How Aquant’s AI Reaches Resolution Step-by-Step

    RAC replaces static Q&A with an intelligent, iterative loop—blending retrieval and reasoning across every turn.

    Turn What Happens Under the Hood What the User Sees
    1 RAC encodes the question, retrieves data, and generates a reply plus a follow-up. “Do you hear a high-pitched hum?”
    2 User answers. Dialogue Manager logs the fact; Query Generator refines the next search.
    3 New, narrower retrieval kicks in. “Try torquing the rotor bolts to 65 Nm.”
    … The cycle continues: clarify, guide, confirm.
    Final RAC confirms the issue is resolved or escalates with full context. “Great—error cleared. Closing your ticket now.”

    Each turn builds context. Each answer gets smarter. And the user moves closer to resolution.

    3.3 Why Dialogue Is Essential to Solving Complex Problems

    Complex service issues rarely resolve in a single step.

    They require a process of:

    • Asking clarifying questions

    • Verifying next steps

    • Confirming outcomes

    RAC does this naturally. It asks things like:

    “Did that fix the issue?”
    “What’s the part number on the unit?”
    “Are you hearing a click or a hum?”

    Each question narrows the possible cause. In equipment diagnostics, this iterative narrowing—from vague symptom to precise fix—is critical.

    Expecting a single-shot answer to handle this complexity is not only unrealistic—it’s a recipe for escalation and delay.

    RAC Adapts to Your Team

    The RAC loop also adjusts based on the persona interacting with it:

    • A junior tech might get more step-by-step guidance.

    • A field veteran sees streamlined suggestions.

    • A call center agent receives co-piloted probing and next-best actions.

    RAC learns how much help each user needs—and adapts in real time.

    RAC as a Coach, Not Just a Bot

    With RAG, users get an answer. If it works, great. If not—they’re stuck.
    But RAC acts like a diagnostic coach.

    It teaches users:

    • What questions to ask

    • How to think through a problem

    • Why a solution works

    The result?

    • Faster onboarding

    • Smarter technicians

    • Fewer avoidable escalations

    3.4 RAG vs. RAC: A Side-by-Side Comparison for Enterprise Service Teams

    Capability RAG RAC (Aquant-style)
    Interaction Style One-shot question & answer Continuous, context-aware dialogue
    Clarification None Proactively probes for missing details
    Memory Limited to a single query Remembers facts, context, and user intent across turns
    Retrieval Strategy One-time document fetch Multi-round retrievals refined by user inputs
    Guidance Approach Bulk response Step-by-step coaching—one action per turn
    Success Metric “Answer displayed” “Issue resolved / Outcome confirmed”

    3.5 Feeding RAC Better Data = Better Decisions

    Most RAG systems retrieve from static documents only. But RAC can handle far more—if it’s fed the right data.

    Say a tech asks: “Which part should I swap?”
    RAC can weigh:

    • Price differences

    • Parts availability

    • Remote repair options

    • Common fix success rates

    By augmenting the retriever with parts catalogs, cost/time data, fix history, and field insights, RAC becomes decision-aware, not just document-aware.

    This unlocks the most efficient, cost-effective, and field-ready solution—not just the first plausible one.

    4. Real-World RAC Scenarios: From Field Techs to Customer Support

    In complex, outcome-driven environments, a single-shot answer is rarely enough. RAC shines in these scenarios—driving resolution by guiding users through every step of the process.

    Below are real examples that show how RAC delivers results across field service, telecom, healthcare, legal, and IT support.

    4.1 Field Service Example: Diagnosing a Turbine Alarm

    Turn User Input / Bot Output Behind the Scenes
    1 Tech: “Turbine A17 shows E104.” LLM detects and logs: machine = A17, error code = E104
    2 RAC-bot: “E104 often means load imbalance or sensor drift. Do you hear a high-pitched hum?” Retriever pulls bulletins 1259 (load) and 907 (sensor)
    3 Tech: “Yes, loud hum.” Dialogue Manager logs symptom = hum; Query Generator narrows the search
    4 RAC-bot: “Torque rotor bolts to 65 Nm, then run a 5-minute test.” Retrieves procedure from TSB 1259
    5 Tech: “Done.”
    6 RAC-bot: “Error cleared?”
    7 Tech: “Cleared.” Outcome = resolved; API triggers ticket closure and logs parts usage
    8 RAC-bot: “Great—logging first-time fix. Have a safe day!”

    4.2 Telecom Example: Resolving Wi-Fi Dropouts

    Turn User Input / Bot Output RAC Actions
    1 Customer: “My internet drops every evening.” Dialogue Manager opens a new case; missing slots include connection-type and device count
    2 RAC-bot: “Are you using Wi-Fi or a cable connection?” Probing logic identifies connection-type = unknown and asks for clarification
    3 Customer: “Wi-Fi.” Logs connection-type; narrows search using Wi-Fi filter
    4 RAC-bot: “Is the problem on one device or several?” Probes for device count
    5 Customer: “Several.” Logs multi-device issue; retriever pulls firmware bug related to load balancing
    6 RAC-bot: “I’ve pushed a firmware patch to your router. Restart it and stream a video for 5 minutes.” OTA update triggered; step-by-step instructions generated
    7 RAC-bot (5 min later): “Any interruptions so far?” RAC polls telemetry; detects packet-loss < 1%
    8 Customer: “No, it’s stable now.” Outcome = resolved; triggers CSAT survey and closes ticket

    4.3 Additional RAC Use Cases Across Industries

    Healthcare Triage

    • RAC-bot asks for symptom duration, severity, and current medications

    • Retrieves evidence-based triage protocols tailored to patient profile

    Legal Drafting

    • RAC-bot confirms jurisdiction (“Is this for the US or EU?”)

    • Surfaces region-specific statutes and prevents cross-border citation errors

    Internal IT Help Desk

    • RAC collects OS version and error code

    • Retrieves patch instructions, verifies reboot success

    • Average handling time cut in half

    Outcome: RAC adapts to any industry where resolution depends on clarifying details and guiding users to the right next step.

    Whether it’s diagnosing turbine alarms, patching routers, triaging health symptoms, or fixing laptops—RAC turns multi-step problems into resolved tickets.

    5. Inside the Aquant Platform: RAC Reference Architecture Explained

    RAC isn’t just a concept—it’s live and running across some of the world’s most complex service organizations. And it doesn’t require stitching together multiple tools or vendor ecosystems.

    Aquant’s full RAC loop is built into its platform—allowing fast, scalable deployment with no outside orchestration.

    Here’s how it works under the hood.

    RAC Reference Architecture

    Layer Primary Role What That Means for You
    Conversation Interface Provides omnichannel chat and voice UI; supports token streaming for fast response times Deploy RAC across web widgets, mobile apps, voice, Teams, and Slack—no new front-end builds needed
    Dialogue Manager Maintains turn-by-turn memory, tracks intent and outcomes, triggers probes RAC remembers the full conversation—so users never need to repeat themselves
    Query Generator Reformulates focused search queries using case state and chain-of-thought logic Smarter search at every step—retrieval gets sharper with each turn
    Dynamic Retriever Combines vector and keyword search across enterprise knowledge graph, ERP, and history logs Connects to all relevant data—not just PDFs—to generate answers that reflect reality
    Generative Engine Crafts grounded, stepwise instructions; always cites source documents No hallucinations—just clean, auditable guidance built on real documentation
    Outcome & Escalation Logic Verifies issue resolution, logs parts/labor, escalates when needed Escalates only when automation stalls—while maintaining full context for human agents
    Analytics & Feedback Lake Stores conversations, retrieval hits, outcomes, and user behavior Turns every interaction into a data point—fueling continuous improvement and smarter AI

    Fully Integrated. No Vendor Lock-In.

    Unlike most enterprise AI deployments, RAC doesn’t depend on external orchestration platforms or custom integration layers.

    Everything runs on the Aquant platform.

    No additional vendors. No fragile pipelines. No separate tools to train, monitor, or govern.

    From retrieval and generation to memory and escalation, Aquant delivers the full RAC stack—ready for production.

    6. How to Deploy RAC: A Step-by-Step Playbook for Success

    RAC is designed for fast rollout and measurable results. Aquant customers move from prototype to production using a clear, structured playbook—without needing extensive model training or orchestration layers.

    Here’s how to deploy RAC on the Aquant platform for immediate business value.

    Stage 0 – Choose the Right Use Case

    Start with one outcome-heavy workflow.

    • Example: Compressor over-temperature alarms

    • Identify high-impact, high-friction areas where resolution speed matters most

    • Baseline key KPIs:

      • MTTR (Mean Time to Repair)

      • FTF (First-Time Fix Rate)

      • CSAT (Customer Satisfaction)

    Why it matters: This gives you a measurable before-and-after snapshot to prove RAC’s value.

    Stage 1 – Clean and Curate Your Knowledge

    Ensure your knowledge base is ready for retrieval.

    • Ingest structured and unstructured data via Aquant’s pipeline:

      • PDFs

      • HTML

      • CRM and service history data

    • Deduplicate content and tag with relevant metadata

    • Automatically flag obsolete SOPs for archiving

    Why it matters: Cleaner data = sharper answers = faster resolutions.

    Stage 2 – Prototype Your First RAC Skill

    Test the RAC loop in a controlled sandbox.

    • Use the Aquant Console to:

      • Create a RAC Skill

      • Fine-tune prompts based on your business language and KPIs

      • Run real queries with synthetic or test users

    Why it matters: A working prototype builds internal alignment and ensures accuracy before going live.

    Stage 3 – Controlled Launch with Agent Assist

    Deploy RAC with human-in-the-loop guardrails.

    • Enable the Agent-Assist Overlay:

      • RAC makes the suggestion

      • Human agents review/edit before responding

    • Monitor:

      • Containment rate

      • MTTR improvement

      • Agent satisfaction

    Why it matters: This step builds trust—agents learn RAC’s value while still owning the customer experience.

    Stage 4 – Enable Autonomous Mode

    Let RAC auto-resolve issues that meet performance thresholds.

    • Activate Auto-Resolve for Tier-1 categories where:

      • Containment ≥ 80%

      • CSAT ≥ 4.5

    • Human escalation path remains in place for exceptions

    Why it matters: This is where the real ROI kicks in—cut resolution times, reduce escalations, and boost CSAT.

    Stage 5 – Drive Continuous Improvement

    Use real interactions to train and optimize RAC.

    • Surface low-confidence responses via Aquant Insight Dashboards

    • Retrain embeddings or tweak prompts with one-click retraining

    • Run prompt A/B tests directly in the console

    • Control costs with built-in model caching and batching

    Why it matters: Every conversation becomes a feedback loop. RAC gets smarter over time—with zero model retraining.

    Built-In Governance and Compliance

    Security is embedded, not bolted on.

    • Role-based access controls (RBAC)

    • PII redaction

    • GDPR-compliant data retention

    • On-demand audit exports via Aquant Console

    Why it matters: You can deploy RAC confidently in regulated or sensitive environments.

    From day one to self-sufficiency, RAC follows a proven, repeatable path to value.
    Whether you’re solving service tickets, triaging support issues, or enabling field techs, this playbook scales with you.

    7. Unlocking Multichannel AI with RAC: One Engine, Every Channel

    RAC is designed to meet users where they are—whether that’s in a headset on the factory floor, inside a web app, or in a WhatsApp chat at 2 a.m.

    Because RAC is inherently conversation-native, it maintains full context across platforms, learns from every interaction, and drives toward resolution—no matter the interface.

    7.1 Conversation Everywhere: How RAC Works Across All Interfaces

    RAC supports seamless communication across every modern support channel—ensuring consistent guidance, persistent memory, and instant access.

    Channel Real-World Example Why It Matters
    Web RAC powers a chatbot inside a technician portal Instant help without leaving the primary workspace
    Mobile iOS and Android apps use push notifications to re-engage users mid-session Keeps workflows moving even when users are on the go
    Voice A field tech taps a headset: “Aquant, pump 12 overheating.” RAC listens, probes, and guides via audio Enables hands-free troubleshooting in loud or hazardous environments
    Collaboration Apps A RAC-powered bot lives in Teams or Slack and maintains full context inside each thread Eliminates switching tools—adoption soars when AI lives where users already work
    Consumer Messaging WhatsApp or WeChat chats route to RAC, enabling conversational troubleshooting in familiar UIs Makes support more accessible, especially in global or frontline settings
    Video Meetings A rep launches RAC in Zoom or Teams; it listens, retrieves, and recommends next steps in real time Enhances live support calls with instant, contextual intelligence

    7.2 Why Multichannel Matters: Business Impact Beyond Convenience

    True multichannel support isn’t just about flexibility—it’s about enabling smarter, faster service across every touchpoint. RAC delivers real business value in every scenario.

    Capability What It Looks Like Why It Matters
    Frictionless Access A tech says: “Aquant, read me the purge sequence.” No apps, no typing—just an instant audio reply Supports real-time action on the shop floor or in the field
    Context that Travels A voice session drops mid-step. RAC picks up in the mobile app without losing memory No re-explaining; RAC remembers the problem and the progress
    24/7 Global Reach A customer in Tokyo messages RAC via LINE at 2:00 a.m. your time Enables global support without scaling headcount
    In-Flow Work Support Engineers receive verbal safety instructions via Bluetooth while keeping hands on equipment Supports compliance while maintaining physical safety in motion-intensive work
    Zero Learning Curve A customer sends a WhatsApp voice note. RAC replies and asks for a photo of the serial plate Support fits into users’ existing behavior—no new tools to learn
    Seamless Integrations A Teams message spawns a ServiceNow incident. A voice command logs labor. A chat triggers a part check RAC integrates across systems—IT doesn’t need to build custom adapters
    Unified Analytics Every chat, call, and note flows into one transcript lake with channel tags Enables consistent benchmarking, root cause analysis, and AI training
    Captured Field Knowledge Informal phone calls and WhatsApp threads are now structured, searchable data in Aquant Converts tribal knowledge into institutional memory

    7.3 How Aquant Makes It Work

    Aquant’s multichannel architecture is built for speed, scale, and enterprise-grade governance—out of the box.

    • Unified Conversation Gateway
      One WebSocket and one telephony adapter feed all channels into the Dialogue Manager

    • Channel-Aware Prompts
      RAC auto-formats output for each medium (Markdown, plain text, or SSML for voice)

    • Secure Hand-Offs
      Channel tokens map to enterprise identities, maintaining role-based permissions across platforms

    • Analytics Convergence
      Every message, audio exchange, and escalation flows into a single transcript lake—allowing you to analyze performance across every surface

    Bottom Line: RAC offers one reasoning engine, one memory layer, and one feedback loop—deployed across every interface your team or customers already use.

    8. How RAC Adds Value at Every Stage of the Service Lifecycle

    RAG helps users get an answer. RAC helps teams get results.

    That core difference unlocks measurable value across every phase of the service experience—from self-service to workforce development.

    Here’s how RAC transforms each stage:

    Side-by-Side Comparison: RAG vs. RAC Across the Service Lifecycle

    Service Stage With RAG With RAC
    End-User Self-Service Returns a single-shot answer. Can’t clarify missing info, so users often escalate prematurely. Asks follow-up questions, narrows down the issue, confirms resolution—reducing escalations.
    Contact Center Enablement Delivers dense paragraphs agents must interpret, often leading to longer resolution times. Shares the probing loop with the agent, suggesting the next best action in real time.
    First-Time Fix for Field Techs Offers a general instruction, but can’t confirm if the fix worked or recommend next steps. Delivers one step at a time, confirms completion, and dynamically adjusts based on feedback.
    Learning From Every Interaction Logs only inputs and outputs—no insight into what resolved the issue. Each turn contains an embedded feedback loop, teaching RAC (and your team) what works best.
    Smarter Workforce Planning Tracks AI usage, not resolution success—making it hard to identify training gaps or performance issues. Shows where users succeed or stall, enabling targeted training and optimized staffing.

    Why It Matters: RAC doesn’t just power faster fixes—it powers smarter operations.
    Every micro-interaction becomes a data point that sharpens your service, improves technician performance, and boosts long-term ROI.

    Closing Thoughts: Turning Answers Into Outcomes

    RAG was step one. It made enterprise knowledge searchable and answers more reliable. But answers alone don’t drive outcomes—action does.

    RAC is step two. It replicates the probing, clarifying, and iterative reasoning of your most experienced technicians—at scale, across channels, and 24/7.

    If you take just three steps after reading this:

    1. Audit an outcome-heavy workflow

    Pick a real use case where success is measured by resolution, not just information.
    How many back-and-forths does it typically take a human to solve it?

    2. Prototype RAC with real data

    Use a narrow but meaningful knowledge set.
    Run baseline metrics—MTTR, FTF, CSAT—before you touch a line of code.

    3. Prove success, then scale wisely

    RAC isn’t a chatbot. It’s a discipline: retrieval, reasoning, questioning, confirmation.
    Start in one domain. Then copy-paste the loop across your service organization.

    Welcome to the conversational era of enterprise AI.
    Aquant’s RAC blueprint is already in production. The next move is yours.

    About the Author

    Indresh Satyanarayana, VP of Product Technology & Labs at Aquant

    Indresh Satyanarayana is a B2B SaaS veteran with over 20 years of experience, including 15 years in field service management. He has held technology leadership roles at SAP and ServiceMax, where he was Chief Architect. Now at Aquant, he leads the Innovation Labs team, bridging market trends, emerging tech, and customer needs.

  5. How Aquant Transforms Every Step of the Service Lifecycle

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    Aquant isn’t just a tool to help technicians troubleshoot equipment. It’s a multi-agent system that redefines how every stakeholder works, learns, and delivers value. Here’s how workflows looked before Aquant, and how they’ve evolved with it.

    1. End-User Self-Service

    Before Aquant:

    • Customers relied on static help articles or generic chatbot replies.
    • If the first suggestion didn’t resolve the issue, there was no way to clarify or go deeper.
    • Most users escalated to the contact center after a frustrating self-service experience.

    With Aquant:

    • The AI probes for missing context (e.g., model number, symptom details) instead of guessing.
    • It walks users through interactive troubleshooting, asking follow-up questions, and adjusting based on inputs.
    • The system confirms resolution before ending the session, ensuring more issues are solved upfront.

    Impact:

    • Fewer escalations to support
    • Shorter time-to-resolution
    • Higher customer satisfaction

     

    2. Contact Center Enablement

    Before Aquant:

    • Support reps searched internal knowledge bases or relied on tribal knowledge.
    • Responses were long and difficult to interpret, slowing down resolution.
    • Most complex issues were escalated to the field, even when avoidable.

    With Aquant:

    • The AI guides agents through a shared probing loop, asking smart diagnostic questions to gather key details.
    • Suggestions evolve in real-time, always surfacing the next-best step to take.
    • Agents resolve more issues without escalation, confidently and quickly.

    Impact:

    • Fewer truck rolls
    • Faster turnaround times
    • Lower operational costs

     

    3. First-Time Fix for Field Technicians

    Before Aquant:

    • Technicians often arrived on-site with incomplete information or incorrect parts.
    • They followed rigid, linear workflows that didn’t adapt if the first step failed.
    • Repeat visits were common and time-consuming.

    With Aquant:

    • The platform delivers one repair step at a time, waits for technician feedback, then adjusts.
    • If something doesn’t work, Aquant pivots and offers the next logical action.
    • Even new technicians work like seasoned experts.

    Impact:

    • Higher first-time fix rates
    • Reduced downtime
    • Increased technician confidence

     

    4. Learn from Every Interaction

    Before Aquant:

    • There was no system in place to capture which steps worked and which didn’t.
    • Knowledge improvements relied on manual review and anecdotal feedback.
    • Lessons learned from one case rarely benefited the next.

    With Aquant:

    • Each interaction forms a micro-conversation with a built-in feedback loop.
    • The system instantly learns what actions led to a successful resolution.
    • Knowledge continuously evolves and is applied to future cases.

    Impact:

    • Smarter, faster diagnostics over time
    • Better recommendations with every case
    • A truly learning service organization

     

    5. Smarter Workforce Planning

    Before Aquant:

    • Usage logs showed what content was served—not what worked.
    • It was hard to pinpoint technician skill gaps or where onboarding failed.
    • Training programs were broad and generic.

    With Aquant:

    • Conversations reveal where technicians get stuck and which probes unlock success.
    • Leaders gain insights into specific skill gaps and where training is needed.
    • Data drives targeted onboarding and staffing strategies.

    Impact:

    • Faster ramp times
    • Greater team capacity
    • Less reliance on top performers

     

    6. Predict Maintenance Before It’s Needed 

    Before Aquant:

    • Maintenance was reactive or based on static schedules.
    • Emergencies disrupted operations and increased costs.

    With Aquant:

    • Aquant uses patterns across historical and real-time interactions to anticipate failures before they occur.
    • The system guides proactive maintenance planning to avoid breakdowns and service interruptions.

    Impact:

    • Fewer emergency calls
    • Reduced unplanned downtime
    • Lower total cost of ownership

     

    From Reactive to Intelligent Service

    Aquant transforms service from a series of reactive steps into a proactive, intelligent ecosystem—one where every stakeholder is empowered to deliver better outcomes with less effort. Whether it’s helping end-users solve issues on their own, enabling agents to resolve more calls without escalation, guiding technicians step-by-step in the field, or providing managers with real-time insight into team performance. Aquant brings clarity, precision, and learning to every moment in the service lifecycle.

    The result? Fewer escalations. Faster resolutions. Smarter teams. And a service experience that gets better every time.

    Service leaders don’t need more tools. They need a system that learns, adapts, and performs, just like their best people do.

  6. A Voice-First Breakthrough: AI Enters the Call

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    We’ve spent the last few years marveling at how large language models like ChatGPT have transformed our interactions with technology. With just a few keystrokes, we can now generate documents, translate complex concepts, and solve intricate problems in seconds. But there’s always been one big limitation: what happens when your hands are busy, your gloves are on, and you can’t type a single word?

    That moment (the moment where AI becomes truly accessible) has arrived.

    We’re now seeing the emergence of the first voice-native AI experiences. These aren’t just voice-to-text tools or call center bots. They’re systems you can actually talk to, naturally and conversationally, just like you would with a human expert. And not just any expert: one with deep institutional knowledge, real-time reasoning, and round-the-clock availability.

    All through the most universal, low-friction interface of all: a simple phone call.

    Why This Is a Breakthrough

    This isn’t just a technological milestone, it’s a shift in how we think about AI accessibility.

    Until now, AI has mostly lived on screens. It’s been built for desk workers, digital workflows, and app-heavy ecosystems. But in the real world, work doesn’t always happen behind a screen. It happens on the factory floor, in the field, behind the retail counter, and on the move.

    By making AI available via voice, and accessible through nothing more than a phone number, we’re removing the barriers that have kept AI out of the hands of frontline workers. No downloads. No logins. No training.

    Just your voice.

    This expands the reach of AI to millions of people who have historically been underserved by tech, including:

    • Field technicians
    • Warehouse operators
    • Store associates
    • Third-party contractors
    • On-the-go customers

    This is what it really means to “meet users where they are.”

    Changing the Game Across Industries

    This new class of voice-native AI is already transforming how support is delivered in industries where speed, accuracy, and accessibility are critical. Here’s how it’s changing the game:

    • Field Service: In industries like medical devices, industrial equipment, or utilities, experienced technicians are stretched thin and new hires can take months to ramp. With voice-first AI, any technician can call in from a job site and get expert-level support instantly, hands-free, without navigating systems or waiting on hold.
    • Retail: Store associates can now access critical information in real-time, whether it’s a pricing policy, inventory lookup, or compliance question, just by speaking. This lightens the load on centralized support teams and helps frontline employees deliver better customer experiences without disruption.
    • Manufacturing: On the production line, workers often can’t stop to use a screen. With voice-first AI, they can ask questions about machine diagnostics, process protocols, or safety guidelines mid-task, all while keeping their hands free.
    • Logistics & Transportation: Drivers and warehouse staff can get routing instructions, delivery updates, or issue resolution guidance through a simple phone call, no app toggling or manual lookup required, keeping operations efficient and safe.

    This isn’t just scaling support, it’s democratizing expertise. Everyone, from the newest hire to a third-party partner, can now access institutional knowledge on demand.

    A New Kind of Learning Loop

    And it doesn’t stop there.

    It evolves like a human apprentice, only at machine scale.

    The result is an ever-expanding brain trust that becomes more powerful and more accurate with every call.

    The Future Is Conversational

    This is the next frontier of AI.

    It’s not just about faster answers or better interfaces, it’s about creating equitable access to intelligence. It’s about empowering workers who don’t sit at desks, who don’t have time for dashboards, and who don’t want another app.

    This is AI that doesn’t just work with people, it works like people.

    And it all starts with a simple phone call.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  7. The Next Frontier of AI: AI That Works Without a Signal

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    We’re used to thinking of AI as something that lives in the cloud. It’s always-on, always-connected, and always-reliant on internet access. But what happens when that connection drops?

    For most AI systems today, the answer is simple: they stop working.

    That’s a problem. Because the moments when AI guidance matters most – the highest-stakes situations – often happen in the least connected places: a military outpost, a remote facility, the factory floor, in the air, under the ocean, or out on the road. The recent power outage that swept across parts of Europe was a harsh reminder of just how fragile connectivity can be. When the grid went down, so did access to critical systems – transportation, communication, even medical equipment. In moments like these, you need intelligence that shows up anyway. And once you’ve had it, you get used to it fast. So fast that when it’s gone, it feels like losing a sense you didn’t realize you relied on.

    This is where offline AI comes in.

    Offline AI Is Not Just a Feature. It’s a Paradigm Shift.

    Building AI that works without a signal may sound like a convenience, but it’s actually a fundamental change in how we architect intelligent systems. Until now, large language models (LLMs) have depended on constant access to the cloud. They’re massive, resource-hungry, and typically tethered to datacenters. But new breakthroughs in edge computing, model compression, and contextual prioritization are changing that.

    Today, it’s possible to embed lightweight, task-specific AI directly onto a device, giving users access to natural language tools, intelligent search, and decision support even when they’re offline. It’s like having ChatGPT in your pocket, but one that’s been trained specifically for your domain, your workflows, and your knowledge base.

    This isn’t theoretical. It’s happening right now. And it changes everything.

    Why Offline Matters

    Offline AI brings a host of benefits that extend far beyond just convenience:

    • Reliability in critical moments: From disaster response teams cut off from the grid to technicians, soldiers, or frontline workers in connectivity dead zones – intelligence needs to be there when everything else isn’t.
    • Speed and responsiveness: Without the latency of cloud roundtrips, on-device AI delivers lightning-fast results tailored to the task at hand.
    • Data privacy and control: Sensitive operations can now be performed locally, without needing to send data into the cloud.
    • Resilience at scale: When AI is embedded at the edge, systems become less dependent on centralized infrastructure, leading to more robust and scalable deployments.

    Think of it like downloading a playlist on Spotify or a movie on Netflix before you get on a plane. You don’t have to think about whether it’ll work, it just does. That’s the level of simplicity and trust we should demand from our AI systems.

    A Future Where Intelligence Follows You

    The next generation of AI won’t be defined by how big the models are, but by how close they are to where real decisions get made.

    AI that is embedded, autonomous, and adaptive will reshape how we operate, especially in environments where connectivity cannot be counted on. From emergency response to defense operations to mission-critical infrastructure, the ability to access knowledge and guidance anytime and anywhere will define the difference between AI that is ready for anything and AI that falls short when it matters most.

    Offline AI isn’t just the future, it’s already here. And it’s about time.

    About the Author

    Assaf Melochna, President and CoFounder, Aquant

    Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant. 

  8. How Waters and Aquant Are Transforming Service, One Technician at a Time

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    AI isn’t about replacing people, it’s about meeting them where they are.

    That was the core message behind a lively session featuring Edwin Pahk, VP of Customer Success at Aquant, and Sara Smith, Service AI Program Owner at Waters Corporation. In a conversation filled with real-world examples, humor, and heartfelt takeaways, the two explored how AI can truly support, not supersede, technicians in the field.

    “AI wasn’t on my five-year plan.”

    Sara’s background is in forensic toxicology, not AI. But after more than a decade in the field service trenches, she understood firsthand the challenges technicians face, from solving complex issues on-site to tracking down the right part numbers with limited time and support. That’s what made her the perfect person to lead Waters’ AI initiatives for its global service organization.

    Learning to Let Go of Perfection

    Before launching their AI tools, Sara’s team grappled with the same concerns many service leaders face: Is our data good enough? Is our knowledge base strong enough? Are our people ready? Her advice?

    “Perfection is the enemy of progress. Good enough is good enough to go.”

    And that mindset paid off. Within six months of implementation, Waters met its adoption and performance goals, fueled by a combination of early champions and creative training strategies.

    Training Through Play: The Star Wars Scavenger Hunt

    One of the most memorable strategies? A Star Wars-themed scavenger hunt, designed to help technicians build confidence in prompting and interacting with AI.

    Rather than mandating training through traditional e-learning, the Waters team made it fun and inclusive. Each day, employees received a new clue and had to find the answer using Aquant’s AI-powered platform. The result: increased engagement, deeper insights, and even a few surprises, like the fact that diehard Star Wars fans sometimes performed worse than casual participants, revealing a tendency to fixate on what they thought the answer should be.

    Meeting Techs Where They Are, Literally

    From basements with no connectivity to hands-free environments, Edwin showcased how Aquant is designing tools for the real-world conditions technicians face every day – including Call Assist, which offers instant, voice-accessible support for those without platform access or the ability to tap or type, and AI in Offline Mode, which ensures full functionality even when techs are underground, offshore, or at the most remote job sites.

    “This isn’t about building cool tech, it’s about building tools that feel natural, intuitive, and useful in the field,” Edwin explained. “AI is no longer the future. AI is now.”

    The Big Takeaway:
    Start now. Start small. Make it fun. AI transformation doesn’t have to mean sweeping change overnight, but it does require a shift in mindset. When done right, it can make every technician feel supported, informed, and empowered to deliver better service.

    Curious how Waters did it? Connect with the Aquant team to see how AI is reshaping field service, one technician at a time. Request a demo here. 

  9. Aquant Releases New Features to Address the Unique Demands of Field Work

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    AI in Offline Mode and Call Assist features turn Aquant into the always-on, always-ready partner for service teams in the field

    Aquant today announced two new features: AI in Offline Mode and Call Assist, helping service teams meet the real-world demands of field work with technology that’s available anytime, anywhere. Unveiled at Field Service Palm Springs 2025, these updates turn Aquant into the always-on, always-ready partner for technicians working in the field.

    AI in Offline Mode gives technicians access to all of Aquant’s Agentic agents, even in areas with little to no connectivity, such as offshore, underground or remote job sites. This ensures they can resolve issues quickly and independently, without disruption, no matter where they are.

    Call Assist is the voice-accessible extension of Aquant’s Agentic AI support platform, designed to scale expert-level support across the service organization. Whether it’s a technician trying to reach a busy expert helpdesk, a technician or dealer without direct platform access, or a field tech working hands-free, Call Assist delivers Aquant’s expert-level guidance through a simple phone call, no login, typing, or tapping required. It’s like having your best expert on the line, anytime, anywhere.

    “In the field, when connectivity drops, field service technicians are often on their own. If they need help, they either have to step away from the job site or wait until they’re back online,” said Assaf Melochna, president & co-founder of Aquant. “It’s a breakthrough in technology – Aquant is the only company to offer AI that works like ChatGPT, even when there’s no signal. Whether online or off, whether typing or talking, Aquant is now available exactly when and how a technician needs it.”

    These features were developed in direct response to customer demand. Numerous organizations use Aquant to increase equipment uptime, reduce service costs, and deliver consistent support across all teams. As service calls increasingly occur in remote or constrained environments, customers ranked offline mode and call assist among the most critical enhancements. 

    By removing connectivity as a dependency, Aquant increases technician independence, improves first-time fix rates, and helps service teams scale consistent, high-quality support, no matter the setting. Call Assist further streamlines workflows by enabling expert-level guidance through a simple phone call, no login, typing, or tapping required.