Put any feedback-analysis method to the test—starting with ours.
Point a general-purpose AI tool at raw customer feedback and you get answers that sound confident, change every time you ask, and trace back to nothing. Wordnerds takes a different route. This page walks through the architecture, the method, the team and the proof—so you can judge it for yourself.
Where Wordnerds fits in your data stack
Wordnerds is a five-step data pipeline—unstructured to structured to semantic, then human and agent. Raw feedback becomes a structured classification and a semantic model your whole organisation can query: analysts get a full-detail Power BI environment, AI agents get plain-language answers, all served from one verified foundation.
Most feedback tools stop at a dashboard inside their own platform. Wordnerds is built the other way round: the intelligence is pushed out permanently into your own Microsoft Power BI, where your teams already work.
The pipeline runs once. Unstructured feedback—surveys, complaints, reviews, calls—is classified against an analyst-authored taxonomy, then modelled semantically so it can be queried. Build once, serve both: from that single foundation, analysts work in full-detail Power BI and AI agents answer in plain language, so the same verified intelligence reaches every team without anyone rebuilding it for a new channel.
That's the architecture. The three phases below—Connect, Build, Deliver—are how you build it with us.
How we think feedback analysis should work
We believe customer feedback is a strategic asset—and that it's wasted when it stays locked inside the insight team. So we built a method everyone can act on, and built it so the answers hold up when a board or a regulator pushes back.
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Insight belongs to everyone, not just the insight team
Feedback only changes anything when the people making operational decisions can see it. Wordnerds is built to put customer insight in front of every team that needs it, in the tools they already use—not to guard it inside a specialist platform only analysts log into.
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Human judgement and AI, not AI on its own
We're not anti-AI—we just apply it at the right layer. Wordnerds' models do the heavy classification automatically; our analysts author and ratify the framework they run against. That's accountability by design: a person stands behind every category, so you can always show your working.
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Your taxonomy, owned by you
The categories should reflect the language your organisation and your regulators actually use—not whatever a model happens to surface on a given run. With Wordnerds, your analysts encode that vocabulary before the model runs, so the structure stays yours and stays stable enough to compare like for like over time.
Three phases, from raw feedback to live intelligence
Five layers is the architecture; three phases is how you build it with us. The whole point is to apply AI at the right layer—structured, classified data—instead of pointing it at raw feedback and hoping for the best.
Connect every feedback source
Wordnerds ingests every channel your customers actually use—surveys, complaints, contact-centre calls, reviews, social and in-product feedback—into one foundation. No sampling, no leaving the awkward channels out because they're hard to read. The alternative most teams live with is a spreadsheet per source and a manual copy-paste job that never quite finishes; a general-purpose AI tool pointed at a single export has the same blind spots. You finish this phase with every customer voice in one place, ready to be classified consistently.
Build structured insight
This is where Wordnerds' automated classification does the heavy lifting: sector-tuned models read every comment and assign it against a structured taxonomy, at a scale and consistency no manual process can match. Your analysts then author and ratify that taxonomy, so the model runs against definitions you own and recognise. Point a general-purpose AI model at the same raw text and you get themes that shift on every run, with nothing to audit. You finish with structured, scored, comparable data—and a classification you can stand behind.
Deliver actionable intelligence
The structured intelligence pushes straight into Microsoft Power BI, where your teams already make decisions—and because Wordnerds builds once and serves both, the same foundation answers AI agents and chat tools in plain language. The output is built to be acted on, not admired: the priorities that matter, ranked by impact, each carrying the evidence behind it and a clear read on what to fix first. You finish with insight living where the work already happens, not in a report archived in a platform nobody opens.
A team of experts behind you, not just software
Wordnerds isn't only software. Our Nerd-assisted consultancy co-designs your classification framework, trains the models on your sector, and shapes the Power BI delivery around how your teams actually work. You can have it done with you or done for you—either way, the result is insight that fits your organisation from day one.
Behind every Wordnerds deployment is a team of analysts who do this for a living across housing, transport and other regulated sectors—people who have built feedback frameworks dozens of times before. They sit with your team to agree the themes that matter, bring sector frameworks that are already built, and tune the models to the language your customers and regulators use.
That co-design is the difference between a tool you have to learn and insight that's accurate and defensible from the first report. As your needs change—new channels, new regulatory questions, a board that wants something different—the same team keeps the framework current. It's a partnership, not a licence you're left to work out on your own.
Why our approach is better
Most methods compete on how their dashboards look. The questions that actually matter surface later—when someone challenges a number, or asks the AI something the underlying data can't safely answer.
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Can you defend the AI's decisions?
Ian Fox at Trent & Dove put the worry plainly: "My concern is when you're completely leaving it up to an AI model—because then who is liable?" Wordnerds answers it: automated classification runs first, analysts ratify the taxonomy it uses, and every theme traces back to source verbatim. And because that intelligence is pushed permanently into your BI stack rather than queried in on demand, any agent reasoning over it works from an auditable layer, not a black box.
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Does the insight live where decisions happen?
Wordnerds pushes themes, drivers and priorities straight into Microsoft Power BI—the surface your operational teams already use—and keeps them there permanently. Insight that sits in a separate platform waits for someone to go and find it; insight on the dashboard people already open gets acted on. No replatforming, no extra login, no export-and-paste.
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Is it built for your sector's rules?
Wordnerds comes with frameworks already built for UK regulated sectors—Awaab's Law and Housing Ombudsman categories, ORR and DFT reporting, FCA Consumer Duty. But this isn't only about passing an audit: the same structured evidence that satisfies a regulator is what cuts labour costs and frees up analyst time at the operators we work with. Compliance and commercial outcome come from one build, not two.
Avanti West Coast: from 1.3 million passenger voices to operational change
Avanti West Coast runs passenger feedback from surveys, social and ad-hoc channels through Wordnerds—1.3 million voices structured, scored and linked to the operational changes they drove. The result: 7,102 working days saved and £1.35 million in labour costs released back to the business, every figure traceable to the feedback behind it.
Before Wordnerds, Avanti's insight team was reading and re-packaging passenger feedback by hand—across multiple channels, and into a different cut for every internal team that needed one. Customers had taken the time to say what was working and what wasn't; too much of it went unheard, and the operational teams who could act rarely got the right signal in time.
Wordnerds connected Avanti's feedback sources and built a classification framework around the questions that actually run a railway: punctuality, onboard experience, accessibility, staff recognition. Every operational manager could see their own area's performance in Power BI without waiting for a quarterly report.
With structured, scored feedback flowing continuously, the team moved from writing up the past to guiding what happened next. The 7,102 working days saved and £1.35 million returned came from operational decisions the structured evidence made obvious.
This is the working state Avanti reached: an insight team that stopped defending its data and started being asked, every week, what to do next.
Questions buyers ask about the method
What is Wordnerds?
Wordnerds is a structured classification layer for unstructured customer feedback. We take surveys, complaints, reviews and calls, run every response through a taxonomy our analysts author with you, and deliver scored, categorised data into Power BI—so the numbers are comparable over time and every theme traces back to what a customer actually said.
How long does it take to go live, and who helps us do it?
Most teams are live in two to three weeks; the fastest go-live we've run took a single day. You're not on your own doing it—Wordnerds analysts co-design your classification framework with you, as part of the Nerd-assisted service. That co-design is what makes the output reliable from the start rather than something you build alone. Our consultancy page walks through exactly how the engagement works.
How is this different from using an AI model to analyse our feedback?
Point a general-purpose AI model at raw feedback and it will summarise—but the themes change every run, the counts aren't reliable, and you can't trace a number back to a defined category. Wordnerds builds the classification logic with your analysts, validates it, and pushes the structured output into your BI stack permanently. The difference is the layer: an AI model is only as trustworthy as the structured, auditable data it reasons against, and that layer is what we build.
Can we use the output with AI agents or chat-based tools?
Yes—and it's where the five-step pipeline matters most. An agent needs structured, semantically-enriched data to reason against; point it at raw survey text and you get patterns that shift with no explanation. Because Wordnerds output is already classified and modelled, an agent reasons against verified data instead of guessing. The same intelligence layer answers your Power BI analysts and your chat and agent tools.
Do analysts and AI agents use different data, or the same output?
The same output—that's the whole point. We build once, serve both: one structured intelligence layer feeds the full analyst-grade Power BI environment and the plain-language agent and chat layer at the same time. One classification framework, authored once and validated once, drives every channel—so an analyst and an AI agent are always working from the same verified numbers.
