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.
How platform onboarding runs
You're buying software, but you're not left to work it out alone—and we don't disappear once the training ends.
Most teams are up and running on Wordnerds within a few weeks. Our customer success team runs three structured training sessions—on a single day, or spread over a couple of weeks—covering how to build your frameworks, train your theme banks and choose your methodologies, with support continuing well after go-live.
Build your frameworks
We set up the classification frameworks your feedback gets sorted against—shaped to your sector and the questions your teams actually need answered, so the structure is yours from the start.
Train your theme banks
We train the theme banks on your language, so the models recognise the themes that matter to you and tag them the same way across every channel—surveys, complaints, reviews and calls.
Choose your methodologies
We help you select the analytical methodologies—segmentation, root-cause, KPI linkage and more—that fit your goals, so the platform delivers the answers you need, not just a wall of charts.
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.
What makes Wordnerds better than other VoC tools?
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.
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: our customer success team runs three structured training sessions—building your frameworks, training your theme banks and choosing your methodologies—and we stay alongside after go-live. If you'd rather we ran it for you, or took on a one-off project, that's the Nerd-assisted consultancy.
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.
How does Wordnerds keep AI costs and energy use in check at scale?
Most AI analysis tools pass every sentence through a large language model—which works at small scale, but the token costs and energy footprint climb steeply as volumes grow. Wordnerds uses a different architecture: the LLM defines what each theme means on a small sample; a lightweight custom model applies those definitions to the full dataset. The result is up to 99% lower energy consumption than a pure LLM approach at enterprise scale (Luccioni et al. 2024). And because the sentence embeddings are generated once and reused, adding a new theme bank later costs almost nothing to run.
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.
Two sides of Wordnerds
Software, a service—or both
You're looking at the platform your team runs day to day. Want us to run it for you instead, or take on a one-off project? That's the consultancy—most customers use both.
