How to turn customer feedback into build-ready specs

I built Circuit to be a secondary research tool.

Teams would collect feedback however they already collected it — support tickets, Slack messages, interview notes — and Circuit would process it. Ingest, classify, prioritise, spec. My job was the middle of the pipeline, not the beginning.

Then I started doing discovery. And the feedback told me I was wrong.

I shared the concept with early users — founders, product people, solo builders I respected. I took careful notes on every conversation and fed those notes into Circuit. One theme kept surfacing: what if Circuit could also collect the feedback? People wanted a widget — something they could embed on their own product. The further I went into discovery, the louder that signal got.

So I built the widget. Not because I'd planned it. Because my own process — run through my own tool — told me to.

Here's what still gets me: I also hadn't planned to build transcript ingestion. It was the act of doing discovery, sitting in those conversations and realising how much signal was locked inside them, that made it obvious. The feature I needed for my own research became a feature inside the product.

The researcher became the research subject. The loop closed.

Two of Circuit's most important features exist because I listened to what the feedback was telling me — not what I expected to hear. That's what this article is about. The translation step between what customers say and what gets built. How to turn customer feedback into build specs an AI coding tool can act on today. The feedback-to-spec translation, in practice.

The product feedback translation problem

The problem isn't feedback. It's translation.

Most teams have more product feedback than they can process. Support tickets. Slack messages from power users. NPS surveys. Sales call notes. Feature request threads. Discovery interviews. The volume available to a small team in 2026 would have been unimaginable a decade ago.

And yet most teams still struggle to decide what to build next.

How to connect customer feedback to engineering without the manual middle step is the entire question. The problem isn't data. It's the step between data and decision — the translation layer where raw customer signal becomes something an engineer can act on. Customer feedback management is the slow, expensive, signal-degrading middle. Manual feedback management runs alongside the fast tools, not integrated with them. Product feedback to engineering runs through one human in most teams. That human is the bottleneck.

That translation is manual, slow, and lots of work. A product manager reads through feedback, forms a mental model, makes judgment calls about priority, writes a ticket that tries to capture intent, and hands it to engineering.

Every step in that chain degrades the signal.

The PM interprets feedback through their own lens. Prioritisation reflects whoever argued loudest in the last meeting. The ticket captures the solution the PM imagined, not the problem the customer described. By the time an engineer starts building, they're no longer solving the customer's problem. They're solving a compressed, interpreted version of it — three translations removed from the original.

I spent twenty years watching this happen across global product teams. The tools changed. The process didn't. Teams invested in feedback collection and then drowned in what they collected. Prioritisation drifted toward gut feel. Specs were written from memory, not signal. The loop never closed.

The bottleneck was never engineering. It was always the translation.

How AI coding tools changed product development priorities

AI changed two things simultaneously, and the combination creates a new kind of pressure.

First, AI coding tools — Cursor, Claude Code — compressed the time between decision and shipped code. What took a sprint now takes hours. The build cycle got faster by an order of magnitude.

Second, AI made it possible to process qualitative feedback at scale. To cluster themes across hundreds of inputs. To surface patterns a human reader would miss. To score and rank based on multiple signals at once.

The result: the bottleneck moved.

For the first time, product development has a new shape:

Customers → AI system → Engineers

Not:

Customers → Product manager → Engineers

That shift sounds subtle. It isn't. It removes the manual translation layer that product teams have relied on for decades — the one where signal degrades, decisions slow down, and the customer's actual words get lost somewhere between feedback and spec.

Engineering got faster. The constraint shifted upstream, to the product decision layer. If you can't decide quickly and clearly what to build, the speed of your coding tools doesn't matter. You're still slow — just at a different point in the process.

This is why the translation layer matters more now than it ever has. You can't outrun it with better engineering. You have to solve it.

How I turn customer feedback into build-ready specs

Let me walk you through exactly how I do this building Circuit. Not the idealised version. The actual workflow.

Step 1: Collect feedback with intent

I start with discovery conversations. When I'm exploring a new direction or trying to understand a problem area, I talk to people — founders, product managers, solo builders who are my target users.

These aren't surveys. They're conversations. I ask open questions. I share designs and watch reactions. I listen for the language people use to describe their problems, not just the features they request.

I take notes during or immediately after each conversation. Not transcriptions — synthesised notes that capture the key moments: what surprised me, what they said more than once, what they reacted to strongly.

The format doesn't need to be perfect. Circuit doesn't need structured input — it needs honest signal.

What I actually do: I paste my notes into Circuit as manual entries or upload them as text files. If I've recorded a conversation (with permission), I'll get a transcript and feed that in directly. The transcript ingestion feature exists because I found myself doing this manually in discovery and realised it should be automated.

Step 2: Let Circuit surface the themes

Once the feedback is in, Circuit runs it through a classification and clustering pipeline. This is where something interesting happens.

The individual feedback items feel disparate when you're in the conversations. Person A talks about their workflow. Person B has a specific technical question. Person C wants a feature you hadn't considered. It's easy to come away from five conversations with five different impressions.

Circuit collapses that noise into themes. Not by finding identical words, but by finding the same underlying problem expressed differently. It groups feedback into clusters, names them, and scores each one. The scoring runs across seven signals: volume, urgency, revenue impact, positive sentiment, negative sentiment, feature demand and competitive mentions.

What this looked like for the widget: The conversations didn't all say "build a widget." Some people asked how Circuit would connect to their existing product. Some asked about real-time feedback vs. batch import. Some described wanting to be closer to their users. Circuit grouped these into a single theme: feedback collection built into Circuit, not just into the workflow around it. That reframe was more useful than any individual request had been.

Step 3: Identify the real problem, not the requested feature

This is the most important step, and the one most teams skip.

Users request features. They rarely articulate problems. "I want a widget" is a feature request. The problem underneath it — in this case — was that Circuit as I'd designed it required teams to already have a feedback collection process. It assumed the infrastructure existed. For many small teams, it didn't.

That's a different problem than "we need a widget." It's a problem about onboarding, about barrier to entry, about what Circuit assumes about the world.

Circuit's specs are built around problems, not features. When you generate a spec, the first section is context — the customer voice, the pattern of feedback, the problem being solved. The feature is downstream of that. It's the answer to a well-understood question.

In practice: I look at what Circuit has surfaced and ask: what's the real problem this cluster is describing? Sometimes the cluster name makes it obvious. Sometimes I need to go back and read the original feedback items to feel the shape of it.

This is one place where human judgment stays in the loop. Circuit surfaces the signal. You interpret the meaning.

Step 4: Set your goal and let the Priority Engine re-rank

Once themes are identified, Circuit scores them across the seven signals. But score isn't the same as priority — it depends on what you're optimising for.

If you're focused on retention, a small cluster of deeply frustrated existing users might outrank a larger cluster of feature requests from people who aren't yet customers. If you're in growth mode, the calculus flips. The seven signals are all there — how you weigh them reflects what matters to the business right now.

Circuit surfaces the ranked list based on those scores. It doesn't make the decision — it makes the data legible enough that the decision becomes obvious.

What I actually do: I review what Circuit has surfaced and apply my own judgment about what matters most given where the business is. Customer feedback prioritisation isn't the score — it's what you decide given the score and the goal. How to prioritise customer feedback when your goal is retention is different from when it's growth. The scores tell me where customer pain is concentrated. I decide what to act on first.

This is the step that replaces most prioritisation meetings. Not because meetings are bad, but because the conversation you'd have in a meeting is now happening in the data, explicitly, before anyone's in the room.

Step 5: Generate the spec

This is where Circuit does the work that used to take hours. Feedback-to-brief generation used to take most of a day.

A Circuit spec — Circuit's AI engineering brief — has five sections. A build spec an AI coding tool can act on today is the output, not a ticket:

1. What to build. A clear description of the feature or change, grounded in the problem — not just a solution description.
2. Why. The customer evidence behind it. The themes. The volume. The specific signals that make this a real priority rather than a gut call.
3. Customer voice. Direct quotes from the feedback. The actual words customers used. This section exists because specs often lose the human signal by the time they reach engineering. The customer voice keeps it grounded.
4. Files to touch. If you've connected GitHub, Circuit reads your codebase and identifies the specific files, components and patterns relevant to this spec. Not generic guidance — actual file paths in your actual codebase. This is what makes specs immediately actionable in Cursor or Claude Code.
5. Done criteria. How you'll know it's working. Concrete, testable, tied to the problem being solved.

What this looked like for the widget: The spec that came out of the widget cluster didn't say "build a widget." It said: Circuit assumes teams have existing feedback infrastructure. Many early-stage founders don't. The barrier to entry is the absence of a feedback collection mechanism built into Circuit itself. The solution is an embeddable widget that captures feedback directly into Circuit, removing the infrastructure assumption. Then it specified the files to touch, the exact implementation context from the codebase, and the criteria for knowing it worked.

That spec took Circuit minutes to generate. That's spec-driven development: customer signal becomes a build-ready document before you've opened your editor. Spec-driven development works when the spec is grounded in customer signal, not interpretation. Codebase-aware build specs mean the engineer starts with a map, not a blank page. The version I would have written manually — after synthesising the discovery notes, identifying the theme, working out the problem framing, pulling codebase context — would have taken most of a day.

Step 6: Deliver it where code gets written

Specs are only useful if they reach engineering at the right moment.

Circuit delivers specs via MCP — Model Context Protocol directly into Cursor and Claude Code. You can pull priorities and fetch specs without leaving your editor. The file paths are there. The context is there. You start coding from a spec, not from a ticket that points to a Jira board that references a conversation that happened three weeks ago.

The last step in the circuit closes it: when a feature ships, Circuit emails the customers who asked for it, with their original feedback quoted back. New feedback on what shipped flows straight back in. The cycle restarts.

What to automate in your product workflow — and what stays human

This is worth being precise about, because the answer matters for how you think about the workflow.

What stays human:

• Having the conversations
• Interpreting what a cluster actually means
• Setting the goal you're optimising for
• Making the final call on what to build

What gets automated:

• Grouping and scoring incoming signal into themes
• Re-ranking priorities based on your stated goal
• Generating specs with codebase context, customer voice and done criteria
• Delivering them to Cursor or Claude Code via MCP
• Notifying customers when their request ships
• Flagging when priorities shift as new feedback arrives

The translation layer — the slow, expensive, signal-degrading middle — is what gets automated. What remains is the judgment that only you can apply: understanding what customers mean, not just what they say, and deciding what matters enough to build.

That's not a small thing. It's the whole job. Circuit doesn't replace it — it clears the way for it.

The evolution of the widget: what happened next

The widget story didn't end with version one.

As I continued speaking to users — particularly those building AI-native products — a new pattern emerged. The way feedback arrives is changing. Traditional products get text: support tickets, feature requests, open-ended responses. AI-native products get signals: thumbs up, thumbs down, retries, edits. Implicit reactions from interaction patterns rather than explicit words.

Version one was a classic feedback form. But as I spoke to more teams building AI-native products, it became clear that feedback was shifting from sentences to signals — and the widget had to evolve with that reality. So it did: from one widget type to five, each designed for a different kind of feedback surface.

That evolution came entirely from discovery. Not a roadmap session. Not a product meeting. From listening — and having a system that surfaced what I was hearing with enough clarity that the pattern was undeniable.

The widget I didn't plan to build taught me what it needed to become.

Why most product teams can't convert feedback into build-ready specs

I've worked in product for over twenty years. I've seen this process fail in the same ways, repeatedly.

Feedback piles up and never gets processed. The gap between collection and decision is where signal dies.

Prioritisation happens without the data. The loudest voice wins. Or whoever has the best slide deck. The feedback exists but it's not in the room when decisions get made.

Specs are written from memory. The original customer language is gone by the time engineering reads the ticket. What's left is one person's interpretation of another person's problem.

The loop never closes. Features ship and customers never know. Each cycle starts cold.

None of this is because teams don't care. It's because the translation layer is genuinely hard work, and it sits between the two things teams measure — customer conversations and shipped features. The middle is invisible. And invisible work doesn't get fixed.

How to start turning customer feedback into specs today

If you're doing this manually today, here's where I'd start.

Pick one feedback source and process it this week. Don't try to ingest everything at once. Take your last ten customer conversations, or your last month of Slack messages, or your support ticket backlog. Run it through any clustering method — even manually — and see what themes emerge. You'll find three to five patterns you already suspected but hadn't articulated.

Write one spec from the output. Not a ticket. A spec — problem, customer evidence, what to build, how you'll know it worked. Feel the difference between starting from a theme and starting from a stated request.

Notice what the loop is missing. Most teams have the feedback. Most teams have engineering. The gap is in the middle. What does your translation layer actually look like right now? How long does it take? Where does signal degrade?

The teams that will build the best products in the next few years aren't the ones with the most feedback or the fastest coding tools. They're the ones that convert customer signal into product work fastest — without losing what the customer actually said along the way.

That loop is what Circuit is built to close. Circuit is the AI product spec generator that runs on customer signal, not blank-page prompts.

Try it in Circuit: Import your first feedback → · Working with specs →

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Catherine Williams-Treloar is the founder of Circuit — the AI product system that turns customer feedback into scored priorities and build-ready specs for Cursor and Claude Code. She has 20+ years leading product, insights, strategy and GTM at scale-ups and enterprises. Circuit was founded in Sydney in November 2025 and launched in February 2026.