Product & UX
We walk the core journeys the way a real user does — onboarding, the activation moment, the daily loop — and surface where attention leaks, friction stacks up, and the value gets buried.
Stage · Product Audits & Assessments
Find what's working —
Find what's working —
and what's quietly costing you.
For teams with a live product that feels stuck. A senior, clear-eyed assessment across UX, code, data & AI-readiness, and growth — and a prioritized roadmap you can actually act on.
Senior reviewers · no juniors, no junk findings.
—— What we assess
Four lenses on the same product. No blind spots.
A UX problem is often a data problem. A stalled metric is often an architecture one. We assess across all four layers simultaneously — so the findings tell a complete story, not four disconnected ones.
Code & Architecture
Senior engineers read the codebase the way they'd read one they're about to inherit — structure, test coverage, dependencies, and the load-bearing decisions that are making change slow or risky.
Data & AI-Readiness
Is the data complete, accessible, and governed enough to build on? We assess where AI could earn its place — and where it can't yet — and point to the AI Audit when a deeper, AI-specific pass is warranted.
Growth & Retention
We follow the funnel from first touch to repeat use — activation, retention, and the points where growth stalls — to separate a traffic problem from a product problem from a positioning one.
—— Where findings lead
The audit is the start, not the finish.
When we find something worth fixing, we can fix it — with the same senior team that did the review. No handoff, no translation layer.
AI Audit
When the AI-readiness lens turns up real opportunity, the AI Audit goes the rest of the way — an opportunity map, integration-risk assessment, ROI model, and a phased roadmap aligned to the NIST AI Risk Framework.
Explore the AI Audit →Hardening
When the findings are about a fragile codebase or mounting tech debt, hardening is the fix — a focused engagement to shore up the architecture, close the gaps, and make the product safe to build on again.
Fix what the audit finds →UX / UI Design
When the audit traces the leak to the experience itself, our design team redraws the flows that bury the value — research-led, shipped by senior designers, with a handoff your engineers can build from.
See the design work →Growth Services
When the plateau is in the funnel — not the product — our growth team picks up the retention and conversion findings and turns them into experiments that move the curve again.
See growth services →—— When an audit pays for itself
When an audit pays for itself.
These are the moments when a senior, cross-layer assessment delivers the most value — because the cost of guessing is higher than the cost of knowing.
The line stopped climbing
The acquisition holds but the line stopped climbing. An audit finds whether the ceiling is in the funnel, the product, the architecture, or all three — and tells you which lever to pull first.
Every feature takes longer
Every feature takes longer than it should. An architecture review finds the load-bearing decisions that are making change slow and risky — and gives you a sequenced plan to fix them without stopping the team.
Confirm readiness before committing budget
You know there's leverage in AI — you just need someone to confirm the data, the architecture, and the use case are actually ready. An audit answers that before you commit budget to finding out the hard way.
—— What you walk away with
From findings to forward motion.
Every finding is written to be acted on — ranked by business impact and effort, tied to a clear next step. No 80-page PDF that lands in a drawer.
A prioritized findings report
Everything we found across the four lenses, ranked by business impact and effort — with the evidence behind each call, so your team can challenge it and own it.
A sequenced roadmap
A phased plan that puts the findings in order — what to fix first, what can wait, and the decision gates between phases. Executable by your team, with us, or with anyone.
A short list of quick wins
The handful of changes worth shipping this week — low effort, real impact — so the audit pays back before the roadmap even starts, and the team feels the difference fast.
—— How it works
Four steps. A plan you can defend.
A focused engagement with clear inputs and outputs at each step. Light on your team's time, heavy on what you walk away with.
1
Step 01
Scope
Align on the questions that matter and the metrics that aren't moving. We gather access — analytics, the codebase, and a few stakeholder conversations — and set the bar for what a useful answer looks like.
2
Step 02
Review
Senior reviewers go deep across the four lenses — product & UX, code & architecture, data & AI-readiness, and growth — using the product the way real users and engineers do, not a checklist from the outside.
3
Step 03
Synthesize
We connect the findings into a story — what's working, what's quietly costing you, and how the layers compound — then rank everything by impact and effort so the priorities are obvious, not arbitrary.
4
Step 04
Roadmap
We hand off the prioritized report, a sequenced roadmap, and the quick wins — then walk your team through it live, so the plan is understood and owned, not just delivered.
—— Selected work
The team auditing your product ships, too.
Our findings come with real, build-grounded judgment because we build as well as assess. A few of the products we've shaped — from connected fitness and healthcare to mobility and IoT.
—— Common questions
What teams ask first.
How this differs from the AI Audit, what access you need to give us, whether we fix what we find, how long it takes, what it costs, and what happens if the news isn't bad.
How is this different from the AI Audit?
A product audit is the wide-angle view — UX, code, data & AI-readiness, and growth, all at once — to find what's holding a live product back. The AI Audit is a deep, AI-specific pass: opportunity mapping, integration-risk, ROI modeling, and a NIST-aligned roadmap. If the product audit flags real AI opportunity, the AI Audit is the natural next step.
What access do you need from our team?
Read access to the codebase, your product analytics, and a few stakeholder conversations. The more we can see, the sharper the findings — but we scope to what you're comfortable sharing, and we don't need production access or sensitive customer data to do the work.
Do you fix what you find, or just report it?
Who actually does the review?
Senior people only — the engineers and designers who've been shipping and inheriting products since 2008. No juniors running a checklist. You get judgment grounded in having built and scaled real products, not a template scored from the outside.
What if the audit says the product is fine?
Then you get that, plainly — and the confidence to keep investing where you are instead of chasing a phantom problem. An honest "you're on the right track" is a real outcome. We're not here to manufacture findings to justify a follow-on engagement.
How long does a product audit take, and what does it cost?
A typical audit runs two to four weeks depending on scope — the number of lenses, the size of the codebase, and how many stakeholder conversations are involved. Pricing is fixed-scope, agreed before we start, with no surprise overages. Talk to us and we'll scope it to your product and budget.
—— Field notes
What we’re writing about.
Field notes from the studio — what we’re learning about AI products, agent UX, and the messy reality of shipping software in 2026.
