A three-senior boutique for consequential product and data problems: diagnosed, explained and fixed in one to four weeks. You get working systems and a clear decision, not a deck.
The problem
When a metric that matters stalls, the same scene plays out everywhere: product blames the funnel, growth blames the tracking, data blames the events, and next quarter gets bet on whoever argued best in the meeting.
The market answers this badly. Big consultancies sell a strategy deck that needs another team to implement. Agencies rent you a bench by the day. The people who can read the data usually cannot ship the fix, and the people who can ship rarely sit in the meeting where the number gets used.
We sit exactly in that gap: find out what is happening, say why, and build the fix before we leave. Diagnosis and implementation in the same heads.
The wedge
Funnels, cohorts, experiments, and the operational analytics that connect what users do to what it costs and earns. If the metric is lying, we find where and prove it.
dbt, Snowflake, Python, AWS. The tracking plan gets implemented, the tests get written, the pipeline still holds long after the engagement ends.
Head-of-data experience on the team: decisions defended at exec level, and AI adoption grounded in semantic layers instead of demos.
Most shops rent you one of these. Some manage two. The point of this one is all three, at the same table, on the same problem. Hence the name.
The menu
Every offer runs the same way: a fixed fee, a fixed number of weeks, explicit exclusions, and a result that stands alone. The first engagement is never a teaser for a second one. Three offers are marked as a good first engagement: if in doubt, start with one of those.
Your north-star metric stalled and every team tells a different story.
Three to five hypotheses that survived our attempts to kill them, the experiment backlog to test them, and a decision made on evidence instead of volume. We rebuild the funnel from raw events, segment your users by what they do rather than who they are, and hunt counterevidence for every theory in play.
Every metrics meeting starts with an argument about definitions.
A metric dictionary wired to the decisions each number exists to support, a tracking plan with the vanity events cut, and the warehouse models that matter, built and tested. QA on new events keeps the taxonomy clean after we’re gone.
Your channels all claim the same conversion and your CPA is fiction.
Channel cost you can budget against: UTMs and identity joins audited, attribution rules written with their limits stated out loud, and unattributed traffic counted rather than hidden. We have built this before, at Deel.
Two dashboards, one KPI, different numbers. The board noticed.
The numbers that leave the building, traced source to report, reconciled against independent systems, and automated tests standing guard on the failure modes we find. Scoped at qualification to the numbers in your board pack. We built the reconciliation layer behind this at Convex.
Reports fail silently, engineers firefight, the bill keeps climbing, and the roadmap lost the room.
What is broken, what is merely complicated, and what to fix first. Ranked failure modes, a 30/60/90 plan, quick fixes shipped on the way through, and what not to rebuild, in writing.
A dashboard change takes three weeks and nobody can explain why.
A shorter path from request to production, and proof: a real change shipped through it before we go. Queues broken, standard modelling and testing patterns set, and CI/CD replacing manual releases.
Everyone is pasting into chatbots. Nothing is governed.
One bounded workflow: the model interprets, classifies and drafts, deterministic code owns the numbers and the writes, a human approves what matters. Evaluation set and audit log included, so it survives contact with your security team.
Leadership wants agents. Your data can’t hold them yet.
Proceed, narrow, postpone or reject, decided inside two weeks: data quality, permissions, evaluation data, and the semantic layer your agents will read.
You’re about to buy a company on the strength of its metrics.
A decision memo before the wire goes out: architecture, data quality, key-person risk, and the headline metrics re-derived from raw data rather than taken on trust.
Recognise one of these? Tell us the problem.
Method
The team
The whole path, one pair of hands. From the event landing in the cloud to the number a board acts on: dbt, Snowflake, Python, AWS. Rebuilt Deel’s data function as its second data hire, built its marketing attribution, and migrated Convex’s analytics platform off Scala. Deliveroo in between. Ships the fix personally.
Reads a product the way an operator reads a P&L. Funnels, cohorts and experiments on one side; the operational analytics that tie user behaviour to cost and revenue on the other, built across AirHelp, Deel and Allegro. Owns the investigations, and the moment a plausible story meets an inconvenient segment.
Has run the data function, not just advised it. Built Deel’s 35-person data team as its Head of Data, and AirHelp’s 25-person team from scratch before that. Around a hundred analytics hires, over a thousand interviews. Today at Owner.com, deep in AI adoption and semantic layers, the groundwork agents need. Owns the executive conversation and the readiness calls.
Track record
Send the problem, the stack and the outcome you need. We’ll tell you straight whether it fits in one to four weeks, what it would cost, and what we’d explicitly leave out.
Tell us the problem →