We close the gap between the data you have and the decisions it should be improving.

A small senior team sits in your stack, gets the numbers agreeing, and builds the systems and AI agents that act on them.

Teams we've worked with
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Fashion & Apparel | Wholesale
Food & Beverage | Licensing
Home & Kitchen | Omnichannel
Food Marketplace | SaaS
Autonomous Vehicles
Fintech | Financial Services
Health & Fitness | SaaS
Luxury Camper Vans | DTC
Maternity Apparel | DTC
Apparel & Fashion | DTC
Sound familiar

Where people usually are when they call us.

Sick of agencies.
Fair, and on paper we are one. The difference is structural: everything is built in your stack under your name, and there is no notice period. If we stop being useful, stopping is easy.
AI sounds right, and you have no time to work out what is actually trustworthy.
You should not have to become an AI evaluator to benefit from it. We bring patterns already running in other operations, set them up under your approvals, and stay until your team runs them without us.
Too busy to fix it in-house.
Your team knows the reports disagree, and nobody has three clear months to fix why. We do that work inside your stack with a couple of hours a week from your side, and the definitions end up written down and tested.
You do not trust the measurement, and nobody has shown you a better option.
There is no one true number, and anyone selling one is guessing. We match the method to the decision and tell you how much confidence each answer carries, including when the honest answer is directional at best.

Underneath all four it is the same work, in the same order: make the numbers trustworthy, build the systems and agents that act on them, and hand it over working.

Where we fit

Everyone is piping their data into AI. The gap is knowing which answers you can act on.

Platformsship one-size answers they can't rebuild around your business.
Agencieshand you the report; whether it's right stays your problem.
In-houseworks, if you can put senior people on it full time.

We build the governed layer in your stack, so every answer knows whether it has been signed off, and you own all of it.

How we build AI solutions
Ask the dataGoverned layer · live
Head of GrowthWhat was CAC last week, and is there room to push Meta harder this month?
$62 blended CAC
~8% better than the trailing 4 weeks, driven by new-customer campaigns.
Governed · finance definition, tested nightly
Meta new-customer CAC looks ~15% better on its own, but that cut has not been signed off yet. Fine for direction; it has been flagged for review before it drives the budget.
Not governed · sent for review

Illustrative example.

Proof

What happened when we did it.

Different businesses, same approach. The full studies say how each result was measured.

Home & lifestyle DTC brand
Home & lifestyle DTC brand
Two flat years, then +30% topline and EBITDA in the first year the budget ran on measurement the team trusted.

The changes were earlier TV and direct mail flighting and a re-ranked channel mix. We will not claim the split between the measurement and a good year. The team now runs it without us.

$400M ARR meal marketplace
$400M ARR meal marketplace
Letting the system choose each customer's next offer added +10% revenue per user.

One promo calendar for everyone became a per-customer decision about which offer comes next, optimizing toward the same number finance reports.

Multi-site care operations · illustrative composite
Multi-site care operations · illustrative composite
A nightly agent now does the reconciliation that used to eat someone's Mondays.

Census, billing, and the CRM never agreed, and a person absorbed the difference by hand. The agent matches them overnight, clears routine mismatches on rules the operator signed off, and escalates the genuine ones. Actions taken without approval: zero, by design. [Composite of the agent pattern we deploy; swap for a named pilot when one is publishable. Owner: Sam]

Read the full case studies
How it starts

We start by doing some of the work, because it is the fastest way for both of us to know if this is worth it.

Most of our work comes through referrals, so the first conversation can just be about your problem. From there, engagements grow in small steps that each have to earn the next, and this is what that has looked like:

amp · Health & Fitness SaaSStart to today

amp went from metrics nobody trusted to a growth equation the whole company could bet on.

A look at one number
Cost nothing, took under an hour of their time, and found no reliable growth equation and metrics nobody trusted.
$4k fixed, four weeks
One trusted core metric layer: the growth equation, defined, tested, and agreed with finance.
Scope grew as it earned it
Marketing, product, and ops reporting, each added when the last piece proved out.
Embedded partner
Now including governed AI self-serve, live in production.
amp grew from under $1M to $30M ARR in that time. Data maturity stayed ahead of the business the whole way, so the right bets got pressed early and wrong turns got caught before they got expensive.

Get in touch.

Tell us what you are trying to figure out. If we can help, we will say how. If we are not the right fit, we will say that too, and point you somewhere better.