Services · priced in the open

Get implementation-ready. Then ship.

Five offers, ordered as a ladder. Start small: a fixed-price assessment or a rescue of the AI feature you already shipped. The deliverable at each step is scoped to be the plan for the next one — so you never buy a proposal, you buy work. Prices are on the page because your time matters; if a number is wrong for you, we’ve both saved a meeting.

Start with the assessment Or just email me

How the ladder works

Enter small.

A one-week assessment or a two-week rescue. Fixed price, fixed scope, useful on its own even if we never speak again.

The report is the proposal.

Every entry engagement ends with a scoped spec for the build — what to ship, the eval plan, the latency budget, the cost cap. No proposal-writing phase, ever.

Build, then keep it honest.

The Clarity Sprint or a dashboard pilot ships the thing. The ops retainer keeps the evals green and the costs flat after I hand it off.

Where to start

Entry · one week

AI Implementation Readiness Assessment

$5,000 fixed

Founding-client rate: the first three assessments run $2,500 in exchange for a named, written case study. When the third case study ships, so does this rate.

For teams that want an AI feature in a data-heavy product and need to know, before anyone writes code, whether the data, the evals, and the budget will hold.

Data-foundations audit — can your warehouse and pipelines feed the feature you want?
Eval readiness score — what a golden set looks like for your use case, and what it takes to build one.
Latency and cost model for the feature, against your real traffic shape.
A decision-first feature spec, prioritized: what to ship first and what to skip.

The spec doubles as the Clarity Sprint scope. Roughly half of assessments should convert; the other half saved themselves a bad build.

Book an assessment Try the free cost estimator first

Entry · one to two weeks

Eval Rescue Sprint

$7,500 fixed

For teams that already shipped an AI feature and are watching it misbehave in production — without the harness that would say how, where, or how much it costs you.

50–100 golden examples sampled from your real traffic, labelled with your team.
An eval harness wired into your CI, so regressions block the merge instead of reaching users.
A cost monitor with an alert threshold you choose.
A findings memo: the failure modes, ranked by user impact, with the fix I’d ship first.

If the memo says “rebuild it,” the rescue price counts toward a Clarity Sprint.

Book a rescue Size your golden set free

The build

Flagship · two to three weeks

The Clarity Sprint

One AI feature, shipped in production, in your codebase. Eval-first: 50–100 golden examples before any production code, a latency budget and cost cap you sign off on, observability built in. The engagement flow is user moment → spec → eval setup → build → latency/cost validation → handoff. Boring is the goal.

Standard

$18,000 fixed

The feature, working, in your stack.
The eval harness and golden set, in your repo.
Cost monitor and latency validation against the signed budget.
A short doc: what shipped, why, and how to extend it.

With team enablement

$28,000 fixed

Everything in Standard.
Pairing sessions with your engineers throughout the build, not a demo at the end.
A second feature spec’d and eval-planned for your team to ship themselves.
A playbook for running evals and cost reviews without me.

Scoped by the assessment. If you skip the assessment, the first sprint week does that work — same flow, one bill.

Scope a sprint See shipped work

On your data, and after the handoff

Build · two to three weeks

Decision Dashboard Pilot

from $9,500 scoped fixed

For revops, sales ops, and fintech analytics teams: one decision surface on your warehouse, with an AI layer that narrates what changed and why — not a chatbot in the corner. The dashboards on this site are the pattern, rebuilt in the open; the pilot is that pattern on your data.

One dashboard answering one recurring decision — forecast trust, pipeline health, risk triage.
Automated insight narration with an eval set behind it, so the narration is measured, not vibes.
Semantic definitions documented in your repo, so the numbers survive me leaving.

Fixed price set after a scoping call — “from” covers a single-source warehouse; more sources, more scope.

Scope a pilot Browse the dashboards

Ongoing · monthly

AI Feature Ops

$3,000 / month

For teams I’ve built with. Every engagement hands off an eval harness and a cost monitor; this is me watching them so your team doesn’t have to.

Eval regression review on every prompt or model change your team ships.
Cost drift watch against the cap we set, with a monthly one-page readout.
Model migrations — when providers ship new models, I re-run the harness, migrate, and report the before/after. This happens more often than anyone budgets for.

Month to month, cancel anytime. Available after any build engagement above.

Ask about ops

Fit, stated plainly

Where I’m credible

Sales ops, revops, ad tech, analytics tooling, BI, and the modern data stack — fourteen years of it, and the features I build for clients are the ones I watch my own teams need. Decision-first specs, eval harnesses, latency budgets, cost caps.

Where I’m not

Model training, mobile apps, ML platform builds, and domains I haven’t operated in. If your problem lives outside my lane I’ll say so on the first call and, where I can, point you at someone better. That policy is cheaper for both of us than the alternative.

Not sure which rung is yours? Send two sentences about the decision your users are stuck on and what you’ve shipped so far. I’ll reply with the offer I’d pick — or tell you that you don’t need me yet.

Email me Or follow the work first