AI Transformation
AI transformation for growing companies, from readiness to production. Whether you build AI into your product or want AI working in your operations, we find the work it can take on, get the data and one owner in place, build it into your systems, and keep its cost and reliability in check. The people who advise are the people who write the code.
30 minutes. You leave with a written read, whether or not we work together.
AI transformation at a company your size is not a strategy deck. It is a short list of workflows worth automating, each with one named owner, data that can feed it, a bar it must clear before production, and a cost ledger once it is live. One workflow at a time, running in production before the next one starts.
We take each one from pilot to production: pick it by return, build the agent or automation, wrap it in evaluations, cost ceilings and human sign-off, and run it until it is boring. The code, the evals and the runbooks stay with you.
Where the Gains Show Up
The point of production is a number that moves. Independent studies published since 2025 find it moving on specific work.
- 15% more customer issues resolved per hour across 5,172 support agents given an AI assistant. The gains went mostly to newer, less experienced agents. Quarterly Journal of Economics, 2025
- 7.5 days less delay before transactions were recorded at 79 US companies using AI in financial reporting. An association in field data, not a controlled trial. Journal of Accounting Research, 2026
- 31% of AI-using US businesses with up to 499 employees reported higher sales because of it. Most saw no change in their labor costs. Federal Reserve Small Business Credit Survey, 2026
None of them measures payback: whether the gain covered what the AI cost to build and run. That is the number we measure, against a baseline taken before the build.
Our process
How We Work
An example of a typical 12-week engagement, from kickoff to a working AI pilot, then on to production and handoff. Timelines vary with each engagement; the phases and their order stay the same.
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Week 1
Kickoff
We agree on scope, what success looks like and a weekly check-in before any work starts.
You receive: Kickoff summary, project plan
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Weeks 1–2
Access and onboarding
We clear your security and vendor onboarding and get working access to the systems and data in scope.
You receive: Confirmed access, updated plan
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Weeks 1–3
Discovery and assessment
Interviews with the people who run the work, workflows mapped end to end, data and stack assessed, and baseline metrics captured so results can be measured later.
You receive: Current-state and AI-readiness findings
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Weeks 3–4
Roadmap and prioritization
Use cases scored on value, feasibility, data readiness, risk and time to value. You sign off on what gets built first.
You receive: Prioritized roadmap, business cases, guardrails
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Weeks 5–12
Build and pilot
Two-week sprints, each ending in a demo, built alongside your engineers if you have them. A working pilot goes into the real workflow and is measured against the baseline every week.
You receive: Working pilot, sprint demos, weekly results
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Month 3 onward
Rollout and enablement
Production hardening: monitoring, quality evaluations, cost controls and incident handling. Your people trained to own it.
You receive: Production deployment, training, updated SOPs
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Final weeks
Handoff and close
Documentation and runbooks detailed enough to run, rebuild and extend the work without us, and a results report against the baseline.
You receive: Documentation, results and ROI report
Client survey
What Our Customers Say
After seven years of building production AI systems, we surveyed our clients. Their averages:
Work hours and productivity
+33% productivity
−7.2% work hours
+65% productivity
−3.5% work hours
Business results
+25%
deals closed
Every 4 became 5
+35%
projects delivered
Every 20 became 27
−45%
entry‑level headcount
Every 20 hires needed became 11
+13%
Net Promoter Score
The research methodology and more detail are available on request. Ask us for it
Where Do You Need AI?
Often one, then the other, or both at once.
AI in your product
It's in your product or pipeline, and scale brought the problems: a bill growing faster than revenue, pilots that fail on real data, agents nobody inside can change.
AI in your operations
So far it's a few ChatGPT accounts and a plan nobody owns, and you want it doing real work: support, document intake, scheduling, reporting.
AI Readiness Assessment
Every engagement starts here: a fixed-price first phase that shows what to build first and what it will take. You leave with:
- Your workflows, ranked by return and by what blocks them
- A data verdict for each: ready, needs work first, or not viable
- The bar each must clear before production
- A baseline for the first workflow, so the return can be proven
- A fixed-price plan for the first build, with a clean exit
30 minutes, then one fixed price for the assessment. If there's nothing worth building yet, you hear that first.
What We Build
- Use cases and ROI
- Workflows ranked by return, a baseline before the build, and kill criteria agreed up front, so every review ends in ship, stop or change.
- Pilot to production
- Evals on your real records, guardrails and rollback. W6W went from 10 to 152 integrations a week at 99.1% accuracy.
- Agents and automation
- Single and multi-agent systems, a person in the loop where it matters, no PII reaching the model. One outbound pipeline lifted successful outreach 430%.
- Cost and reliability
- A cost ledger per task, the cheapest model that passes, budget ceilings. A YC-backed startup's AI bill fell 47.7%; an 800% cloud spike was contained without the source code.
- AI in engineering
- Planner, developer, tester and reviewer agents with evals, cost ceilings and human sign-off. We run our own development this way.
- Governed from day one
- Model inventory, use policy and risk register come with the build. More on AI governance.
- After launch
- Models, prices and traffic change. Most clients keep us on as a fractional CTO and CAIO to run the next workflow.
Questions We Get Asked
What is an AI readiness assessment, and what do we get at the end?
A fixed-price first phase of a few weeks, before anyone builds. You get your workflows ranked by return, a data verdict for each, the bar each must clear before production, a baseline for the first one and a priced plan for the build. The report stands on its own, even if you build with someone else.
How much does AI transformation cost?
Each phase is quoted as one fixed price. The readiness assessment is priced after a free 30-minute call, once we know how many workflows and systems are in scope, and the build is quoted the same way with its own clean exit. You know what each phase costs before it starts.
How do we move an AI pilot to production?
Set a production bar and build to it: evaluations on real records, guardrails on what the model may say or do, a person in the loop where one belongs, and rollback triggers that take it out of service on their own. Most pilots fail at the first part, because the data they were shown isn't the data they will see, so the work usually starts there.
How do we measure AI ROI if we never set a baseline?
Reconstruct one. Most workflows leave a trail in ticket timestamps, order logs, payroll hours or CRM activity. We rebuild the before-state from it, agree on what counts as the return, and measure the after-state the same way. From then on, every workflow gets its baseline before the build.
We're behind on AI in our operations. Where do we start?
With the readiness assessment, not a chatbot or a platform. We find where the hours go in support, intake, scheduling, documents and reporting, build the first automation into the tools your people already use, and measure the hours it gives back. If you have no technology function to run it, we run it.
Should we build or buy AI agents?
Buy when the workflow is generic and the vendor's agent can be measured, contained and switched off. Build when the workflow is how you make money, when your data can't leave, or when the vendor's agent is already the problem. Most companies end up with both; what matters is that every agent is logged, evaluated and under a cost ceiling.
How do you keep AI costs under control?
Measure, route, trim, own: a cost ledger per task, routing to the cheapest model that passes its evaluation, less context and fewer retries per call, and self-hosted inference for steady workloads. That is how a YC-backed startup's AI bill fell 47.7%.
How is this different from an AI strategy consultancy?
We build it. The senior practitioner who writes the readiness report writes the first code and is on the call when something breaks. The code, the evals and the runbooks stay with you.
Start With Your First Workflow
Bring the pilot that stalled, the bill that spiked, or the job you want AI to take over. A free 30-minute call, then a written read and a fixed price.