AI Implementation
Pilots don’t pay.Production does.
We deploy AI into the workflows where your most expensive people spend their least valuable hours — and we prove the economics before we build.
The failure pattern
Why most AI programs stall.
Every one of these is preventable before a line of code gets written.
The engagement
How the work runs.
Diagnose
Two to three weeks. We map every workflow, price the human bottleneck in each, and rank them by value at risk, effort to ship, and payback period. Deliverable: a ranked portfolio and a business case for the top three.
Prove
One workflow. A narrow, instrumented build with a defined success metric and a defined kill criterion, agreed before we start. If the economics don’t hold, we stop and tell you.
Build
Production deployment — integrated with your systems, monitored, evaluated against a test set, with human checkpoints where the cost of a wrong output is high.
Adopt
Enablement role by role, documentation, governance policy, and a cost and latency dashboard your operator can run without us.
Capabilities
What we build.
AI Readiness Diagnostic
A workflow-by-workflow audit of where AI can earn its cost in your business. We interview the operators who live inside the process, quantify the hours and dollars a bottleneck consumes today, and rank every candidate by value at risk, effort to ship, and payback period. You leave with a ranked portfolio, not a wish list.
Use-Case Portfolio & Business Case
The three to five deployments actually worth doing, each with a modeled cost per action, a clear line of sight to profitability, an integration plan, and a kill criterion agreed before we start. If a use case can’t defend itself against a spreadsheet, we don’t build it.
Agentic Workflow Design & Build
Production systems inside sales, support, operations, and finance. Designed around your existing tools, built with the model that fits the job, integrated end-to-end, monitored in production, and handed over with documentation the next engineer can read.
Context & Data Architecture
The memory, retrieval, and grounding layer that makes AI accurate on your business instead of confident about the internet. Document ingestion, structured knowledge, evaluation sets, and the governance controls that keep answers correct as the underlying data changes.
AI-Native Go-to-Market
Pipeline generation, content production, research, and enablement engines that run on agents — so output stops scaling with headcount. Built with brand guardrails, human review where it matters, and reporting that ties activity to revenue rather than to volume.
Governance, Adoption & Unit Economics
Policy, evaluations, human-in-the-loop checkpoints, and token, latency, and cost discipline. A cost-per-action dashboard your operator can read without a data team, plus the change-management work that gets people to actually use what we ship.
The economics
The number that decides everything.
Whether AI actually earns its cost in your business. Not blended gross margin. Not a projection about falling model costs. Whether one unit of delivered value costs you less than you charge for it.
If that math is negative and volume is growing, growth is the problem. We pressure-test it first, model it at two, five, and ten times current volume, and design the deployment around holding the line.
“Every inference has a bill attached. Most decks haven’t read it.”
Fit
Who this is for.
- Founders with real revenue and a workflow that’s expensive in labor hours.
- Founders who want a system in production, not a proof of concept.
- Teams willing to change how they work, not just add a tool.
- Leaders who want the unit economics modeled before the build.
- Pre-revenue founders looking for an AI feature to raise on.
- Organizations that want a strategy deck with no deployment.
- Teams where nobody will own adoption after handover.
- Anyone shopping for the cheapest quote.
Bring us the workflow that’s bleeding.
We’ll model the economics before we touch a line of code. If it doesn’t pay, we’ll tell you.