AI Services
AI, engineered like infrastructure.
AI that earns its budget, stays inside its limits and shows its work, built and run the way we run cloud and networks.
Most companies have bought AI. Few can show what it returned.
The tools are in. What’s missing is the engineering around them: what AI costs, whether its answers are right, and what it’s allowed to touch. Only 31% of engineering leaders measure AI productivity gains (LeadDev, 2026). Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027 over cost, unclear value or weak risk controls (Gartner).
The gap is rarely the model. It’s the missing backstop: nothing measures the output, nothing caps the spend, and nothing stops a confident wrong answer before it acts.
Start where the pressure is
Pick the problem that sounds like yours. Each one starts with a free first step that takes about an hour of your time.
"The board wants an AI plan, and nobody owns it."
Free first step: AI Strategy Snapshot. Then: AI Strategy Sprint, 3–4 weeks.
"The queue is growing faster than the team."
Free first step: Automation Opportunity Scan. Then: Automation Discovery, 2–3 weeks.
"Our AI bill keeps climbing, and nobody can explain it."
Free first step: AI Spend Snapshot. Then: AI Cost Teardown, 1–2 weeks.
"Leadership mandated AI tools. Security has to say how."
Free first step: AI Governability Scan. Then: Safe AI Adoption Assessment, 2–3 weeks.
"AI wrote our code, and a launch or raise is coming."
Free first step: Code Assurance Scan. Then: AI Code Assurance audit, 1–2 weeks.
"Our engineers use AI every day. Nobody can show what it returned."
Free first step: AI Engineering Scorecard. Then: AI Engineering Baseline, 3–4 weeks, or Harness Foundations, 4 weeks.
The flagship: the Governed AI Platform
Your applications, agents and scripts reach AI models through one checkpoint in your own cloud account. The control that enforces your AI policy also shows each call’s cost and quality, and finds savings that help pay for it. One platform and one discovery instead of two, priced below buying both.
How we keep AI in bounds
We size the controls to what a wrong answer would cost. An assistant a person reads gets light rules. An agent that moves money or writes production code gets the full set.
Code first, model last
Code looks up, calculates and validates everything it can. The model handles only what’s left: language and judgment.
Done is decided by tests, not by the model
“Done” means passing acceptance tests written before the code, enforced by checks the AI can’t change.
Priced on every routed call, scored on a sample
Each model call through the gateway is traced and costed. A sample of the output is scored every night, and hard budgets stop the work when they run out.
Limits enforced in code
Data limits, approval rules and spend caps are enforced by automatic checks, not by a memo people have to remember.
People approve what matters
Money movements and customer-facing actions wait for a person until you choose to automate them. Every action is logged.
Built in your accounts
Gateways, agents and dashboards run in your cloud and your tools, and what we build for you is yours.
Receipts, not hype
We run these controls on our own production systems, and every number below comes from them.
44%
under-count in AI cost reporting, caught because two independent meters check each other
3.3%
of the SEC filings our monitoring pipeline reads ever reach an AI model; plain code screens the rest and does the scoring
1,400+
code-review items fixed by our AI fix agent, at as little as $0.09 each
~480
rules written for AI agents, in daily use across our engineering estate
Every service, by stage
Grouped by what they do: lead, measure and govern, build, equip and run. Every paid engagement is a fixed fee, quoted up front.
Lead: AI Strategy & Leadership
A 12-month AI roadmap with a budget, and a fractional Chief AI Officer who owns it and reports to your board.
Measure: AI Observability & Measurement
Find out what AI is returning, what it costs and which savings apply.
Govern: Safe AI Adoption & Governance
Adopt the AI tools your leadership mandated, with controls that hold, and get your Microsoft 365 tenant ready for Copilot.
Measure: AI Code Assurance
Find out whether your AI-written code, and the tests behind it, will hold up.
Build: AI Agents & Automation
Customer service, answering, workflow, routing, knowledge and research agents, built on clean, trusted data.
Build: Verified AI Delivery
New software our engineers build with AI agents, at a fixed price per milestone, with proof on every release. Start with a scoping call and a fixed quote for the first milestone.
Equip: Agent Harness Engineering
Make your engineers’ coding agents follow your standards, with rules outside the agent’s reach.
Equip: AI Enablement & Training
Hands-on training on your own code and workflows, measured before and after.
Run: Managed AI Operations
We monitor, tune and upgrade your agents and gateway every month, with spend capped. Start with the free AI Spend Snapshot, then a scoped review of what you run today.
How an engagement runs
1. A fixed-fee first step
1–4 weeks, ending in something you can act on: a roadmap, a savings plan, a findings register or a costed automation plan.
2. Build or install, proven as it goes
You approve the specification and the acceptance tests before any code is written. Our engineers build with AI agents under checks the agents can’t change, and every release ships with its test results.
3. Run and improve
We run it for you on a capped monthly retainer, or hand it over with runbooks and trained owners. Either way, cost and quality stay measured.
See governed AI working in 30 minutes
Not ready for a paid engagement? Watch it live, on our own working governed environment. You see personal data blocked at the gateway, budgets enforced, cost and quality scored live, and an agent stopped from editing its own tests.
Or take one of the free assessments, each about an hour of your time.
Questions buyers ask
Will you see our data?
Free assessments run on your side, under your credentials; for the code scan, you can instead give our scan account read-only access to one repository. For paid work, you approve in writing exactly what our engineers can access, and your data is not used to train AI models.
Which models and clouds do you work with?
We’re model-neutral. The gateway routes to Anthropic, OpenAI, Amazon Bedrock and open models, and we install on the stack you run. Connectors for anything else are part of the delivery.
How do you price?
Every engagement is a fixed fee, quoted up front after a short scoping call. Pricing scales with the systems and teams in scope. Monthly services are capped retainers: one fixed monthly fee, not an hourly bill. Model and cloud usage pass through at cost or sit inside the fee under a hard cap.
Who owns what you build?
You do. Systems run in your accounts and your source control, documented so your team can run them.
How quickly can we start?
As soon as the scope is agreed on a short scoping call. Entry engagements then run 1–4 weeks, and builds and installs typically take 4–12 weeks.
Find out where AI will pay off for you.
Book the live demo, pick a free assessment, or tell us what you’re working on. Your message goes straight to our senior engineers.