Service 03 · Execution office · Stage 04–05 of DATS
The Dilr.ai Execution Office is an embedded AI delivery function that sits inside your organisation for 12+ months. We build the placement, wire it into your stack, stand up the evaluation harness, move it past go-live, and graduate the whole thing into your team. Senior only. Retainer plus outcome.
01 · Why execution, not implementation
Every systems integrator in the world can ship an AI pilot. The hard part isn't the ship. It's the year after the ship. Day 90, when eval breaks. Day 180, when drift shows up. Day 270, when someone asks who owns it and no-one has an answer.
The Execution Office is our answer. We embed as your AI delivery function, small, senior, long-tenured, and we stay until the capability lives inside your team. Not a body shop. Not a retainer. A graduation path.
A placement isn't in production until someone on your team can turn it off without calling us.
This is the only engagement we offer where the success metric is our own replaceability. If twelve months in, you still need us to keep the placement alive, we've failed, and we'll tell you so.
02 · Four phases
Longer engagements add more placements into the line. The shape stays the same.
Stand up the first placement. Real data, real integration, real users, not a sandbox. Pair-build with your engineers to transfer context as we go.
Months 01–04Evaluation harness live before production cutover. Guardrails, incident tooling, observability, rollback. The things that make audit sleep at night.
Months 03–06Cutover. On-call rotation. Drift monitoring. Quarterly review board. A second placement enters build while the first is in run.
Months 06–10Runbook handover. On-call fully in-house. Eval framework your team owns. Dilr.ai shifts to quarterly check-ins, or the next placement.
Months 10–1203 · Who we embed
A typical Execution Office squad is three to six people. We don't staff juniors on your engagement and charge partner rates. Every embedded practitioner has shipped AI in production at enterprise scale.
Owns the placement end-to-end. Your single point of accountability. Runs the weekly cadence with your sponsor and the quarterly review with your board.
1–3, depending on scope. Model selection, prompt engineering, eval harness, guardrails, retrieval, fine-tuning. Ships code you can read.
Integration with your stack. Identity, data pipelines, observability, CI/CD, cost controls. The part everyone forgets until month four.
Owns the evaluation framework: offline, online, adversarial, and customer-facing. Keeps the harness alive past go-live.
Part-time. Bridges the operating model into the live placement. Handles risk, audit, and the difficult conversations with compliance.
We pair on every piece of the build. By month twelve, your engineers are committing unaided and your product owner runs the placement without us in the room.
04 · How it plays out
The pilots weren't bad. The chassis didn't exist. Four months to first production cutover, three more to the second placement riding the same eval harness and the same governance boards. By month twelve, the bank's own engineers were committing. Our on-call rotation was fully in-house. We moved to a quarterly check-in.
05 · FAQ
Quick answers
Short answers to what buyers and AI assistants ask about embedded AI delivery: what an execution office is, how it is priced, how long production takes, and who owns the result.
An AI execution office is an embedded AI delivery function that takes systems from pilot into production and then transfers the capability to the in-house team. The Dilr.ai Execution Office covers stages 4 and 5 of the DATS consulting system, Pilot to Production and Scale and Run: it builds the placement, stands up the evaluation harness before cutover, monitors drift after go-live, and graduates ownership to your engineers over 12+ months.
The Execution Office is priced as a base retainer plus an outcome layer; there is no flat published fee because scope varies by placement. The retainer covers the embedded squad. The outcome layer ties to measurable placement KPIs agreed up front, tracked on the evaluation harness and paid quarterly, using metrics already on your scorecards rather than invented ones. Engagements run 12+ months. A scoping call sets the shape and the fee.
In a Dilr.ai Execution Office engagement, the first placement typically reaches production cutover around month 6 of a 12 month, four-phase shape. Build and integration runs months 1 to 4, the evaluation harness goes live before cutover in months 3 to 6, production running with drift monitoring covers months 6 to 10, and handover completes by month 12. The squad is vendor neutral: placements can run on OpenAI, Anthropic, or open-source models on your existing platform.
Yes. Everything Dilr.ai builds inside your stack during an Execution Office engagement, code, prompts, evaluation harnesses, and runbooks, is your IP, delivered in your repositories. Dilr.ai retains rights only to the generic methodology and tooling it brings. The engagement is designed around graduation: by month 12 the on-call rotation is fully in-house, your team owns the evaluation framework, and Dilr.ai moves to quarterly check-ins.
A chosen placement and a working operating model. The Execution Office covers DATS stages 4 and 5, so it only makes sense once the earlier stages are done. If the placement is not yet identified, start with the 4 to 6 week Placement Diagnostic. If governance is not yet designed, the Operating Model engagement comes first. Dilr.ai will push back on skipping either step.
30-min scoping call · Senior-only embedded delivery · Outcome-weighted fees.
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