Assesses readiness
Reviews your idea or POC honestly: what's real, what's missing, what it takes to make it production-grade.
Foundations · Delivery
Get AI from "promising demo" to "running in production".
Plenty of AI pilots impress and then stall. Implementation is the unglamorous part that makes them real: integrations, evals, monitoring, rollout, and handover. We take an idea or a stuck POC and get it live, reliable, and owned by your team.
What does AI implementation involve?
AI implementation is the end-to-end delivery that turns an AI idea or proof-of-concept into a reliable production system. It covers integration with your real tools and data, an eval suite so you can trust outputs, monitoring and alerting, a phased rollout (often human-in-the-loop first), and handover documentation so your team can run and extend it. Foundations tier. It's the difference between a demo that wows and a system that works on a Tuesday at 3pm. It underpins every agent we ship, but we also do it for AI projects you've already started elsewhere.
theagency47 · Updated June 2026 The spec sheet
How the AI Implementation actually works.
| Job-to-be-done | Take an AI idea or stuck POC to reliable, monitored, owned production |
| Tier | Foundations (delivery process) |
| Underlying model | Anthropic Claude (or your existing model choice) |
| Phases | Assess → integrate → eval → pilot (human-in-loop) → production → handover |
| Inputs | Existing POC or spec, target systems, success + safety criteria, data access |
| Deliverables | Integrations, eval suite, monitoring/alerting, rollout plan, runbook + docs |
| Guardrails | Evals on every change, human-in-the-loop windows, audit logging, rollback plan |
| Ownership | Your team can run, monitor, and extend it |
| Outcome measured | Reliability/uptime, eval pass rate, adoption, incidents, time-to-production |
What it does
The work it takes off your team.
Integrates for real
Connects to your actual tools and data: the step where most pilots quietly die.
Builds the eval suite
Creates the tests that let you trust outputs and catch regressions before users do.
Monitoring & alerting
Instruments the system so you know when something drifts or breaks: before customers tell you.
Phased rollout
Ships with a human-in-the-loop window first, then graduates to autonomy as confidence is earned.
Handover
Leaves your team a runbook and docs so they can operate and extend it without us.
Integrations
Standard integration stack.
- Your stack: the tools and data the system must touch
- Models: Claude or your existing choice
- Evals: automated test suite in CI
- Monitoring: logging, alerting, dashboards
- Handover: runbook, docs, training
Custom integrations with proprietary systems are quoted as add-ons. Not sure if yours fits? Describe your stack and we'll confirm.
What it looks like in practice
A typical run.
- Assess, Honest review of the idea/POC and a plan to production.
- Integrate & eval, Wire it to real systems; build the eval suite and monitoring.
- Pilot, Run human-in-the-loop, measure, tune until it clears the bar.
- Production & handover, Graduate to live, document, and hand your team the keys.
FAQ
Questions about the AI Implementation.
We have a POC that stalled (can you finish it?
Yes) that's a common starting point. We assess what you have, fill the gaps (integration, evals, monitoring), and get it to reliable production, whether or not we built the original.
Why do AI pilots fail to ship?
Usually not the model, it's the unglamorous parts: real integrations, trustworthy evals, monitoring, and a safe rollout. Implementation is exactly that work, which is why we treat it as a discipline of its own.
Will my team be able to run it?
Yes, handover is part of the deliverable. You get a runbook, documentation, and training so you can operate, monitor, and extend the system without depending on us.
How does this relate to your other services?
Implementation underpins every agent we build. As a standalone, it's for AI work you've started elsewhere. See how we work and the Enterprise engagement.
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