One use case is a project. Twenty is an operating capability. The AI Factory is the model that gets you from the first to the twentieth without the cost, governance and quality curve bending the wrong way.
Ask an enterprise what its tenth AI use case cost and you usually learn it cost roughly what the first one did. That is the definition of a programme that has not industrialised — every team rebuilding retrieval, evaluation, governance and deployment from scratch, in slightly different ways, with slightly different risks.
A delivery model with four permanent components: a shared technical platform, a library of proven patterns, a standing multi-disciplinary team, and a governed intake-to-production pipeline. Use cases flow through it. The platform, patterns and controls persist and improve.
It is how Adroitent builds AI internally through DROIT Labs and the Agent Factory, and it is the model we stand up inside client organisations — as a managed capability, or as a dedicated GCC that transfers to you.
Data foundation, retrieval infrastructure, orchestration spine, model gateway, evaluation harness and observability — built once, consumed by every use case. AATMa and AARAM provide the lifecycle and assurance layer.
Reference implementations for document intelligence, agentic service operations, retrieval assistants, forecasting and workflow automation — each pre-evaluated, instrumented and governed. Most use cases start at 60% complete.
A durable pod of AI engineers, data engineers, evaluation specialists and a governance lead. No re-forming, no re-learning your estate on every project.
A single route from idea to production: intake, value and risk triage, design review, build, assurance gate, release, monitor. Every use case takes the same path, so nothing reaches production ungoverned.
| Phase | Timeline | What is delivered |
|---|---|---|
| Foundation | Weeks 1–6 | Platform baseline, intake and governance pipeline, team stood up, first use case in build |
| Proof | Weeks 6–14 | Two to three use cases delivered to production through the pipeline; patterns extracted and hardened |
| Scale | Quarter 2 onward | Throughput increases; pattern library grows; unit cost per use case falls |
| Transfer | Optional, to 36 months | Capability transitions to a client-owned team or dedicated GCC under Build-Operate-Transfer |
Where the Factory is delivered as a dedicated GCC, launch runs 4–8 weeks with a zero-CAPEX operating model and a defined 36-month Build-Operate-Transfer path to full client ownership.
A declining cost per use caseInstead of a flat one, because platform and pattern investment is amortised across the portfolio.
A governance posture that holdsAutomatically, because the pipeline enforces it rather than a committee remembering to.
Faster time to productionOn every use case after the first, with most starting from a hardened pattern rather than a blank repository.
A single evaluation standardAcross the estate, so quality is comparable between use cases and teams.
An institutional capabilityThat stays when the engagement ends — people, platform, patterns and process.
Broadly, five or more within eighteen months, or fewer if they share a data foundation and a governance burden. Below that, a Forward Deployed Engineer pod is usually the better economics.
No. Platform, patterns and documentation are yours, built on open and portable components, with an explicit transfer path. Retention should be earned by throughput, not by dependency.
Yes — most often it becomes the delivery arm of one. The CoE sets standards and prioritises; the Factory builds and runs. We map the boundary explicitly during the foundation phase.
Talk to an Adroitent AI lead. We will come with a point of view on your estate, not a generic capability deck.
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