Custom AI Products. AI-native software, shipped.

Every custom product engagement runs the same arc — sized to the ambition, never skipped.

1. Discovery (2–3 weeks) — problem framing, user research, model and architecture options, success metrics, evaluation plan.
2. Prototype (2–4 weeks) — a working narrow slice tested against real inputs and real users, with first-pass evals.
3. Production (4–8 weeks) — full system, multi-model orchestration where appropriate, guardrails, observability, deployment pipeline.
4. Scale (ongoing) — usage instrumentation, eval-driven iteration, cost optimisation, on-call posture.

Working software in production, fully owned. Source code, evaluations, prompts, infrastructure-as-code, runbooks, observability dashboards, and a documented architecture. Where useful, an ongoing support arrangement to keep the system honest as models, data and usage evolve.

We bring the same engineering team behind Raise Platform — the proprietary AI capital intelligence platform — to your product.

Discovery on a custom AI product runs on two axes at once: the user problem, and the model's actual capability surface against that problem. We prototype against the model in week one, not in month three, so scope follows what works rather than what sounds ambitious. The output of discovery is a working narrow slice tested against real inputs and real users, with first-pass evaluations attached, a defended product brief and a costed production plan.

This phase deliberately kills features that do not survive contact with the data, the integration surface or the model's tail behaviour. Cutting scope in week three is cheap; doing it in month six is the difference between a launched product and a stalled one.

Every Built AI product carries an evaluation harness from day one. Prompts, retrieval configurations and model selections are versioned and reviewed like code; regressions block deploys. The prototype phase ends when the eval suite consistently passes against representative real inputs — not when the demo looks good in a meeting room. This is the single most reliable predictor of whether an AI product will hold up in production.

Most AI-native products live or die on the quality of the context they assemble. We treat retrieval as a first-class architectural concern: what is indexed, how it is chunked, what is fetched per request, how recency and authority are weighted. Integration design is held to the same standard — clean interfaces with the surrounding SaaS estate, idempotent writes, auditable trails, and the operational discipline to handle the messy reality of production data.

Production rollout is staged behind feature flags with full observability over system health, model behaviour and per-call cost. Dashboards show evaluation pass-rates, drift, latency and token economics in the same view as uptime. On-call runbooks include traces a model engineer can read, not just stack traces. Variable inference cost is budgeted per workflow and alerted on regressions — the unit economics are managed, not discovered in the wrong quarter.

The client owns everything: source code, prompts, evaluations, retrieval configurations, infrastructure-as-code, runbooks and observability dashboards. Built AI retains no rights to client product IP and no lock-in to a Raise AI Ltd platform. Where the product handles regulated, sensitive or customer-confidential data, we design the data residency, access control and audit posture in week one, not as a retrofit. Mutual NDAs are standard from the first technical conversation.