AI MVP development is the process of designing, building, and launching a production-ready AI product around one core LLM or agent capability. 7code ships AI MVPs in six weeks — including an evaluation harness, cloud infrastructure, and a real production launch — with fixed scope and pricing from £25k.
We design, build, and ship LLM-powered products end-to-end, with an evaluation harness, cloud infrastructure, and a clean production launch, all inside a six-week fixed scope.
Discovery phases run long. The spec changes when the demo lands. Engineering starts before the eval set exists. By the time you have a production system, the model it was built on is two generations old. We've inherited enough of these projects to design against them.
We map the product against LLM capabilities, define what 'good' looks like, and build the held-out evaluation dataset before writing a line of product code. Architecture decisions made here have a 10× impact on what's possible at week six.
Two-week sprint with a deployable build at the end. Every AI feature is scored against the held-out eval set at the sprint review — not demoed on cherry-picked prompts. Regressions are caught on day one, not month three.
Production hardening, observability, prompt versioning, cost controls, and a clean launch. You end week six with a shipped product, an eval harness your team can run, and dashboards for latency and token spend.
Claude, GPT, or open-weight models integrated with streaming, structured outputs, and fallback paths designed from day one.
Retrieval-augmented generation over your data, or a scoped agent pipeline with tool use and human-in-the-loop checkpoints.
A held-out eval set and automated scoring that proves quality before every release and catches drift in production.
Cloud-native deployment (AWS, GCP, or Azure), CI/CD, monitoring, and cost controls included in the six-week scope.
Versioned prompt library with A/B testing and regression tracking, not ad-hoc edits in a shared Notion doc.
Architecture docs, runbooks, and a working eval CI pipeline your engineering team can own from day one after launch.
An AI MVP is a minimum viable product built around a core LLM or agent capability — a copilot, a RAG search, an intelligent automation — shipped to real users in six weeks to validate the value proposition before a larger build investment. Unlike a traditional MVP, it needs an evaluation harness from day one, because AI quality degrades silently and you need a metric, not a vibe, to know if it's working.
Yes. OctoLabs (AI support copilot) went from kick-off to a production system deflecting 47% of support tickets in six weeks. Daily8's AI moderation and summarisation features shipped inside a six-month engagement. The pattern: ruthlessly scoped capabilities, eval-gated sprints, and a team that doesn't need to learn the stack mid-project. The six-week clock starts with a real kick-off, not a discovery phase.
Product design (AI-aware UX), the LLM or agent integration, RAG pipeline if needed, evaluation harness, production cloud deployment, basic observability (latency, cost, error rate), and a handover package. It does not include extensive data migration, complex third-party integrations, or mobile apps, those extend the timeline.
Most clients do. We typically move to a sprint retainer (two-week sprints, rolling monthly) or an outstaffing arrangement where we embed one or two senior engineers in your team. The MVP's clean architecture and eval harness make it straightforward to add features without accumulating technical debt.
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