Production-grade AI engineering consulting.

A named workflow implemented in your environment, with permissioned retrieval or tool integrations, schema-validated outputs and evaluation against your own data. Architecture decisions, integration contracts and quality criteria make the scope explicit before the team commits to a rollout.

Production delivery includes CI evaluation gates, prompt and tool-call tracing, cost and latency budgets, IAM controls, audit logging and a non-AI fallback for failure paths. Your engineers receive the source code, evaluation suites, architecture decision records and runbooks, with pairing throughout the engagement.

An AI Proof of Value takes 4 to 8 weeks and produces an evaluated working system, an integration plan and an evidence-backed go / no-go recommendation. It is not a promise of full production deployment in that time. A well-scoped production implementation typically takes 3 to 6 months, depending on data access, integration, security review and governance.

Start with one business workflow, a technical owner, representative data and access to the people who approve security and quality. Agree acceptance criteria for quality, latency, cost and escalation before rollout. An AI Engineering Pod can then work alongside your team across architecture, AI, integration and production engineering.

Because the prototype solved the easy half. The hard half is permissioned data access, integration with systems of record, evaluation that a reviewer accepts, observability, cost control at volume, and behaviour on the failure paths nobody demoed.

That work is ordinary senior software engineering applied to a new component. It is also why a team that only writes prompts stalls at the security review, and why we staff engagements with engineers who have shipped platforms before.

It means the model is one dependency among many. A working AI feature needs retrieval with access control, schema-validated outputs, idempotent writes, tracing, budgets, fallbacks and a release process — and each of those is code we write and hand over.

It also means we will tell you when a smaller model, a classifier or a plain database query is the right answer. The cheapest AI engineering is the work you do not have to run.

By treating tokens as infrastructure spend. Classification and extraction go to small models, the main workload to a mid-tier model, and only genuine hard cases escalate — with the escalation rule set by the eval suite rather than by instinct.

Stable context is cached, budgets for cost and latency are enforced per request at the gateway, and spend is attributed per feature so a regression shows up on a dashboard rather than on an invoice.

A model gateway in front of every provider, so routing, retries, fallbacks, rate limits and per-feature budgets live in one place instead of being re-implemented in every product team. Prompt and tool registries with versioning, so a change to a prompt goes through the same review and release process as a change to code.

Underneath that: ingestion and embedding pipelines with backfill and replay, a vector store with tenancy and access control matching the source systems, secrets in KMS, and deployment inside your VPC on Bedrock, Azure OpenAI or Vertex. This is the layer that turns one AI feature into a platform several teams can build on.

Through typed, least-privilege tool surfaces — usually MCP servers or service APIs — with identity pass-through so the model acts as the user, not as a service account with broad access. Writes are idempotent and approval-gated; reads are scoped and logged.

The failure modes get as much design time as the happy path: timeouts, partial writes, stale data, and a graceful non-AI fallback when a dependency is down. Integration is where most enterprise AI projects stall, and it is ordinary integration engineering — done to the same standard as the rest of your estate.