Production AI Systems & Research-to-Production
Turn a promising prototype into an observable operating system.
Who commissions this
A defined technical problem.
Teams with a validated prototype or research result that must meet production reliability, integration, and support requirements.
Engineering scope
What the work covers.
- Establish task-level acceptance criteria and representative deployment workloads.
- Build reproducible model, data, and configuration release pipelines.
- Implement integration contracts, monitoring, fallback behavior, and human escalation.
- Plan progressive rollout, rollback, regression evaluation, and operational handover.
Representative deliverables
Work your team can inspect and use.
- Production architecture and integration contracts
- Versioned evaluation harness and release gates
- Deployment and rollback runbooks
- Monitoring specification and operational ownership plan
A measurable outcome, scoped together.
A bounded path from research uncertainty to deployment readiness. Production acceptance depends on measured results under the agreed workload.
Typical engagements begin at $50,000. Before a proposal, we agree the business objective, data access, system boundaries, deliverables, acceptance criteria, and operating owner. Timelines depend on scope and access.
Related technical evidence
Production AI architecture and inference economics · How to verify an AI performance claim
Explore all engineering services · Independent technical assurance