Production Readiness Foundation
Build the production foundation your AI feature needs before it scales. Evaluation, release controls, observability, failure handling, and the engineering workflow around them.
Scoped to the feature, its production risk, and the team that will own it.
Who It Is For
- You have one or two AI features in production or about to ship
- Evaluation is missing, or what you have is unstructured
- You need release confidence before scaling AI feature count
- You have an engineering team that can own the system, but need senior production engineering help to establish the foundation correctly
- You are willing to commit to a focused engagement scoped to the system and its production risk
The cost of shipping AI features without a foundation
Most teams ship their first AI feature using manual testing, prompt judgement and a small number of known examples. That can work for an early release. It becomes fragile as the system changes.
Without a production foundation, every prompt change, model upgrade, retrieval change, workflow modification or integration change introduces risk that the team may not be able to see before release. Evaluation is part of the answer, but so are release controls, observability, failure handling, integration boundaries and clear ownership.
Production Readiness Foundation puts the right controls around the feature so your team can change it, release it and operate it with evidence rather than judgement alone.
What You Get
Depending on the system and scope, the foundation can include:
| Deliverable | Description |
|---|---|
| Production architecture and boundaries | The production shape of the feature, its dependencies, ownership boundaries and the controls required around it |
| Production evaluation suite | Evaluation coverage for the behaviours and failure modes that matter to the feature |
| Release controls | Evidence-based controls connected to the delivery workflow so unsafe changes can be stopped before release |
| RAG and grounding controls | Retrieval quality, grounding, source attribution and relevant failure scenarios where RAG is part of the system |
| Failure and fallback design | Defined handling for low-confidence, invalid, unavailable, unsafe or degraded system behaviour, including fallback, retry or rollback where appropriate |
| CI/CD and deployment integration | Production controls integrated into the engineering and deployment workflow the client already uses |
| Production observability | Signals required to understand system behaviour after deploy, including regression, drift, latency, failures or other relevant production indicators |
| Evaluation data and release thresholds | Versioned examples, rubrics, expected behaviours and thresholds appropriate to the system and production risk |
| Security and operational controls | Relevant secrets, permissions, access boundaries, human approval points or operational safeguards where required by the system |
| Engineering handover | Documentation, operating guidance, runbooks and knowledge transfer appropriate to the engagement scope |
How It Works
Phase 1: Discovery
Map the target AI feature, its dependencies, production risks, ownership boundaries and the controls required around it. Agree the implementation scope before build begins.
Phase 2: Foundation build
Build the agreed production controls, which may include evaluation, release checks, observability, failure handling, integration controls and deployment safeguards appropriate to the system. Integrate them into the engineering workflow the team already uses.
Phase 3: Validation and tuning
Exercise the foundation against representative production scenarios, failure cases and release changes. Tune thresholds, fallback behaviour, release criteria and operating controls around the risk the system actually carries.
Phase 4: Handover
Finalize documentation, operating guidance and runbooks, then transfer the system context required for the client team to own the foundation.
Investment
Production Readiness Foundation is scoped after the AI Production Audit. Pricing depends on the AI system complexity, production risk, current architecture, integration surface, existing controls, deployment workflow and handover requirements.
After the audit, you receive a fixed-scope proposal covering timeline, deliverables, team structure, and commercial terms.
Success Metrics
Unsafe changes are stopped by an agreed release control before they reach production.
Your team can change prompts, models, retrieval logic, integrations or workflow behaviour with evidence about what changed and whether the system still meets its production thresholds.
When the system fails or degrades, the expected fallback, recovery or escalation path is defined rather than improvised.
Your engineering leadership can answer the question "is this AI feature ready to ship and operate?" with evidence, not opinion.
Sample Deliverable
Depending on scope, the handover can include production architecture decisions, evaluation code, release-control configuration, CI/CD changes, versioned evaluation data, observability configuration, fallback and recovery logic, operating documentation, runbooks and implementation guidance.
FAQ
Give your AI feature a production foundation before you scale it.
Scoped to the system, the production risk and the team that will own it.