AI Production Readiness Consulting
Move from a promising AI prototype to a production system your team can observe, operate, secure, and improve without uncontrolled cost or risk.
Readiness score across product, data, model, infrastructure, security, and operations
Concrete launch blockers, risk owners, and a prioritized remediation roadmap
Evaluation, monitoring, rollback, and cost-control plan for production AI systems
What the assessment covers
AI production readiness consulting for agents, RAG and LLM applications. Identify launch blockers, define quality checks and plan costs, monitoring and recovery.
Use-case fit, success metrics, escalation paths, and human-in-the-loop boundaries
Prompt, tool, and retrieval evaluation coverage with regression fixtures
RAG quality, source freshness, data contracts, and hallucination failure modes
Model gateway design, rate limits, fallback models, token budgets, and cost telemetry
Tracing, audit logs, drift signals, red-team cases, and incident response runbooks
Privacy, data retention, access control, and vendor boundary review
Deliverables
- Written production-readiness report
- Launch checklist and risk register
- Evaluation and monitoring recommendations
- Architecture notes for the production path
Engagement Flow
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1
Initial consultation and architecture walkthrough (€999, including follow-up and concrete first steps)
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2
Hands-on review of prompts, pipelines, telemetry, deployment, and security controls
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3
Findings workshop with prioritized fixes and ownership
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4
Optional implementation sprint for the highest-risk gaps
Common problems this catches
AI features that work in demos but lack measurable quality gates
RAG pipelines with no retrieval diagnostics or source-quality feedback loop
Token costs and latency that are invisible until traffic grows
No safe rollback path when a model, prompt, or provider behavior changes
Questions Teams Ask
Short answers before the initial consultation.
Is this only for generative AI?
No. The review is useful for LLM applications, agents, RAG systems, ML-backed workflows, and AI-assisted internal tools where reliability and governance matter.
Do you implement fixes too?
Yes. The first engagement can be an assessment only, or it can continue into a focused implementation sprint for evals, observability, deployment, or security controls.
What access do you need?
Usually architecture diagrams, code or pipeline access, prompts or system instructions, model gateway configuration, telemetry, and a walkthrough with the owning engineers.
Related Services
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