GenAI / RAG Production Readiness
The AI feature looked good in a demo, but real users expose slow answers, wrong answers, and unclear costs.
Book a production reviewBest fit
Teams moving AI features from demo to production.
Timeline
2-6 weeks
Proof frame
Sanitized examples can be discussed with sanitized or NDA-safe detail.
Symptoms first, architecture second.
The useful work starts by naming what is hurting, what can be measured, and what can be changed safely this week.
What you may be seeing
- Answers are inconsistent or hard to trust
- Latency and cost per request are unstable
- Prompts and retrieval changes are not versioned
- There is no eval or fallback behavior
How I find the cause
- grounding and retrieval quality
- context design, prompt/version control, and evaluation
- guardrails, audit trail, model routing, and observability
- cost per request and fallback behavior
The output has to survive handoff.
The point is not a prettier diagram. The point is a plan that names service boundaries, owners, rollback, cost drivers, and what gets observed.
The model is rarely the whole problem. I will not hide retrieval, eval, or failure handling behind prompt changes.
What you get
- - readiness report
- - eval plan
- - cost controls
- - retrieval and context recommendations
- - production runbook
What to bring
- - sample queries
- - source data map
- - current prompts or flow
The language this service is meant to own.
These are not keyword decorations. They describe the buying problem this page is built to answer.
Case studies that support this service.
AI-Driven Development
ASTM International · Enterprise engineering enablement
Ad-hoc AI coding → governed Claude Code delivery loop with standards, app KBs, skills, commands, Jira context, and PR review gates.
- Program Model
- 4 steps
- Process Maps
- 7
- Artifact Plan
- 59 rows
GenAI RAG Platform
Enterprise Knowledge Base
30+ min document hunts → sub-10s answers; hybrid RAG + semantic cache cut LLM spend ~60%.
- Documents
- 2M+
- Retrieval
- <1s
- LLM Cost Red.
- 60%
Discovery & Loyalty Platform
Qraved Indonesia · Imaginato
PHP monolith with static feeds → serverless discovery on Lambda, DynamoDB, and GraphQL; 3x throughput, +18% CTR and +22% DAU via Amazon Personalize.
- Throughput
- 3x
- CTR
- +18%
- DAU
- +22%
Questions this service should answer before a call.
Can you review an existing RAG or Bedrock implementation?
Yes. I review retrieval, prompts, evals, source attribution, latency, cost per request, logging, and fallback behavior.
Do you only work with AWS Bedrock?
AWS Bedrock is the strongest fit for this site, but the review can cover model-agnostic production concerns such as retrieval, evaluation, observability, and cost.