Forward Deployed AI Engineering
The AI idea is approved, but someone still has to make it work inside messy data, auth, security, AWS, product, and user constraints.
Book a production reviewBest fit
Founders, CTOs, product teams, and platform teams that need a senior builder embedded close to the problem: discovery, architecture, implementation, integration, rollout, adoption, and production hardening.
Timeline
2-8 weeks
Proof frame
NDA-safe 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
- AI demos are moving faster than production readiness
- Requirements are ambiguous and nobody owns the technical path end to end
- RAG, agents, auth, data access, and legacy integrations are colliding
- The team needs reusable patterns, not one-off prototype code
- AI adoption needs developer workflow, governance, observability, and handoff
How I find the cause
- business workflow, user journey, success metric, and failure mode
- data access, permissions, identity, security, and compliance constraints
- RAG, Bedrock/model routing, agents, evals, guardrails, and fallback behavior
- AWS architecture, IaC, CI/CD, observability, cost per request, and rollback
- developer adoption, documentation, reusable scaffolds, and operating handoff
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.
I will not treat this like a lab prototype. Forward-deployed AI work has to survive users, permissions, cost, rollout, and the team that inherits it.
What you get
- - discovery-to-delivery technical plan
- - production architecture and integration map
- - working critical path or implementation sprint
- - eval, observability, governance, and rollback baseline
- - reusable playbook your team can own after handoff
What to bring
- - product goal
- - current stack
- - data/API constraints
- - what is failing now
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%
E-Commerce Serverless
KFC Thailand · Imaginato
Peak-hour monolith failures + inflated EC2 → 5M+ orders/mo, P99 <200ms, ~30% infra reduction.
- Orders/Mo
- 5M+
- Latency
- <200ms
- Cost Red.
- 30%
Questions this service should answer before a call.
How is this different from AI consulting?
The work is closer to implementation than advisory. I help discover the real workflow, design the system, build the critical path, wire it into AWS/product constraints, and leave reusable patterns behind.
Is this only for GenAI agents?
No. The strongest fit is production GenAI on AWS: Bedrock, RAG, agents, evaluation, governance, observability, and integration with existing systems.
Can this support internal AI-driven development?
Yes. The ASTM AI-driven development work is a good example: developer standards, Claude Code workflows, application knowledge bases, Jira/Confluence context, review gates, and team adoption.