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Diagnose the production risk. Then execute the right path.

Start with a paid, bounded reliability audit. Deeper productionization, platform rescue, and ongoing architecture ownership follow once the evidence and highest-risk path are clear.

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What you can buy

Four ways to put production judgment to work.

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Production GenAI & AWS Reliability Audit

From $2,000 · one bounded system · report in 10 business days

AWS-based SaaS and enterprise teams approaching launch or carrying material production risk.

A paid, fixed-scope audit of one production system or mature AI workflow: reliability, IAM, data boundaries, observability, cost, deployment, and rollback.

What's included

  • Current-state risk register with severity and evidence
  • Reliability, IAM, observability, cost, deploy, and rollback review
  • Defensible target architecture and prioritized remediation plan
  • Written findings your engineering team can execute

Includes a focused AWS Cost & Reliability Audit when the bill is the symptom.

Request the auditHow it works

AI Agent & RAG Productionization

Paid engagement · fixed scope

Teams whose agent or RAG workflow works in a demo but cannot yet pass production or security review.

Move a mature AI prototype into controlled production with narrower tool permissions, stronger retrieval evidence, evals, fallbacks, human approval, and workflow-level cost controls.

What's included

  • Agent, retrieval, state, and tool-execution architecture
  • Permission boundaries and human-approval controls
  • Evaluation, fallback, and release strategy
  • Prompt-to-business-outcome observability and unit economics
Discuss productionizationHow it works

AWS Platform Rescue

Paid engagement · scoped phase

Teams facing repeated incidents, unclear ownership, unsafe deployments, or a blocked migration.

Stabilize an expensive, fragile, undocumented, or partially abandoned AWS platform without beginning with a risky rebuild.

What's included

  • Architecture discovery and dependency mapping
  • Immediate stabilization and rollback priorities
  • Ownership, observability, security, and cost controls
  • A focused implementation phase inside your AWS environment
Discuss a platform rescueHow it works

Fractional Cloud & AI Architecture

Monthly retainer · scoped hours

Teams that need recurring architecture decisions, standards, and an escalation point.

Principal-level AWS and GenAI ownership without waiting for a full-time hire or bringing in a large consultancy.

What's included

  • Recurring architecture reviews on your cadence
  • Clear decision records, ownership boundaries, and standards
  • Reliability, security, evaluation, and cost guardrails
  • A principal architect your team can escalate to
Discuss fractional ownershipHow it works
Capabilities

The technical depth behind each engagement.

Every offer above draws on these capability areas. Each links to a deeper page with symptoms, checks, deliverables, and what I won't do.

012-8 weeksNDA-safe

Forward Deployed AI Engineering

Pain this fixesThe AI idea is approved, but someone still has to make it work inside messy data, auth, security, AWS, product, and user constraints.

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.

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.

Open service detail

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

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
AI adoptionproduction readinessdelivery speedteam ownership

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Forward Deployed AI EngineerForward Deployed Engineer GenAIAI implementation engineerproduction AI engineer
021-2 weeksSanitized

AWS Production Architecture Review

Pain this fixesYour AWS setup works, but the bill, releases, permissions, and incidents are starting to feel hard to trust.

Series A-C SaaS and product teams that built fast and now need cost, reliability, IAM, observability, and deployment risk reviewed.

I will not turn this into a generic cloud maturity deck. The output must name real risks, owners, and first moves.

Open service detail

What you may be seeing

  • - AWS spend is rising without a clear owner
  • - Incidents require too much manual diagnosis
  • - IAM, accounts, and environments have drifted
  • - Rollback is vague or untested

How I find the cause

  • - account and environment structure
  • - IAM boundaries and network exposure
  • - CI/CD, rollback, and release ownership
  • - CloudWatch, X-Ray, logs, alarms, and cost hotspots
  • - scaling limits and failure paths

What you get

  • - architecture risk report
  • - top 10 production risks
  • - quick wins
  • - 30/60/90-day technical plan
  • - cost and reliability action list

What to bring

  • - architecture sketch
  • - billing context
  • - incident history
cost visibilityrelease confidenceoperational clarity

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production AWS architectAWS platform architectAWS architecture reviewAWS reliability consultant
032-6 weeksSanitized

GenAI / RAG Production Readiness

Pain this fixesThe AI feature looked good in a demo, but real users expose slow answers, wrong answers, and unclear costs.

Teams moving AI features from demo to production.

The model is rarely the whole problem. I will not hide retrieval, eval, or failure handling behind prompt changes.

Open service detail

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

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
answer trustlatency controlLLM cost discipline

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GenAI platform architectAWS Bedrock consultantproduction RAG consultantRAG production readiness
041-3 weeksSanitized

AWS Cost Optimization and FinOps Review

Pain this fixesThe AWS or LLM bill is climbing faster than confidence, and nobody knows which usage is worth keeping.

Teams with real traffic, multiple AWS services, and enough spend that cost mistakes now affect roadmap decisions.

I will not promise blanket savings without billing and utilization data. The useful answer is what to cut, what to keep, and what to measure next.

Open service detail

What you may be seeing

  • - Bill spikes are discovered after finance asks
  • - Idle or oversized resources have unclear owners
  • - S3, logs, data transfer, NAT, or model calls grow silently
  • - Savings Plans or commitments feel risky because workload shape is unclear

How I find the cause

  • - Cost Explorer, CUR, tags, accounts, and workload ownership
  • - compute, database, storage, logging, NAT, and data-transfer hotspots
  • - serverless and LLM/token cost per business action
  • - commitment risk, lifecycle policies, and unit economics

What you get

  • - cost driver map
  • - quick-win reduction list
  • - commitment and right-sizing recommendation
  • - LLM/token cost guardrails where relevant
  • - FinOps operating cadence

What to bring

  • - billing access/export
  • - service ownership map
  • - traffic or usage history
cost ownershipunit economicsbudget confidence

Search intent this page owns

AWS cost optimization consultantAWS FinOps consultantreduce AWS billLLM cost optimization
054-10 weeksNDA-safe

Serverless and Event-Driven Platform Build

Pain this fixesYou need the system to handle real traffic without hiring a large operations team or defaulting to Kubernetes too early.

Teams that need scale without carrying a heavy operations burden.

I will not suggest Kubernetes when Lambda, SQS, and Step Functions solve the problem with less operational load.

Open service detail

What you may be seeing

  • - Synchronous calls are creating cascading failures
  • - Workers, retries, and DLQs are missing or inconsistent
  • - Kubernetes is being considered by default
  • - Traffic spikes require manual babysitting

How I find the cause

  • - API Gateway, Lambda, SQS, EventBridge, Step Functions
  • - DynamoDB access patterns
  • - DLQ design, retries, idempotency, alarms, and IaC
  • - blast radius and fallback paths

What you get

  • - event flow and service boundaries
  • - IaC modules
  • - deployment and rollback runbook
  • - observability baseline
  • - operational handoff

What to bring

  • - traffic patterns
  • - critical workflows
  • - current deployment flow
failure isolationscale postureteam ownership

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serverless AWS consultantevent driven architecture consultantAWS Lambda consultantAWS platform architect
063-8 weeksSanitized

Platform Engineering and IaC Foundation

Pain this fixesYour team ships, but environments, infrastructure changes, and rollback depend on too much memory and manual work.

Teams with messy Terraform/CDK, environments, CI/CD, and deployment workflows.

Good platform work should reduce decisions for product teams, not create another system they are afraid to touch.

Open service detail

What you may be seeing

  • - Infrastructure is partly ClickOps and partly code
  • - Environment setup is slow or inconsistent
  • - Developers are unsure which platform path to use
  • - CI/CD gates are either absent or painful

How I find the cause

  • - Terraform/CDK structure
  • - GitHub Actions and environment strategy
  • - IAM guardrails, reusable modules, and deployment standards
  • - rollback process and observability baseline

What you get

  • - platform structure
  • - reusable IaC modules
  • - CI/CD standards
  • - rollback process
  • - team-facing documentation

What to bring

  • - repos
  • - current IaC
  • - deployment pain points
developer flowchange safetyplatform consistency

Search intent this page owns

AWS platform engineeringTerraform consultantAWS IaC consultantDevOps architect
How it starts

No sales deck. Start with the failure mode.

You describe what is hurting. I tell you what I would measure first, what I would pause, and whether the work is a fit.

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