Production AI engineering that survives real users.
Agents, RAG pipelines, MCP servers, Claude Code workflows, model-stack systems, AI ops dashboards, evals, observability, and deployment for teams moving from demo to production.
Evals, observability, and handover built into every production engagement.
Field notes from building AI systems. Build logs, working blueprints, and what shipped — weekly.
AI demos are easy. Production AI systems are not. Most AI demos work once. Production AI systems need architecture, evals, logs, approvals, state, cost visibility, deployment, and maintenance.
Production AI systems need state, permissions, tool boundaries, evals, tracing, cost telemetry, human approval, failure recovery, deployment, and a dashboard your team can actually operate.
Core services
Audits, provider systems, and production builds — USD-priced, with evals and observability baked in.
AI Engineering Audit
A 5-day technical audit of your AI system, codebase, model stack, and production risks.
Model Stack Architecture
Choose the right model stack before you build the wrong AI system.
Production OpenAI Agent Systems
OpenAI agent systems designed for production workflows, not one-off demos.
Claude Code Team Rollout
Turn Claude Code from individual prompting into a controlled engineering workflow.
Custom MCP Server Development
Custom MCP servers that let AI systems safely access your tools, data, and workflows.
RAG Knowledge Pipeline
RAG knowledge systems that retrieve the right information, cite sources, respect permissions, and improve over time.
AI Ops Dashboard / Control Layer
The control layer for production AI systems.
AI-Native MVP
A technical AI-native MVP for founders who need the first serious product version.
Flagship
The control layer for production AI systems.
A custom dashboard for agent runs, RAG quality, model costs, tool calls, evals, failures, prompt versions, approvals, audit logs, and workflow visibility. The wedge most AI teams eventually need — and the one nobody else productizes.
Dashboard modules
- Agent run viewer
- Cost dashboard
- Tool-call history
- Prompt / model version tracking
- Eval regression tracking
- RAG retrieval quality
- Approval queues
- Failure alerts
- Audit logs
- Access control
- Executive summary view
The model stack layer
OpenAI, Claude, Gemini, and Grok — each routed to the workload it wins, with fallback logic and cost control.
OpenAI
OpenAI for agent apps, tools, structured outputs, tracing, handoffs, and multi-step workflows.
Claude
Claude for codebase work, Claude Code workflows, long-form reasoning, and repo-aware engineering.
Gemini
Gemini for the Google ecosystem, enterprise agent platforms, and document-heavy, governed workflows.
Grok
Grok for real-time, search-aware systems — web search, X search, code execution, RAG collections, and remote MCP tools.
The technical stack
Model providers, agent orchestration, MCP, RAG, observability, and the app stack we run for funded teams.
Model providers
OpenAI · Claude · Gemini · Grok
Agent orchestration
LangGraph · OpenAI Agents SDK · Vercel AI SDK · Custom orchestration
MCP
Model Context Protocol · Custom MCP servers · Tool registries · Private tools
RAG
Postgres / pgvector · Supabase · Pinecone · Weaviate · Hybrid search · Reranking · RAG
Observability
Langfuse · LangSmith · OpenAI tracing · Custom dashboards
App stack
Next.js · React · TypeScript · Python · Supabase · Postgres · Redis · Cloudflare · Vercel
How we engineer AI systems
Audit → Architecture → Evals → Integration → Observability → Control.
Audit
We review your codebase, model stack, agents, RAG, evals, and production risks — and write the roadmap.
Architecture
System design, model and provider choice, tool and MCP boundaries, and data flow — before any code.
Evals
Golden datasets and an eval harness so quality is measured, not assumed, before launch.
Integration
Build and integrate the agents, RAG, MCP, and workflows into your product and stack.
Observability
Tracing, cost telemetry, and logging so you can see what the system does in production.
Control
A dashboard and control layer — run history, approvals, audit logs — your team can operate.
Reference systems, not slideware
Internal reference systems and prototypes that show how we build — architecture, logging, evals, and the failure modes we design for.
AI Ops Dashboard
A control layer for agent runs, costs, evals, approvals, and failures.
RAG Pipeline
Ingestion to hybrid retrieval to reranking to cited answers.
Production Agent
A bounded LangGraph agent with explicit state, tools, and approvals.
Claude Code Workflow
Repo instructions, skills, MCP, and a GitHub Actions review loop.
MCP Server
A TypeScript MCP server exposing internal tools behind auth.
Model Routing System
Routes tasks across OpenAI, Claude, Gemini, and Grok with fallback and cost control.
Keep it running
Monthly retainers for teams with deployed AI — managed Claude Code, AI ops, an embedded pod, or a fractional head of AI.
Managed Claude Code
Keep your Claude Code setup running — new skills, MCP additions, onboarding, monthly review.
AI Ops Retainer
Production monitoring, evals, cost optimization, and incident response for deployed AI.
Embedded AI Engineering Pod
A senior AI engineer embedded in your team, shipping in your repo.
Fractional Head of AI Engineering
Senior leadership for your AI engineering — strategy, architecture, and team mentoring.
Writing Build logs, model-stack decisions, agent failures, and what survives real users.
Moving from demo to production? A 5-day AI Engineering Audit turns your codebase, model stack, and production risks into a 90-day roadmap with USD bands. It credits 100% into any follow-on build within 30 days.
$5K · Credits 100% into any build within 30 days