Inspired By Frustration// Embedded delivery · production LLM systems
I join your team, work in your repo, and take LLM features from scoping through prototype, evaluation, hardening, and handoff — production systems on the Anthropic API and the Claude Code Agent SDK, not slideware.
Why teams hire us
Real, shipped production work behind every engagement — not advisory slideware or portfolio mockups.
100
MCP servers behind one gateway
69
tools under a confirm-before-mutate gate
8 min
hard CI cap across 12 production repos
In short
A forward-deployed AI engineer is a senior engineer who embeds inside a customer's team to ship AI systems in the customer's own codebase — scoping the use case, building the prototype, proving it with evaluation harnesses, hardening it for production, and handing it over with runbooks and docs.
The title comes from Palantir and is now how OpenAI and Anthropic ship AI into real organizations: not an advisor, not an agency — an engineer deployed forward, inside your team, accountable for the system working in your environment.
Most AI features die between the demo and production.
The demo takes a day; the retrieval that doesn't hallucinate, the tool calls that can't mutate what they shouldn't, the eval harness that catches regressions before users do — that's the actual work.
That layer is what I build, in your repo, to your standards, with your engineers in the loop so the knowledge stays when I leave.
Which AI feature is load-bearing and which is vanity. A written scope with a measurable bar before any code — so we build the one that moves your product.
Typed tool contracts, MCP servers, confirm-before-mutate gates for anything that writes. The pattern behind AppHandoff's 69-tool action layer and MCP Beast's governed gateway.
Grounded retrieval with citations, hybrid search (Postgres + pgvector + embeddings), and honest failure modes — the difference between a demo and an answer you can ship.
Graded, repeatable eval suites wired into CI, so a prompt or model change is measurable in minutes. Regressions get caught by the gate, not by your users.
Auth, rate limits, audit logging, cost ceilings, observability, incident runbooks. The same discipline that runs a self-hosted CI fleet across 12 production repos.
Your repo, your accounts, your team trained. Architecture decision records and runbooks are deliverables, not extras — the system survives my exit.
Forward-deployed engineers ship the product inside the customer's environment instead of throwing an API over the wall. The model works because the last 20% of an AI system — data access, permissions, evals, failure handling — is always specific to your stack. An embedded engineer closes that gap in weeks; a generic integration partner bills quarters for it.
I work in your repo on a branch like any senior engineer: standups if you run them, PRs your team reviews, demos every week. The difference is throughput — I bring my own agent tooling (Claude Code, Cursor, a coordination layer I built) so volume work parallelizes while the judgment calls stay human. You see working software weekly, not status decks.
Production LLM systems, built solo and verifiable from the repos: MCP Beast — an enterprise gateway routing 100 MCP servers behind one authenticated endpoint with RBAC and audit logging. AppHandoff — a plain-English action layer over 69 tools with a confirm-before-mutate gate. A browser-to-server speech-to-text pipeline on OpenAI Whisper with a full HTTP error taxonomy, unit-tested both ends. And the CI runner fleet that ships all of it, ~50–100 jobs a day under an 8-minute cap.
Hire the forward-deployed engineer when you have an engineering team and a concrete AI surface to ship — you need hands in the codebase. Hire the fractional CTO when you need the calls made — architecture, hiring, vendor and model strategy, governance. They're the two front doors of the same practice; engagements sometimes start as one and become the other.
Access, codebase walkthrough, stakeholder pass. Out: a written scope with one load-bearing use case and a measurable bar.
A working prototype in your environment — real data, real permissions — with an eval harness from day one, not bolted on after.
Observability, cost ceilings, failure modes, audit trail. Evals gate the merges so the system stays correct as it evolves.
Runbooks, decision records, team walkthrough. Scale me down to advisory or out entirely — no lock-in by design.
MCP Beast: server-side credential injection, RBAC, per-call audit logging, semantic tool discovery. Production on Fly.io.
AppHandoff: plain-English requests become confirmed, audited actions. The safety pattern your compliance team will ask about, already built.
The self-hosted runner fleet that ships everything above — fail-closed merge gate, drain-aware deploys, two-tier caching.
Bring the use case — retrieval, agents, tool use, an LLM feature stuck at 80%. I'll tell you what the embedded engagement would look like and what the first measurable bar should be.
The demo worked; production didn't come. An embedded engineer closes the retrieval, evals, and hardening gap inside your codebase.
Book a callTool contracts, MCP servers, governance, audit — designed once, correctly, with the patterns already running in production elsewhere.
MCP server developmentIf the calls are the bottleneck rather than the code, the fractional AI CTO seat is the other front door of this practice.
Fractional AI CTOA forward-deployed AI engineer embeds inside your team and ships the AI system in your codebase — scoping, prototype, evals, hardening, handoff — instead of advising from outside it.
The demo takes a day. Retrieval that doesn't hallucinate, tool calls that can't mutate what they shouldn't, and an eval harness wired into CI are the actual work.
Receipts, not claims: MCP Beast routes 100 MCP servers behind one governed gateway; AppHandoff turns plain English into confirmed, audited actions across 69 tools.
Embedded means your repo, your standards, your team trained — with runbooks and decision records as deliverables, so the system survives the engineer's exit.
Embedded, not outsourced
Your repo, your review standards, weekly demos. The knowledge transfers to your team instead of leaving with a vendor.
Production LLM systems as receipts
MCP Beast, AppHandoff, a Whisper speech-to-text pipeline, the CI fleet — running systems you can probe, not case-study PDFs.
Judgment plus throughput
Twelve years on the business side aim the work; my own agent tooling parallelizes the volume. Senior calls, junior-free delivery.
// what clients say
We came in with a Lovable prototype and a board deadline. Three weeks later we had a typed backend, real auth, and an MCP server our support agents actually trust. The POC went to production without the usual rewrite tax.
DaanHead of Engineering
fintech scale-upPOC → production
I needed someone who could orchestrate a swarm of coding agents and still own the architecture. The agent-orchestration setup shipped 40+ PRs in a week — every one reviewed, scoped, and reversible. No hallucinated mess to clean up.
M.R.Founder
B2B SaaSagent orchestration at scale
The MCP integration was the part three other vendors quoted us six months for. Here it was live in under three weeks — tool schema, OAuth, rate limits, traces, the lot. Our Claude agents finally touch real data safely.
PriyaVP Product
healthtech startupMCP integration
One senior engineer, embedded, accountable for the AI system working in your environment. Book a 20-minute call to scope the first measurable bar.
Get in touchA senior engineer who embeds inside your team and ships AI systems in your own codebase — scoping the use case, building the prototype, proving it with evals, hardening it for production, and handing it over with runbooks. The title comes from Palantir's forward-deployed engineer model, now used by OpenAI and Anthropic to ship AI into real organizations.
An agency ships from outside your environment and the knowledge leaves with them. A forward-deployed engineer works in your repo, through your review process, with your engineers in the loop — and the deliverables include the runbooks and decision records that make the system maintainable after handoff.
Anthropic API and Claude Code Agent SDK for the AI layer; MCP servers and typed tool contracts for integration; Next.js, TypeScript, Supabase, Postgres + pgvector, and Fly.io for the systems around it. If your stack differs, the patterns transfer — evals, tool governance, and observability are stack-agnostic.
The first measurable bar is usually 2–4 weeks: embed, scope, prototype against real data. Hardening and handoff typically bring the total to one to two quarters, scaling down to advisory as your team takes over. Month to month, no retainer minimum.
That's the ideal shape. Forward deployment works best when there's a team to embed with — your engineers review the PRs, absorb the eval and tool-use patterns, and own the system at handoff. If you have no engineers yet, the fractional AI CTO seat is usually the better front door.