The shippable agent-month is the work. What that means.
Find out whether your AI workload is worth optimizing.
We help engineering teams ship at agent-scale — production agent infrastructure built by engineers who’ve shipped it. We start where it pays for itself fastest: your LLM bill.
Or start from:AI coding with no measurable impact →AI-built app not launch-ready →AI feature breaks on change →
- Week 1
- read-only baseline
- 4–6 wk
- to a measured result
- Outcome
- priced — we win when you save
- Vendor
- agnostic, model-agnostic
Start from the problem you have
Each of these has a page that helps you judge whether it is worth fixing — and says when we would advise against buying anything.
Common starting points
Most teams that find us have already built something. These are the per-tool playbooks for the most common case: an AI-generated prototype that needs to ship to production.
From Lovable to production
A prompt-to-app builder that ships a working Next.js + Supabase prototype in a weekend.
7 production gaps documented
From Bolt.new to production
A prompt-to-fullstack builder that ships a working React + Node (or Next.js) prototype in a weekend.
7 production gaps documented
From v0 by Vercel to production
A Vercel-owned prompt-to-React / Next.js component generator, often used inside an existing Next.js app to scaffold UI quickly.
7 production gaps documented
From Replit Agent to production
A cloud-based IDE with an AI agent that can build, run, and deploy a full-stack app from a prompt — production-deployable directly from the IDE.
7 production gaps documented
LLM Cost & Performance Optimization
Production LLM spend grows faster than anyone can see inside it. We audit your prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and the observability you’re missing — powered by the same engine as our open-sourcefast-litellm.
- A target set after a read-only baseline week, measured at equal or better quality
- Model routing that keeps quality on the prompts that matter
- Caching, batching, and fallback chains tuned to your traffic
- Cost + latency observability wired into your dashboards
Engagement at a glance
- Outcome
- A measured cost reduction against a recorded baseline, quality held
- Timeline
- 4–6 weeks
- Pricing
- $15–40k, or outcome-priced
- Buyer
- CTO, VP Eng, Head of Infra
Outcome pricing means we only get paid when you measurably save — and it usually lets you start without a procurement cycle. The exact terms are on the engagement call.
What we build
Every engagement ships working software, not a slide deck. Start small and measurable; grow into the platform.
New — taking AI-generated prototypes to production
AI-Generated Code: From Demo to Production
You built the prototype in a weekend with Lovable, Bolt, v0, or Replit Agent. We take it to production — auth, data, APIs, observability, security, and compliance. Same engineers we use for the LLM cost work + the AI-coding rollout.
LLM Cost & Performance Optimization
Production LLM spend usually grows faster than anyone’s ability to see inside it. We baseline it per route, audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
A measured cost reduction against a recorded baseline, quality held
Agentic Codebase Readiness Audit
We map your codebase against what actually makes it AI-coding-ready — module boundaries, test coverage, type strictness, docs, CLAUDE.md / rules files, and MCP potential — and quantify how far off you are.
A scored report + prioritized remediation roadmap
Internal AI Coding Workflow Build-Out
We standardize how your team uses Claude Code / Cursor / Copilot — custom slash commands, agent definitions, MCP servers for your internal tools, golden-path templates, and code-review hooks.
A working internal AI dev platform your team adopts
AI-Generated Code: From Demo to Production
You built the prototype in a weekend with Lovable, Bolt, v0, or Replit Agent. We take it to production — auth (SSO + RBAC), data (RLS + migrations + backups), APIs (versioning + rate limits), observability (cost + latency + errors), and the security + compliance the demo doesn't have. Same engineers we use for the AI-coding rollout and LLM cost work.
A documented, production-grade codebase your team owns
The proof is open source
We don’t deploy slideware. The infrastructure we ship for clients is the same infrastructure we build, in the open, under our own name.
Rust acceleration for LiteLLM — faster connection pooling, rate limiting, and memory-intensive workloads.
→ powers our LLM cost & performance optimization
LLM routing with automatic prompt optimization — sending each request to the model that fits.
→ powers our LLM cost & performance optimization
A multi-agent harness for AI coding tools — crash-safe state, parallel execution, one CLI.
→ powers our Agentic development platform builds
Gives AI agents database access without the risk — scoped, governed access for agents.
→ powers our MCP server builds & safe internal access
A workspace runtime and execution boundary for AI agents — guardrails on what an agent can touch.
→ powers our Agentic dev platforms & AI code security
An OS-like architecture for AI assistants — a kernel-based design with process-isolated apps.
→ powers our Agent infrastructure & observability
How we work
No discovery theater. We get to numbers fast, ship against a fixed scope, and leave you owning the result.
- 1Step 01
Technical call
A 30-minute call with an engineer, not a salesperson. We scope the real problem and tell you straight whether we can move the number.
- 2Step 02
Read-only audit
We instrument your traffic, prompts, and routing — or your repo — and quantify the gap. You get numbers before you commit to a build.
- 3Step 03
Fixed-scope ship
A defined engagement with a measurable target. We implement cost-aware routing, caching, evals, or tooling — and document the result.
- 4Step 04
Handoff & transition
We leave you with code you own, runbooks, and dashboards. You should hire in-house eventually — we help you get there, then transition out.
Free resources
The same work we ship for clients, written down so a senior engineer can run it. Buyer-fit vendor comparisons and playbooks for the buying motion.
Productionize AI code
All tools →Vendor comparisons
All comparisons →LLM cost optimization firms compared (2026): which fits your situation
9 options, by buyer fit
MCP server development companies compared (2026): which fits your situation
5 options, by buyer fit
AI coding tool consulting firms compared (2026): which fits your situation
4 options, by buyer fit
Self-hosted LLM consulting firms compared (2026): which fits your situation
4 options, by buyer fit
Playbooks (long-form how-to)
All playbooks →The CTO's first 90 days with an AI mandate
For: CTO
How to build a production MCP server
For: Head of Platform
LLM cost optimization playbook: 7 levers, in order
For: VP Engineering
The AI coding golden path: a 6–10 week rollout
For: Head of DevEx
Reference architectures
All →Reference architecture: a 6-week LLM cost engagement
5-phase plan, 15 deliverables
Reference architecture: a 6-week AI coding workflow rollout
4-phase plan, 15 deliverables
Reference architecture: a 4-week MCP server build
4-phase plan, 12 deliverables
Common questions
What exactly do you do?
We build and optimize production AI infrastructure for engineering teams: cutting LLM costs, adding evals and observability, building MCP servers and internal AI-coding workflows, and making codebases ready for agentic development. Every engagement ships working software, not a slide deck.
How is the LLM cost optimization priced?
Two ways. A fixed engagement of $15–40k, or outcome-based pricing: 25% of the first-year savings we measure against a baseline agreed before work starts, at equal or better quality. Savings from provider price cuts or lower traffic do not count, and the measurement rules are written into the agreement up front. Fixed pricing suits teams that want certainty; outcome pricing puts more of the risk on us.
How fast do you deliver results?
The cost optimization engagement is 4–6 weeks; we set its target with you after a read-only baseline week and report the result against that baseline. A codebase readiness audit is 2–3 weeks. Larger platform builds run 6–10 weeks; migrations run 3–6 months.
Why hire you instead of building it in-house?
You should hire — eventually. We help you ship now and hire later, then transition out. We move faster because this is the only thing we build, and our open-source work (brat, harmony-protocol, fast-litellm) is the same infrastructure we deploy for clients.
Find the money you’re losing on AI.
Most teams can’t say which routes drive their LLM bill. Tell us about the workload and we’ll tell you, straight, whether it is worth optimizing — and whether we are the right people to do it.