Brooks said the man-month was a myth. The agent-month isn’t.
Stop overpaying for AI. Cut LLM costs 30–60% in four weeks.
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.
- 30–60%
- typical cost cut
- 4–6 wk
- to a documented result
- Outcome
- priced — we win when you save
- Vendor
- agnostic, model-agnostic
The 2026 engineering-leadership panic
Every one of these is keeping a VP of Engineering up at night. Every one is a fixable, measurable engagement.
- Production AI features you shipped a year ago are slow, expensive, and have no evals.
- Your LLM bill is climbing every month with no observability into what’s driving it.
- The CEO wants you to “add AI” and you’re not sure which workflows to standardize.
- Your codebase isn’t ready for agentic development — no specs, thin tests, fragmented context.
- Juniors are 2x faster with agents; seniors say quality is slipping. No standards exist.
- You’re evaluating Claude Code / Cursor / Copilot and need a golden path, not a free-for-all.
LLM Cost & Performance Optimization
Most teams running production AI pay 3–10x what they should. 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 documented 30–60% reduction, or you don’t pay the success fee
- 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
- 30–60% LLM cost reduction in 4 weeks, documented
- Timeline
- 4–6 weeks
- Pricing
- $15–40k, or outcome-priced (25% of first-year savings)
- Buyer
- CTO, VP Eng, Head of Infra
Outcome pricing means we keep 25% of your documented first-year savings — and it usually lets you start without a procurement cycle.
What we build
Every engagement ships working software, not a slide deck. Start small and measurable; grow into the platform.
LLM Cost & Performance Optimization
Most teams running production AI pay 3–10x what they should. We audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
30–60% LLM cost reduction in 4 weeks, documented
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
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.
- 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.
- 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.
- 03
Fixed-scope ship
A defined engagement with a measurable target. We implement cost-aware routing, caching, evals, or tooling — and document the result.
- 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.
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 where we keep 25% of your documented first-year savings. Outcome pricing means we only win when you measurably save — and it usually bypasses procurement.
How fast do you deliver results?
The cost optimization engagement is 4–6 weeks and targets a documented 30–60% reduction. 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 are bleeding $20–200k/month on LLM costs they can’t see. Book a 30-minute technical call and we’ll tell you, straight, whether we can move the number.