01
Scope and fit
We decide where Llama earns its place in your system, and where a simpler tool wins. No resume-driven architecture.
★ LlamaLLM Models8-week engagement
META LLAMA · OPEN-WEIGHTLlama when the weights must be yours.
Meta's open-weight Llama models run on your own infrastructure. We deploy and tune them when data residency, cost at scale, or control demand it.
Frontier LLM providersLLM APIEval pipelines
Cycle
8 weeks · fixed price
Stack
Llama, self-hosted
Output
Production code + eval suite
Handoff
Full source ownership
[THE SHORT VERSION]
Control and privacy, at the cost of running it yourself.
Open-weight models like Llama let you keep data in-house, avoid per-token vendor pricing at scale, and customize freely. The trade is that you now operate inference: GPUs, serving, scaling, and updates. We help decide if that trade pays off, then run it properly.
When it fits
- Strict data residency or privacy requirements
- High-volume workloads where hosted per-token cost hurts
- Customization or fine-tuning on your own data
When it does not
- Low-volume needs better served by a hosted API
[HOW WE BUILD IT]
How we build with Llama.
02
Build on a tested foundation
We integrate Llama against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface.
03
Eval before launch
An eval suite proves the build behaves before it reaches a user. We measure, then ship.
04
Handoff with ownership
Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework.
[WHAT YOU GET]
What the engagement leaves behind.
8 wks
Problem to production
100%
Source ownership at handoff
Eval-first
Tested before it ships
0
Framework lock-in
[RELATED]
Worth a look next.
APPLIED K-FRAMEWORKBring the problem.
Bring the problem.
We’ll bring the build.
Eight weeks, fixed price, eval suite at handoff. Senior engineers, full source ownership, no framework lock-in.