Private assistants
Chat and retrieval assistants served inside your network with vLLM or llama.cpp, so prompts and documents never leave your infrastructure.
AI platforms
LLaMA development.
LLaMA is a family of open-weight models you can run on your own hardware or private cloud. We choose it when data cannot leave your environment, when you want to fine tune on your own material, or when you need control over how inference runs.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with LLaMA
Where it fits.
LoRA fine-tunes
Task-specific adapters trained with LoRA or QLoRA on your labelled examples, producing a smaller model that follows your format reliably.
Quantised deployments
Models quantised to fit available GPUs or even CPU-only machines, with quality checked against the full-precision version.
Batch processing jobs
Overnight classification and summarisation runs over large archives on hardware you already own.
When it is the right choice
Choose LLaMA when
- →Privacy or residency rules rule out sending data to a hosted API.
- →You have a narrow, repeatable task that a tuned smaller model can do.
- →You want predictable infrastructure rather than per-call pricing.
Worth knowing
Honest trade-offs.
- →You take on serving, monitoring and upgrades, so there is real operational work compared with a hosted API.
- →Out of the box, open models can trail the largest hosted ones on hard reasoning, so we measure before choosing.
Often paired with
PyTorchvLLMHugging FaceKubernetesDockerPostgreSQL
Related services
Services that use it.
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
Do we need our own GPUs?
For decent speed, usually yes, either owned or rented from a cloud provider. Smaller or quantised models can run on modest hardware for low-volume internal use.
Is the licence a problem for commercial use?
Open-weight models come with licence terms that differ by release. We read the terms for the specific model before recommending it for your use case.
When is fine tuning worth it?
When prompting and retrieval cannot get you consistent output on a narrow task and you have good labelled examples. Otherwise we start with prompting and retrieval, which is cheaper to maintain.
What do you build with LLaMA?
Private deployment, Fine tuning, Cost-controlled inference, On-premise AI, and related work. We choose LLaMA when it is the right fit for the product, the team and the budget.
Can you work with our existing LLaMA code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than LLaMA.
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.