LLM Engineering & Evaluation
From fine-tuning to the harness that proves it works for your use case — the same loop behind our own research.
The problem
Most teams can prompt a model. Almost none can prove it keeps working on their own data, or catch it quietly failing when it doesn’t.
The outcome
A fine-tuned, grounded model with an evaluation harness that measures whether it actually works on your tasks — not a generic benchmark.
- Grounded in real evidence: our own research program built exactly this — fine-tuning specialist models on a shared backbone, then an evaluation and abstention harness — and cut silent errors from 36% to 4% on our internal benchmark.
- The same honesty standard applies to your evaluation harness as to our own research: what it catches, and what it still lets through, is reported plainly.
What's included.
- Use-case definition and success criteria, agreed up front.
- Data preparation for fine-tuning and grounding.
- Model selection and LoRA fine-tuning.
- Grounding and retrieval (RAG) over your own documents.
- An evaluation harness measuring silent-error rate, abstention, and coverage.
- Human-in-the-loop design, delivered on your hardware.
How this actually runs.
Use case & success criteria
A paid discovery sprint defines exactly what "working" means for your use case before anything is built.
Data preparation
Your own documents and examples, prepared for fine-tuning and grounding.
Model selection & fine-tuning
We select a base model and fine-tune it (LoRA) on your data and task — the same approach behind our own research program.
Grounding & RAG
Retrieval over your own documents, so every answer is grounded in something real, not memorised.
The evaluation harness
A harness built around your tasks, measuring silent-error rate, abstention, and coverage — the metrics we publish about our own research.
HITL design & handover
A human-in-the-loop review workflow, delivered on your hardware, backed by a warranty period and an AMC.
Built on.
- LoRA / PEFT fine-tuning
- GGUF / llama.cpp
- Conformal prediction & abstention
- DSPy
See the products this work is grounded in.
All products
CrestBid AI, Orchestrator Studio, and Vexilo — three local AI products built by the same team.
Questions worth asking.
Both — and the harness matters more. A fine-tuned model without an evaluation harness is a guess dressed up as a product; the harness is what tells you when it’s wrong.
Then we say so during use-case definition, before any data preparation starts — grounding and retrieval alone are sometimes enough, and we don’t fine-tune for its own sake.
Consulting decides whether this is worth doing at all. This builds and proves the model works on your data. Local AI Deployment gets the result running long-term on your hardware.
Ready to talk about llm engineering & evaluation?
From fine-tuning to the harness that proves it works for your use case — the same loop behind our own research.