Generic models are expensive, slow, and wrong in the ways that matter to you. We fine-tune and deploy LLMs built on your domain, your docs, and your use cases — measurably better on accuracy, latency, and cost per query.
Domain accuracy · base vs. fine-tuned
tuned on your data
typical domain-accuracy lift
quantized, low-cost inference
private deployment option
of examples can be enough
What custom LLM engineering includes
Four pillars that turn a base model into something cheaper, faster and more accurate on your actual task.
Instruction tuning, LoRA/QLoRA adapters and preference alignment so the model behaves the way your domain demands.
Quantization, batching and right-sized serving that cut latency and cost per 1k tokens — without surrendering accuracy.
A benchmark built on your data so 'better' is a measured number, with regression tests that catch quality drops.
Open-weight models served inside your environment — full ownership, no data leaving your boundary, no per-token lock-in.
How we tune
We never claim a model is 'better' without a number next to it. Here's the path from base model to a tuned one you can trust.
We define the task, gather examples, and measure how the best base model already does — so every later gain is provable.
Fine-tune on your data, then quantize and optimise serving for the latency and cost target you set.
Run the harness: accuracy, latency and cost, head-to-head with the base model and human review on edge cases.
Ship behind a secure inference API with monitoring, versioning and a retraining path as your data evolves.
A model we tuned, in production
Solvent's whole product depended on one thing — reading raw, messy transactions and categorising them correctly. A generic model wasn't close. A domain-tuned one hit 92% on real data, and became the foundation of the app.
Solvent
Series A consumer FinTech · USA
Categorisation accuracy
Concept → live app
Permanent ML hires
Runway preserved
“We pitched the board a vision and a deadline. pyronix handed us a working app two days early. The pod cost us a fraction of the two seniors we almost hired — and we'd have still been onboarding them.”
Straight answers
No. We use quantization (4-bit / 8-bit) and right-sized architectures so a tuned model runs efficiently on consumer-grade or older enterprise hardware — often at a fraction of the cost of calling a frontier API at scale.
Prompting is cheapest and fastest; fine-tuning wins when you need consistent behaviour, format or a specialised skill; RAG wins when answers must reflect current, private knowledge. We benchmark the options against your task and usually combine them rather than betting on one.
We build an evaluation harness on your domain — automated benchmarks plus human-in-the-loop scoring — and report accuracy against the base model before and after tuning. You see the lift in numbers, not adjectives.
Less than most teams expect. With transfer learning, instruction tuning and synthetic data augmentation we can get meaningful gains from hundreds to low thousands of good examples. We assess what you have in week one.
Yes. We can deploy open-weight models entirely inside your cloud or on-prem, so prompts and outputs never leave your boundary — important for regulated and sensitive workloads.
Send us your task and a sample of your data. We'll benchmark a base model and show you the lift a tuned one would deliver.
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