Most businesses don't need a custom model. When your formats, tone or domain language really do need one, we fine-tune on your data and prove the gain first.
Documents that come out in your structure and voice every time.
Better handling of specialist terms and formats.
A smaller tuned model replacing a large general one for a repeat task.
Reliable extraction into your schemas.
Private and local first for critical and sensitive data. Partnered cloud models only when a task needs them, behind a policy gate, with audit logs and monitoring you can see.
What we use for fine-tuning, and what each piece is for.
A working AI product in your users' hands in seven days, built on our proven components.
No-obligation free MVP for startups. Scope agreed in the free consultation; you keep it either way.
Built your app with AI coding tools but it isn't secure or won't scale? We review it free and fix the critical security and scaling issues free.
Efficient tuning of open models on modest hardware
When a larger change in behaviour is justified
OpenAI or Gemini tuning where data may leave your environment
Models, datasets and training libraries
Training framework
Experiment tracking and model registry
De-identified examples from your real work
Extra examples where real ones are scarce
Approved by you, never trained on
Typical phases and timelines; your plan is agreed after the free consultation.
Measure RAG and prompting on your task first
1 weekCollect, clean and de-identify examples
1–3 weeksSide-by-side against the baseline
1–2 weeksServe privately or via provider, with monitoring
1–2 weeksFrom products we built and run, and engagements we measured.
Fine-tuned drafting models learn a firm's precedents and house style, alongside RAG for facts.
A fine-tuned transformer for survey sentiment turned weeks of manual coding into same-day analysis.
The technical detail, for your engineers.
Only after RAG and prompting are measured and a gap remains on accuracy, format or cost.
Curated, de-identified examples from your own work, with a held-out test set you approve.
Parameter-efficient tuning (LoRA/QLoRA) on open models, or provider fine-tuning where data may leave your environment.
Side-by-side evaluation against the untuned baseline on your real tasks before deployment.
No. We fine-tune only when RAG and prompting leave a measurable gap.
You do, including weights for open models.
Often hundreds to a few thousand good examples; we tell you after looking at your data.
Yes, with open models on your hardware.
Typically three to six weeks including data preparation and evaluation.
Often less than a large general model, because a smaller tuned model does the job; we model it first.
It can. We plan refresh cycles and monitor quality over time.
Yes, with suitable open models and enough examples.
No. We use private models or enterprise agreements that forbid training on your data.
Book the free two-hour consultation. We look at one real workflow and tell you whether this technology fits.
Yes. Engagements we take on carry our 10× productivity guarantee on the agreed workflow, or the fee comes back.
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assets/img/projects/lexedge.pngOne AI workspace for legal drafting, research and citation checks
240+ practices using it
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assets/img/projects/hr-copilot.pngOne copilot for resume screening, candidate outreach and survey analysis
~50% faster time-to-hire
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assets/img/projects/sukhan-ai.pngRead, understand and turn Urdu-Hindi poetry into songs
30+ legendary poetsTwo free hours on a real workflow. We'll tell you whether it fits, and what it would take.
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