Packaged offer · 6–10 weeks
LLM Fine-Tuning Accelerator
Retrieval has plateaued and prompting has run out of room.
Deliverables
What you have at the end.
- Model selection
- Dataset creation
- SFT / LoRA / QLoRA
- Evaluation
- Serving and deployment
The capability behind it
LLM Fine-Tuning
SFT, LoRA and QLoRA to adapt models to your domain and formats.
Dataset engineering
The real work: curating, cleaning, deduplicating and labelling examples that represent the task as it occurs.
Synthetic data generation
Augmenting scarce examples with generated variants, filtered for quality and checked for leakage.
SFT, LoRA and QLoRA
Parameter-efficient adaptation chosen for your compute and serving constraints, with adapters kept separately versioned.
Instruction tuning
Teaching consistent response structure, refusal behaviour and tool-call formatting.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Fine-Tune a Domain Model
Data sovereignty. Enterprise RAG. AI agents. Fine-tuning. AI economics. Security. Legacy systems. Autonomous operations. Start with the problem — we'll help you engineer the intelligence.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT