DigiEdze AI · LLM Fine-Tuning
Make the Model Understand Your Domain
When retrieval has plateaued and prompting has run out of room, fine-tuning teaches a model your terminology, your formats and your task behaviour — at a fraction of the inference cost of a larger general model.
- SFT
- LoRA
- QLoRA
- PEFT
- instruction tuning
- domain adaptation
- synthetic datasets
Reference architecture
- 07Task definition & success criteria
- 06Dataset curation and labelling
- 05Base model selection
- 04PEFT training — LoRA / QLoRA / SFT
- 03Evaluation & regression gates
- 02Model registry
- 01Serving with adapter routing
The Challenge
What Happens When This Goes Wrong?
The model produces good prose in the wrong shape, and no amount of prompting fixes it reliably.
Your domain language is misread, because it never appeared in general pre-training.
A frontier model is being paid for on every call to do a narrow, repetitive task.
What we build
LLM Fine-Tuning Capability Grid
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.
Evaluation before promotion
Held-out benchmarks plus regression suites, so a tuned model is never promoted on a demo impression.
Serving and versioning
Adapter hot-swapping, rollback paths and model-registry discipline for every version that reaches production.
How it works
Technology
- Open-weight base models
- PEFT libraries
- GPU training clusters
- Experiment tracking
- Model registries
- Serving runtimes
We bring the genuine implementation capability. Where your estate dictates a different choice, the architecture accommodates it.
Use cases
Practical Approach Across Verticals
BFSI
A compliant summarisation model that produces the exact regulatory format every time.
Healthcare
Clinical coding assistance tuned to local terminology and documentation practice.
Telecom
A small model tuned for high-volume ticket classification at a fraction of frontier inference cost.
Outcomes
What We Measure
- Task accuracy against held-out sets
- Format compliance rate
- Inference cost per request
- Latency at target throughput
Questions
LLM Fine-Tuning, Answered Directly
How many examples does fine-tuning need?
For format and style adaptation with LoRA, several hundred high-quality examples often suffice. For genuine task capability, low thousands. Quality and coverage of edge cases matter far more than raw volume.
Should we fine-tune or use a bigger model?
Compare on total cost at your real volume. A tuned small model that serves a narrow task at a fraction of the per-call cost usually wins on economics and latency — provided you have the evaluation discipline to keep it honest.
Related services
Intelligence
Custom LLM & SLM Engineering
A routed model portfolio matched to task, risk and cost.
ExplorePlatform & Operations
AI Evaluation
Golden datasets, scorers and regression gates for AI change.
ExploreInfrastructure
GPU & AI Infrastructure
GPU clusters, scheduling, serving and utilisation economics.
ExploreChoose 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
Explore Fine-Tuning
Thirty minutes with an architect who has built this. Bring the constraint that has stopped you — data, security, cost, sovereignty, agents or legacy systems.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT