LLM Fine-Tuning
Adapt foundation models to speak your domain’s language precisely.
LLM Fine-Tuning Services for Every Industry
From single-domain model adaptation to enterprise fine-tuning pipelines — custom AI models that work in production, not just in proof-of-concept demos.
Domain-Specific LLM Fine-Tuning
Adapt foundation models to your exact domain using supervised fine-tuning on your proprietary datasets — legal, medical, finance, or engineering, where generic outputs are not good enough.
- Proprietary dataset training
- Domain-accurate outputs
- Any vertical or industry
Instruction Fine-Tuning & Alignment
Train your model to follow your specific instructions, produce outputs in your required format, and refuse responses outside your defined scope — RLHF and RLAIF alignment included where quality demands it.
- Custom instruction following
- Output format control
- RLHF & RLAIF alignment
Brand Voice & Tone Fine-Tuning
Build a model that generates content in your exact brand style, terminology, and tone — without prompt engineering gymnastics. Consistent output across every touchpoint, every time.
- Brand-consistent outputs
- Tone & style control
- No prompt engineering needed
Code Generation Model Fine-Tuning
Fine-tune code generation models on your proprietary codebase, internal libraries, and coding conventions — developers get an AI pair programmer that already knows your stack.
- Codebase-aware model
- Internal library training
- Convention-matched output
Multilingual & Regional Language Fine-Tuning
Adapt models for regional languages, dialects, and industry-specific vocabulary that generic multilingual models handle poorly — Urdu, Arabic, and low-resource language fine-tuning available.
- Regional language support
- Dialect & vocabulary tuning
- Low-resource languages
Document Classification & Extraction Models
Train specialised models to extract, classify, and structure information from your specific document types — contracts, clinical notes, invoices, and regulatory filings — with accuracy general-purpose models cannot match.
- Contract & invoice parsing
- Clinical note extraction
- Regulatory filing structure
Real-World Applications
Built for Clients. Shipped to Production.
From autonomous document processors to intelligent enterprise platforms - here is what we have delivered.
AI Credit Underwriting Platform - Fintech SaaS
An SME lender deployed a six-stage AI agent pipeline - from document ingestion to explainable decisions. Analysts review flagged cases only. Fast decisions, consistent underwriting, and full FCA audit compliance.
View Case Study →Six-Stage Agent Pipeline
Explainable credit decisions
LLM Routing Platform - Cost, Quality & Latency Optimisation
Task-aware routing classifies requests, estimates complexity, and selects optimal models via LiteLLM. All decisions are logged, while a React dashboard provides visibility, control, and continuous A/B optimisation.
View Case Study →Intelligent LLM Routing
Optimised for every request
On-Premise LLM & RAG Platform - Government Enterprise AI
An on-premise LLM on NVIDIA DGX hardware with a secure RAG pipeline over internal data. Staff query in natural language with zero data leakage. Rollout is planned across 11+ departments.
View Case Study →Secure Enterprise RAG
On-premise government AI
From Use Case to Production
No black boxes. No surprises. Working agents in your hands, sprint by sprint.
Data Audit & Curation
Step 1
We assess your raw business data, clean and format it to training standards, and flag gaps before a single training run begins — garbage-in prevention is where model quality is won or lost.
Baseline Evaluation
Step 2
We benchmark the best available foundation model against your actual tasks — giving you an honest performance baseline and defining exactly what the fine-tuned model needs to beat.
Fine-Tuning & Alignment
Step 3
Supervised fine-tuning using LoRA, QLoRA, or full fine-tuning based on your model size, compute budget, and accuracy requirements — RLHF applied where outputs need to align with human quality judgements.
Domain Benchmarking & Human Evaluation
Step 4
The fine-tuned model is evaluated against domain-specific benchmarks and human reviewers from your team — not generic leaderboards, but performance on the exact tasks you need the model to do.
Deployment & Infrastructure Setup
Step 5
Your model is deployed on your cloud or private on-premises environment. No fine-tuned weights leave your control. Monitoring, versioning, and rollback are configured from day one. 100% model ownership transferred.
Contact Us
We typically respond within 24 hours.