Custom LLM Fine-Tuning
Domain-specific LLM training with LoRA, QLoRA, and full fine-tuning on your data.
- LoRA + QLoRA training
- Domain data preparation
- Evaluation harnesses
- Model cards + docs
LLM Development
LLM development services for AI products that work in production
Custom LLMs, RAG pipelines, AI agents, fine-tuned models, and AI copilots — designed for measurable ROI, not demos.
- Production-grade RAG pipelines
- Evaluation harnesses + guardrails
- On-prem / VPC deployment ready
- Cost + latency optimised
Capabilities
End-to-end LLM engineering
RAG Pipelines & Vector Search
Retrieval-augmented generation with vector DBs, chunking strategies, and grounded answers.
- pgvector, Pinecone, Weaviate
- Chunking + embedding pipelines
- Hybrid search
- Citation + grounding
AI Agents & Copilots
Tool-using LLM agents that complete multi-step workflows with human-in-the-loop controls.
- LangGraph + CrewAI
- Tool + function calling
- Memory + context management
- Audit trails
Safety, Eval & Guardrails
Continuous evaluation, hallucination detection, and content safety layers for production LLMs.
- Eval harnesses
- Hallucination metrics
- Content safety filters
- Cost + latency monitoring
Delivery process
How we work
- 01Step 1
Use-case scoping
What problem; what eval metric.
- 02Step 2
Data + prompt engineering
Quality data, prompt templates.
- 03Step 3
Pilot model
Fine-tune, evaluate, iterate.
- 04Step 4
Productionise
Deploy with guardrails + monitoring.
FAQs
Frequently asked questions
Do we need to fine-tune or just use GPT-4?
Most use-cases work with prompt + RAG using a strong base model. Fine-tuning is needed for domain-specific tone, format, or to reduce inference cost at scale.
Can you deploy LLMs in our VPC / on-prem?
Yes — we support OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, plus open-source Llama, Mixtral, and Qwen models deployed in your environment.
How do you handle hallucinations?
Grounded RAG with citations, evaluation harnesses, and guardrail filters that block ungrounded answers in regulated contexts.
Related services
Related services
Need a tailored proposal for your business?
Tell us your goal, budget, and timeline — we'll respond within one business day with a clear next step.
Project Planning Support
Talk to our LLM Development team about your next build
This page is designed for companies building AI assistants, internal knowledge tools, and language-powered workflows. Our role is not just delivering llm development work but helping you make smarter decisions about scope, execution, and long-term growth before the project becomes expensive or hard to change.
We usually help clients by focusing on launching more useful and controlled LLM experiences that fit real business use cases. That includes use-case discovery, RAG architecture, guardrails, interface planning, testing, and rollout support, along with practical guidance on priorities, timelines, and what should happen first.
What we can help you clarify
- Ground answers in your own content and internal knowledge
- Reduce hallucination and workflow risk with better design
- Move from experimentation to production-ready delivery
Need adjacent services too?
Many projects need more than one specialist area. These related services often work together in the same roadmap.
Request a proposal for LLM Development
Share your goals, current setup, budget range, and timeline. We will review the details and come back with a more practical next-step recommendation.
Tell us about your llm development requirement
The more context you share, the better we can guide scope, priorities, and delivery options.