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
- Clients served
- 425+
- Projects launched
- 855+
- Satisfaction
- 95%
- Reply window
- <24h
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
LLM products, not chatbot demos
Retrieval, evaluation, guardrails, and a path to production cost — we treat language models as software.
Internal knowledge assistants
Secure RAG over your docs, tickets, and policies with citations.
Customer-facing copilots
Support and sales agents with tools, logging, and human handoff.
Document-heavy operations
Contracts, KYC, medical, or legal extraction with review queues.
Why teams trust us
- Clients served
- 425+
- Projects launched
- 855+
- Satisfaction
- 95%
- Reply window
- <24h
What you can expect
A plan you can brief internally
Written scope, timeline, and owners — so stakeholders are not guessing what “phase 1” means.
Quality that survives launch week
Reviews, QA, and a support window after go-live. The work does not end at a demo.
Room to iterate
Analytics, feedback loops, and a backlog so v2 is cheaper than starting over.
Tools and platforms we use
- OpenAI
- Anthropic
- Azure OpenAI
- LangChain
- LlamaIndex
- pgvector
- LangSmith
- vLLM
How we work
A delivery cadence you can brief internally — discovery through launch, with visible checkpoints.
- 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.
Want this scoped to your stack and timeline?
Share goals, constraints, and budget band — we will reply within one business day with a practical next step.
A delivery partner, not a ticket queue
You get a named squad, a written plan, and a product that is still operable after handover.
Senior people on the work
Strategists and engineers who have shipped this category of work before — not a junior bench learning on your budget.
One accountable squad
Design, engineering, SEO, and growth sit in one team, so you are not coordinating three vendors for one outcome.
Visible weekly progress
Demos, written updates, and a shared backlog. You always know what shipped, what is next, and what is blocked.
Built to run after launch
Handover, documentation, monitoring, and a support path — so the product does not stall the week we go live.
Ways to work
Pick the engagement that matches how you buy
Fixed-scope project
Clear deliverables, milestone billing, and a locked timeline after discovery. Best when you know the outcome.
Dedicated squad
A standing product team on a monthly retainer. Best for roadmaps that will keep moving after v1.
Specialist augmentation
Plug senior designers or engineers into your existing team without hiring full-time.
Industries we support
Same delivery quality — domain language and compliance adapted to how you sell.
- SaaS
- Legal
- Healthcare
- Insurance
- Support orgs
- Fintech
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
Pair this service with the adjacent work most clients sequence next.
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.