01
Private & fine-tuned LLMs
Self-hosted or dedicated-endpoint models fine-tuned on your domain, kept inside your security perimeter.
Artificial intelligence
LLMs and agents that do real work.
Private and fine-tuned LLMs, retrieval-augmented generation grounded in your data, and agentic systems that execute multi-step business processes — with human oversight built in.
What we build
02
RAG systems
Retrieval grounded in your documents, warehouses, and live APIs — with citations your auditors can follow.
03
AI agents
Multi-step agents for operations, support, and back-office workflows with approval gates and full logging.
04
Copilot integrations
Assistants embedded in Microsoft 365, Slack, and your line-of-business systems.
How we deliver
Step 01 — Assess
Use-case discovery, data and platform readiness, and a business case with measurable outcomes for LLMs and agents.
Step 02 — Build
Senior-led delivery in weekly increments — architecture, security, and quality gates baked into every sprint.
Step 03 — Operate
Production monitoring, SLAs, and continuous improvement through our managed services team in Chennai.
Tools & platforms
ClaudeOpenAILlamaLangChainLlamaIndexPineconeWeaviateMilvusvLLM
In depth
Private LLMs, RAG, and agents: an architecture, not a feature
Large language models become enterprise software when three layers work together: a model layer you control, a retrieval layer that grounds answers in your knowledge, and an orchestration layer that turns single responses into multi-step work. We design and build all three — and the evaluation harness that proves they behave.
For regulated clients we deploy private and fine-tuned models inside your cloud tenancy, so prompts and documents never leave your perimeter. Retrieval-augmented generation connects those models to contracts, policies, clinical notes, and tickets with permission-aware indexing — the same access rules your users already have. Agentic workflows then execute processes end to end: triaging requests, drafting responses, updating systems of record, always with human checkpoints where the risk demands them.
This is the architecture behind our work in document intelligence and enterprise copilots, and it pairs naturally with generative AI development and AI consulting engagements when you need strategy and build together.
Your perimeter, your models
Self-hosted and fine-tuned LLMs in your tenancy for data residency and IP protection.
Permission-aware RAG
Retrieval that respects your existing access control — users only see what they could already see.
Agents with checkpoints
Multi-step automation with human approval gates where stakes are high.
Frequently asked questions
Have a different question? Talk to an engineer, not a salesperson.
When is fine-tuning worth it over RAG?
RAG wins when knowledge changes often; fine-tuning wins for tone, format, and domain reasoning. Most enterprise systems we ship combine both — the assessment tells you the split.
How do agents stay safe?
Scoped tools, approval gates on irreversible actions, full audit logs, and evaluation suites that replay real scenarios before every release.
What does self-hosting an LLM require?
A GPU footprint sized to your latency and volume — often a single node for internal workloads. We handle serving, quantization, and monitoring.