Knowledge assistants (RAG)
Chat assistants that answer from your documents, policies and SOPs, cite their sources, respect user permissions, and say "I don't know" instead of guessing.
AI & LLM development
AI development company building production-ready LLM solutions
We design and build generative AI systems — knowledge assistants, document intelligence, customer support agents and AI features inside your apps — for businesses in India and worldwide. Engineered like any production system: tested, secure, and with predictable running costs.
- Based in Greater Noida, Delhi NCR
- Working with clients across India & worldwide
- Reply within one business day
What we offer
AI solutions we build
Every AI project starts with a clear business problem and a way to measure success — not a model looking for a use case.
Document intelligence
Turn invoices, forms, contracts and scanned documents into structured data in your ERP or accounting system, with a review queue for low-confidence fields.
Customer support agents
Website and WhatsApp agents that answer questions, check order status through your APIs, and hand off to a human with the full conversation.
AI features in your app
Semantic search, summaries, smart replies and recommendations added to an existing mobile or web product, streamed in real time.
Workflow automation agents
Agents that prepare reports, update records and draft follow-ups across your systems, pausing for human approval where it matters.
AI strategy & readiness
A short engagement to find use cases with real ROI, prototype the riskiest one, and give you accuracy, cost and privacy numbers to decide on.
Why Lachhan Software
Why businesses choose us for AI development
Engineering-led delivery from a team founded by an engineer with 16+ years of production experience — not an agency that hands your project down a chain.
Talk to an engineer- Production engineering first16+ years of shipping production software, including systems at Amazon scale. AI features get the same reliability, monitoring and security as the rest of your stack.
- Measured accuracyEvery AI feature ships with an evaluation set built from your real data, so quality is tracked from release to release instead of judged by demos.
- Your data stays protectedDeployment through AWS Bedrock or enterprise model APIs that don't train on your data by default, with role-based access and data kept in your chosen region.
- Predictable running costsModel selection, caching and usage limits are designed up front, so you know what each conversation or document will cost before you scale.
How we deliver
From first call to launch
-
1
Use-case discovery
Agree the problem, the users, the data available, and what "good enough" accuracy means for your business.
-
2
Prototype on your data
A working prototype on a sample of your real documents or workflows, with first accuracy and cost numbers.
-
3
Production build
Integrations, guardrails, permissions, evaluation suite, monitoring and an interface your team will actually use.
-
4
Launch & improve
Roll out to users, track quality and cost in production, and improve with real feedback.
Technologies we use
- OpenAI GPT
- Anthropic Claude
- AWS Bedrock
- LangChain
- Python
- Node.js
- PostgreSQL + pgvector
- OpenSearch
- Next.js
- Swift / Kotlin
Related work
See it in practice
FAQ
Frequently asked questions
How much does it cost to build an AI solution?
It depends on the use case, the data involved and the integrations needed. We usually start with a short, fixed-scope discovery and prototype so you see real accuracy and running-cost numbers before committing to a full build. After a first call we give you a written estimate.
Is our data safe? Will it be used to train AI models?
We build on AWS Bedrock or enterprise model APIs that, by default, do not use your data to train their models. Data stays in the cloud region you choose, access is restricted by user role, and sensitive fields can be masked before they reach a model.
Which AI models do you use?
We are model-agnostic and choose per use case: OpenAI GPT and Anthropic Claude models for most tasks, smaller or open-source models where cost, latency or on-premise hosting matter more.
How long does an AI project take?
A working prototype on your own data typically takes two to four weeks. A production rollout depends on the number of systems to integrate and the review processes involved — we agree a timeline after discovery.
Can you add AI features to our existing app or website?
Yes. Most of our AI work adds capabilities — search, summaries, assistants, automation — to products that already exist, without rebuilding them.
How do you prevent AI hallucinations?
Answers are grounded in your own data with source citations, the assistant is designed to decline when it can't find an answer, an evaluation set catches regressions before release, and high-stakes outputs go through human review.
Other services