Custom AI development services in India
We build chat assistants, AI agents, automation and machine learning models that run on your own documents and systems, plus the data work they depend on. Before launch, our own security team reviews and hardens what we built, and we verify every fix.
What we build
Pick the closest group to see how we scope and build it, or start with a call and we'll suggest where to begin.
Chat assistants and search that answer from your own documents, and show each person only what they're allowed to see. This is where large language models (LLMs) and retrieval-augmented generation (RAG, letting the model look up your documents) come in.
Agents that read tickets, query your internal systems and plan multi-step work, with a person approving anything that changes data. Automation that extracts data from documents, scores résumés, reconciles invoices and sorts contracts inside the ERP or CRM you already run.
The whole product around the model: sign-in for many customer accounts, prompt versioning, usage billing, rate limits and defence against prompt injection. And custom machine learning when a language model is overkill or too slow: forecasting, anomaly detection, defect classification and computer vision on edge devices.
See it in practice: an AI recruitment platform and an AI lesson-planning platform for K-12 schools.
Why AI pilots stall
Choosing a model takes about a week. The other seven parts decide whether the system holds up.
The model is usually the easy part now. The trouble is everything around it: data nobody cleaned, an evaluation nobody built and a cost nobody capped. Then there's the moment the system is confidently wrong in front of a customer, with no plan for it.
So we treat the model as one component in an ordinary software system. It needs inputs you trust, an interface, tests, monitoring and a rollback path. Without those you have a demo, and demos are why so many AI pilots get quietly shelved.
Security is part of the build. The team that reviews each system before launch also runs AI security reviews and testing for other companies, and that changes how we build. Once you've traced how an agent could reach data it was never meant to touch, you write different code.
Tools we reach for
These are our defaults. If your team already runs something that works, we use that instead.
Claude, GPT, Gemini and Llama through hosted APIs. We self-host on vLLM when data residency (where your data may be stored and processed) or cost makes that the better choice.
LangGraph or LlamaIndex when the workflow is complex. A plain state machine when it isn't, and that's more common than you'd expect.
pgvector when you already run PostgreSQL and want one fewer moving part. Qdrant or Weaviate at larger scale.
A golden set built from your real queries and scored on each change. It's how we know whether a prompt edit helped.
PyTorch, for time-series forecasting, anomaly detection, defect classification and computer vision on edge devices.
Find the right starting point
Not sure what the work is called? Pick the question closest to yours.
How we work
You find out early whether the idea works, and nothing goes live before our security team has checked it.
We start with your data and the systems the AI must touch. Data work drives the price more than the model does, so we price after we've seen it.
We build the hardest, least certain component first. That shows whether the approach holds before you commit to the full build.
We add what a demo lacks: inputs you trust, an interface, tests, monitoring, cost limits and a rollback path.
A separate security team runs a security posture review with hardening and a vulnerability assessment with triage, sets up logging and detection, and checks that each fix holds.
If your team will run it, we build alongside them and hand it over, evaluation set included.
Positions we hold
We'd rather disagree in week one than in month four.
It shapes how a model behaves, and it's a poor way to teach it facts. When accuracy is the complaint, retrieval is nearly always the cause. We'll say so, and we'll show you the evaluation numbers either way.
If a system can act before you can see what it decided, you hear about the failure from a customer. The first release watches and drafts. The next one acts.
Some of what people ask us to build already exists as a product, and we'll tell you when it does. We lose the project and keep the relationship.
What a build can't promise. A language model can still give a wrong answer after launch. Evaluation sets, guardrails and monitoring make that rarer and quicker to spot. Our pre-launch security review is a point-in-time check, so later changes to prompts, tools or data need a review of their own.
FAQ
Cost, timing, ownership and testing. For anything else, ask us directly.
An AI development company builds software where a model does part of the work: reading documents, answering from your own data, or making a judgment a rule engine can't. Most of the effort goes into the parts around the model. That means the data pipeline that feeds it, the evaluation harness that shows whether a change helped, and the guardrails that limit what the system may do.
A narrow automation with one document type and one workflow typically lands in the low tens of thousands of US dollars. A full RAG application with permissions, evaluation and an admin interface costs more. The data work drives the price more than the model does, so we scope the work after we've seen your data. Our cost calculator gives a usable range in a couple of minutes.
We aim to have the risky component working within three to four weeks, because that shows whether the approach holds. A production release usually follows within two to four months. How long depends on how many systems it has to touch and how clean the data is.
Often, yes. Plenty of our work is joining a team that builds software well but hasn't shipped an AI system before. We build the first one alongside them, then hand it over. We'd rather leave you able to maintain it than dependent on us.
You do. That covers the code, prompts, evaluation sets and any fine-tuned weights. The contract says so in writing.
Yes, and a separate security team does it. Before launch, that team reviews the system's security posture, hardens its configuration, triages any vulnerabilities, sets up logging and runs LLM security testing and agent reviews, using the OWASP Top 10 for LLM Applications, 2026 edition, as the risk vocabulary. Each fix is verified before release. We keep the people who build a system apart from the people who review it.
Page last reviewed 25 September 2026.
Let's talk
Tell us the workflow you want to change or the AI system you want secured, and describe the data behind it. We work with teams in India and worldwide. We reply within one working day.