Generative AI development
Generative AI development, built in India for US teams
We design and ship generative AI products: LLM apps, copilots, content generation, and retrieval systems that answer from your own data. Senior engineers, your time zone, your IP.
What generative AI actually means for a product team
For a while, generative AI was a demo you watched once and forgot. That phase is over. The models are now good enough to sit inside a real product and do real work: draft a reply, summarize a long thread, pull the right clause out of a contract, or turn a rough request into a finished asset. The hard part is no longer the model. It is everything around it.
A model on its own is a clever guess machine. To turn it into a feature your customers trust, you need the right context fed in at the right moment, a way to catch wrong answers before users see them, and a cost profile that does not blow up at scale. That is product work, and it is where most of our time goes.
We think about generative AI the way we think about any feature. What does it do for the user, how do we know it is working, and what happens when it gets something wrong? Answer those three questions well and the technology underneath stops mattering quite so much.
What we build
Different products need different shapes of generative AI. Here is the work we take on most often.
LLM apps and APIs
Full applications built around a language model, from the prompt layer to the user interface to the API your own systems call. These are production services with auth, logging, and rate limits, not notebooks.
Copilots inside your product
An assistant that lives where your users already work and knows what they are looking at. It can answer questions, take actions through your tools, and explain what it did, which keeps people in control rather than guessing.
Content generation
Pipelines that produce text, marketing assets, code, or structured records on demand, with templates and review steps so the output stays on brand and on spec.
Multimodal features
Systems that read and generate across text, images, and audio, for products where a screenshot or a voice note is a better input than a form field.
Retrieval and RAG
Answers grounded in your own documents, tickets, and databases instead of whatever the model happened to memorize. This is what turns a chatbot that sounds confident into one that is actually right.
How we work
We scope first. Before any code, we get clear on what the feature should do and how we will measure it. A vague goal like make support faster becomes a concrete target with example inputs and the answers we expect back.
Then we go evals-first. We build the test set early and run the system against it from the first week, so quality is a number we can watch rather than a feeling. When the score is good enough, we ship a small version to real users and learn from what they actually do with it.
After that it is tuning. Real usage shows where the model struggles, and we close those gaps with better prompts, retrieval, or a smaller fine-tuned model where it earns its keep. The eval suite keeps every change honest, so fixing one case does not quietly break three others.
Why teams pick us
We are a small studio in India, and most of our clients are in the US. Our engineers keep a real overlap with your working hours, so you get a live standup and same-day answers instead of waiting overnight for a reply.
You own everything we make. The code, the prompts, the eval sets, and the infrastructure are yours, with no license traps and nothing to buy back later. We work inside your repositories and cloud accounts so the handoff is already done.
The people who scope your project are the people who build it. No junior swap after the sale, no layer of account managers between you and the work. That is why a first version usually takes weeks rather than months.
Common questions
What kinds of generative AI products do you build?
Mostly four shapes of work: LLM-backed apps and APIs, copilots embedded inside an existing product, content generation pipelines for text, images, and structured data, and retrieval systems that ground answers in your own documents. If your idea touches one of those, we can usually scope it in a first call.
How do you keep a model from making things up?
We treat evaluation as part of the build, not an afterthought. Before we ship a feature we write a test set of real prompts with known good answers, measure how often the system gets them right, and add retrieval or guardrails until the numbers hold. When a model still goes off track, the eval suite tells us where instead of leaving it to a user to discover.
Who owns the code and the intellectual property?
You do, completely. Every line we write, the prompts, the eval sets, and the infrastructure config belong to you. We work inside your repositories and cloud accounts where possible so there is nothing to hand over at the end.
How does working with a Chennai team fit US hours?
Our engineers keep a daily overlap window with US time zones, usually the morning hours on the East and West coasts. That gives you a live standup, same-day answers, and code review that does not stall waiting for a reply across the date line.
How long before we see something working?
A focused first version normally lands in a few weeks, not quarters. We start with the smallest slice that proves the idea works, get it in front of real users, and grow from there once we know the model behaves the way it should.