AI software development · India
AI Development Company in India
We build AI features that do a specific job inside your business software: answering questions from your own documents, drafting and classifying, extracting data, or automating a step that people currently do by hand.
We are a Gurugram-based team that treats AI as one component of a larger system. The model call is usually the easy part; the work that decides whether an AI feature is useful is the data it can see, how its output is checked, and what happens when it is wrong.
Where AI tends to help, and where it does not
Repetitive reading and writing
Staff spend hours summarising enquiries, drafting replies, tagging tickets or pulling fields out of documents. A model can do a first pass that a person reviews.
Knowledge that is hard to find
Policies, product details and past answers are scattered across files. Retrieval over your own content lets an assistant answer with a source instead of guessing.
Requirements gathering and intake
Long forms put people off. A conversational intake can collect the same information step by step and hand a structured summary to your team.
Decisions that need to be exact
Pricing, eligibility, payments and anything regulated should stay in deterministic code. We will tell you when a rule-based approach is the better answer.
What we build
- LLM integration
- Connecting language models from established providers to your application through a backend that controls prompts, limits and data access. Keys never reach the browser.
- RAG over your content
- Retrieval-augmented generation: your documents are split, embedded and searched so the model answers from your material, and the answer can show where it came from.
- AI chatbots and assistants
- Chat interfaces for customers or staff with conversation history, handoff to a person, and clear limits on what the assistant is allowed to do.
- Structured extraction and classification
- Turning emails, forms and documents into validated fields your system can store, with a review step for low-confidence results.
- Workflow automation
- AI steps placed inside an existing process: triggered by an event, writing to your database, notifying the right person, and logging what was done.
- Machine learning and NLP features
- Classical ML and NLP in Python where a smaller, cheaper model is a better fit than a large language model, such as categorisation or similarity search.
Engineering AI features that hold up in production
A demo that works on five examples is not a product. These are the parts we design before an AI feature reaches your users.
Evaluation before launch
We agree a set of real example inputs with expected outcomes, then measure the feature against them whenever prompts, models or data change.
Grounding and citations
For question answering, retrieval limits the model to your approved content and the interface shows sources, so people can check an answer instead of trusting it.
Human review
Where a wrong answer has a cost, output is a draft that a person approves. We design the review screen as part of the feature.
Cost and rate control
Usage limits per user, caching of repeated work and choosing the smallest model that meets the quality bar keep running costs predictable.
Privacy and data access
The model only receives the data a task needs, access rules from your application still apply to retrieved documents, and sensitive fields can be excluded.
Failure handling
Timeouts, provider outages and unusable output all have a defined fallback, so the rest of your application keeps working when the AI step does not.
Technology we use for this work
- Languages
- Python · JavaScript
- AI
- LLM provider APIs · Embeddings · Retrieval-augmented generation · NLP
- Data
- PostgreSQL · Vector search
- Application
- FastAPI · React · REST APIs
How a project runs
AI projects start with one workflow, not a platform. We look at real examples of the input and the output you expect, build a small prototype, and measure it before deciding whether to take it further.
- 01DiscoveryYour goals, users, current tools and constraints.
- 02RequirementsAgreed scope for a first useful release, written down.
- 03ArchitectureData model, APIs, integrations and hosting decided up front.
- 04UI/UXScreens and flows designed and reviewed with you.
- 05DevelopmentBuilt in increments you can see and test along the way.
- 06TestingFunctional, mobile, permission and failure-case checks.
- 07DeploymentReleased to production with environment configuration.
- 08SupportFixes, monitoring and the next phase, as agreed.
AI we run ourselves
The project planner on this website is an AI assistant we built. It asks a visitor about their project in a conversation, keeps track of the details collected so far, and produces a written project plan that can be downloaded as a PDF or emailed. It is rate-limited per user and runs on our Python backend, not in the browser.
In the VStitch commerce platform, PostgreSQL holds the transactional data alongside a vector layer that prepares the catalogue for similarity and context-based features.
Questions buyers ask us
Can you add AI to our existing website or software?
Usually, yes. It depends on whether we can reach the data the feature needs and where its output should go. We review the existing system and suggest the least disruptive place to add it.
Will the AI always give correct answers?
No, and you should be wary of anyone who says otherwise. We reduce errors with retrieval, constrained outputs and evaluation, and we design human review in wherever a wrong answer matters.
Is our data used to train the model?
We use provider APIs and settings that do not train on your data where the provider offers that, send only the data a task needs, and discuss data residency and retention before anything is built.
Which AI model will you use?
We pick per task, comparing quality, speed and cost on your own examples. The integration is built so the model can be swapped later without rewriting the feature.
What does it cost to run?
Running cost depends on how many requests you make and how much text each one processes. We estimate it from expected usage during the prototype and add limits and caching to keep it in budget.
Do we need a lot of data to start?
Not for most LLM features. A set of real documents and a few dozen example inputs with good answers is enough to build and evaluate a first version.
Discuss your project
Tell us what you want to build, what it has to connect to and when you need it. We will get back to you to talk through scope and next steps.