AI development company in India for your product
Add AI features to your product that help users find information and draft content inside the tools they already use. We build for clients in India, with work that includes smart search and generated descriptions in your product’s voice. As a web, app and AI studio, we shape the feature around what your users need and work with you through the build.
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What happens next
- 01Tell us what you need, in the form or on WhatsApp.
- 02Sohail replies within 1 working day (Mon–Sat, IST).
- 03We agree the scope together, then send your quote.
- Projects delivered
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- Years building for the web
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- Live and in use
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What you get
What’s included in AI features for your product
Chat with your data
Let users ask questions across their documents, records and history.
Smart search
Search that understands meaning, not just keywords.
Content generation
Drafts, summaries and descriptions generated in your product’s voice.
Classification & extraction
Tag, sort and pull structured data from text and files.
Model selection
The right model for each task, balancing quality, speed and cost.
Evaluation & cost control
Tests, caching and limits that keep quality up and costs down.
Tools we use: OpenAI, Anthropic Claude, Google Gemini, Vector databases, Next.js, Python.
Choosing
Should you use a hosted model or run your own?
A hosted model is a practical starting point when you want to avoid running model infrastructure. You pay for usage by volume, and the data sent to the provider is handled under its terms. An open-source model on your own or rented servers gives you more control over data and cost at high volume, but you also take on hosting, updates and tuning.
The way the feature finds information is another decision. Retrieval fetches relevant records when a user asks a question, making it suitable for information that changes often. Fine-tuning fits a fixed style or task format and needs a good set of training examples. The effort you put into preparing those examples is part of the choice.
Where the feature sits also affects permissions. Building it into your product’s own interface keeps data and permissions in one place. A third-party widget is quicker to add, but it may not respect each user’s access. Check that trade-off against the accounts and records your product already has.
Market
What product buyers in India expect
Your users’ expectations depend on the product they use. Customers of a SaaS tool may expect AI help inside the software they already pay for. In an online store or marketplace, staff and buyers need to find items across a large catalogue; search by meaning can connect a request with a relevant result even when the wording differs.
For education and training platforms, the material users need to question is often course content. Legal, HR and compliance software presents a different task: finding answers across long documents. Uploaded files can also be sorted or have structured fields extracted, such as values needed for a record.
Rules
Handling personal data in an AI feature
For a product handling personal data, the DPDP rules on notice, security safeguards and breach intimation come into force on 13 May 2027. We plan a notice in the product and set up access controls so users see only their own data. When an AI feature answers questions across records, those controls need to apply to the information each person is allowed to use.
How it runs
How an AI feature project runs with us
We work remotely with clients through video calls, WhatsApp and shared project boards. You can review the work and discuss decisions with us without needing to be in the same city.
- 01
Design
We design the feature’s desktop and mobile screens before coding. You review them and give feedback, so we can refine how a user reaches the feature and sees its output.
- 02
Prototype
We try the feature on real product data early. You help us understand the records it needs to handle; messy examples expose failures that clean demonstration data can hide.
- 03
Evaluate
We build a test set from real user inputs and expected results. You agree a quality bar with us before release, giving both of us a way to judge the feature against its intended task.
- 04
Release
We put the feature behind a feature flag or release it to a small group first. You help collect feedback on individual answers, and access widens as quality holds.
Checklist
What to have ready before we talk
Start with a problem users actually want solved; an unused feature still costs money to run.
- The user problem and a measure of success, such as searches ending in a click or time saved.
- Realistic product data for testing, with personal details masked if needed.
- Examples of good output, such as useful search results or model answers.
- Expected usage and any budget limit per user or per month.
- Your customer contracts, privacy terms and any limits on sending data to outside providers.
Cost & time
What affects the cost and time of AI features
The scope of the feature and the work needed to run it both shape the estimate.
Feature scope
More features, or deeper links to existing screens, data and permissions, add integration work.
Data preparation
Records and documents may need cleaning, structuring and indexing before search or retrieval works.
Risk
Features involving money, health or legal content need larger test sets, more review and tighter limits.
Usage and model size
Both affect ongoing model charges. Smaller models and caching can reduce those charges.
Avoid
Mistakes to avoid with AI features
Cross-account data leaks
Filter retrieved records by the signed-in user’s permissions before anything reaches the model.
Unbounded bills
Use caching, usage limits and cost monitoring per request so heavy use or misuse does not multiply the model bill.
Unchecked instructions in source material
Uploaded files or web pages can contain text that tries to override instructions. Check outputs that trigger actions.
Changes without comparison
Keep an evaluation set so you can tell whether a prompt or model change improved the feature or broke it.
Questions
Asked by businesses in India
Page updated 5 November 2026
Can you develop an AI chatbot for our product?
Yes. We can build chat that lets users ask questions across their documents, records or history. Answers can link back to the source, giving the user a way to check the information instead of treating the response as the only record.
Which AI models do you use?
We work with OpenAI, Anthropic Claude, Google Gemini and open-source models, choosing the model for the task. Smaller, faster models suit tagging and short drafts; larger models suit long documents and multi-step reasoning. Routing tasks to a suitable model also helps control cost.
What types of AI features can you build?
We can add smart search, classification and extraction to a product. Smart search understands the meaning of a query, while classification and extraction tag, sort and pull structured data from text or files. The choice starts with the task you want users to complete.
Can AI work with our private data?
Yes. We use providers that do not train on your private data and add access controls so users only see their own data. That approach applies when the feature needs to work with information belonging to different users, including their records or documents.
Who owns the work after the feature is built?
You own your domain, content and code, with no lock-in. That includes the code for the work we build for you. Model selection is part of that work: we choose a model for each task by balancing quality, speed and cost.
What should we review after launch?
Review both answer quality and how people use the feature. Log inputs, outputs, user ratings and cost per request, then add failures to the evaluation set. Track edits, retries and abandonment, and rerun the evaluation set when a provider changes or retires a model.
Tell us what you need
Sohail, Founder, replies within 1 working day (Mon–Sat, IST). Free and no obligation.