AI development company for product features in the United States
Give your users AI features inside the product they already use. For a business in the United States, we build chat over documents and records, and tools that draft summaries or descriptions in your product’s voice. MSH Technologies is a web, app and AI studio in India. We work with you to shape the feature around your users and the task they need to complete.
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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
- 0+Projects delivered
- Years building for the web
- 0+Years building for the web
- Live and in use
- 0%Live and in use
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 needs no infrastructure for you to run. You pay for usage by volume, and the data sent to the model goes to its provider under that provider’s terms. An open-source model on your own or rented servers gives you more control over data and, at high volume, cost. It also leaves you responsible for hosting, updates and tuning.
Match the model to the task. Smaller, faster models suit tagging and short drafts; larger models suit multi-step reasoning and long documents. For records that change often, retrieval fetches relevant material when a user asks a question. Fine-tuning suits a fixed style or task format, provided you have a good set of training examples.
The interface is a separate choice. Building into your product keeps its data and permissions in one place. A third-party widget is quicker to add, but it may not respect access for each user. Look at who needs to see which records before choosing the quicker route.
Market
What product buyers in the United States expect
Your users expect help in the tools they already use. For a SaaS product, that could mean an AI feature within the service customers pay for, with a clear purpose in their existing work.
Online stores and marketplaces with large catalogues have different needs: buyers need search that understands meaning, while staff need useful product descriptions. In a CRM or helpdesk, the task could be a summary, a drafted reply or a support ticket placed in the right category. Education and training platforms need answers and practice feedback grounded in their course material.
Rules
What a project in the United States has to handle
If your website or online service is directed to children under 13, COPPA applies to its operator. Discuss the information an AI feature will use before deciding how it should be handled. We set up access controls so users see only their own data and use providers that do not train on it. During planning, identify what users will put into the feature and which records it needs to retrieve, so those controls can be designed around the product.
How it runs
How your AI feature project runs with us
We work remotely over video calls, WhatsApp and shared project boards. In winter, IST is 10h30m (Eastern) to 13h30m (Pacific) ahead of the mainland US. Shared boards keep decisions and feedback visible across the time difference.
- 01
Prototype
We try the feature on realistic product data early. You help us select records that reflect actual use, including messy material that a clean demonstration would miss.
- 02
Design
We design screens for desktop and mobile. You review them and give feedback before any code is written, so the interface reflects how people will use the feature.
- 03
Evaluate
We build a set of real user inputs with expected results. You agree a quality bar with us before release, giving everyone a basis for reviewing the output.
- 04
Build and test
We add tests, caching and limits to the feature. These give us ways to check quality and control the work sent to the model.
- 05
Release
Start behind a feature flag or with a small group. Collect feedback on individual answers, then widen access as the quality holds.
Checklist
What to have ready before we talk
Bring these details to make the scope discussion useful.
- The user problem and a measure of success, such as searches ending in a click or support tickets avoided.
- A copy of realistic product data for a prototype, with personal details masked if needed.
- Examples of good output, such as an ideal search result, description or answer.
- Expected usage and any budget limit per user or per month.
- Existing customer contracts, privacy policy terms and limits on sending data to outside providers.
Cost & time
What affects the cost and time of AI features
The work involved depends on the feature and the product it has to fit into.
Feature scope
Each feature needs work on its connection to existing screens, data and permissions. A deeper integration adds more to examine and build.
Existing code
The state of your codebase and the APIs available for the feature affect how readily it can connect to your product.
Data preparation
Records or documents may need cleaning, structuring and indexing before search and retrieval can use them.
Model usage
Volume and model size affect ongoing charges. Caching and smaller models reduce those charges, so the intended use belongs in the scope discussion.
Avoid
Mistakes to avoid with AI features
Trusting a clean demo
Test against real inputs before committing to the build. Keep an evaluation set so a prompt or model change can be checked for improvements and breakages.
Leaving usage unbounded
Cache repeated work, set limits and monitor cost per request. A popular feature or misuse can otherwise multiply the model bill.
Ignoring prompt injection
Uploaded files or web pages can contain text that tries to override instructions. Check any output before it triggers an action.
Adding a feature nobody asked for
Define the user problem first. An ignored feature still costs money to run.
Questions
Asked by businesses in United States
Page updated 5 November 2026
Which company can develop an AI chatbot for my product?
We build chat features that let users ask questions across their documents, records or history. Answers can link to their sources so users can check them. We work with models from OpenAI, Anthropic Claude, Google Gemini and open-source options, selecting a model for the task the chatbot needs to perform.
What does smart search include?
Smart search helps users find results by meaning instead of matching keywords alone. Model selection is part of the work: we balance quality, speed and cost for the task. Bring examples of searches and the results you would expect to see, so the feature has a clear target to work towards.
Can AI sort information from our files?
Yes. Classification and extraction can tag and sort text or pull structured fields from files. For example, the feature can route support tickets or read values from uploaded documents. The useful starting point is a set of real files and a clear description of the fields or categories your product needs.
Can users review generated content before using it?
Yes. A content feature can draft descriptions, summaries, replies or reports in a set voice, with an opportunity for the user to edit the result before saving or sending it. We can also label generated content clearly and give users a way to regenerate output when the first draft needs work.
Who owns the code after the project?
You own your domain, content and code, with no lock-in. Before work starts, identify the domain, content and code involved in the feature, and discuss how you want to manage them after launch. That gives you a practical basis for the handover and for future changes to your product.
How do we keep checking the feature after launch?
Review inputs, outputs, user ratings and cost per request, and add failures to the evaluation set. Adoption is useful to track too: look at how often people use, edit, retry or abandon the feature. Providers also update and retire models, so run the evaluation set again when switching models.
Tell us what you need
Sohail, Founder, replies within 1 working day (Mon–Sat, IST). Free and no obligation.
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