AI development company for products in the United Kingdom
Give your users AI features inside the product they already use. We add chat that links answers to source documents, search that understands meaning and content drafting in your product’s voice. For a product in the United Kingdom, MSH Technologies works from India to shape these features around what your users need and build them into your existing website or app.
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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
What should you build first?
Start with the data and the task. For questions over records that change often, retrieval fetches relevant material when the user asks. Fine-tuning suits a fixed style or task format, but needs a good set of training examples. Preparing those examples takes work, so agree the desired output before choosing that route.
You also have an interface choice. Building the feature into your product keeps data and permissions in the same place; a third-party widget is quicker to add, but might not respect access for individual users. Check that distinction before putting private records behind a chat box.
Hosted models need no infrastructure for you to run, which removes some setup work. Their usage is billed by volume, however, and data goes to the provider under its terms. We help you define the user problem and scope before choosing how the feature should fit into your product.
Rules
What a United Kingdom AI feature needs to handle
Online stores and marketplaces need buyers to find products across large catalogues, while staff need descriptions they can work with. SaaS customers may expect AI help inside the tools they already use. Education and training platforms can use question answering and practice feedback over course material.
UK GDPR and the Data Protection Act 2018 govern data protection. When your feature uses customer records, that affects the build: we set up access controls so users see only their own data and use providers that do not train on it. Your existing customer commitments also need to be part of the scope.
How it runs
How an AI feature project runs with us
We work remotely over video calls, WhatsApp and shared project boards. IST is 5h30m ahead, or 4h30m during British Summer Time, from the last Sunday in March to the last Sunday in October. Shared boards keep decisions and feedback visible across that difference.
- 01
Design
We create desktop and mobile screens for the feature. You review them and give feedback before any code is written, so the interaction is agreed before the build starts.
- 02
Evaluate
We build a test set from real user inputs and expected results. You agree the quality bar with us before release, so there is a clear basis for checking the output.
- 03
Release
We put the feature behind a feature flag or offer it to a small group first. Feedback on individual answers helps us decide when quality supports wider access.
- 04
Control
We add tests, caching and limits to keep quality and running costs in view. You can use the agreed scope and quality bar to review the feature as it develops.
Checklist
What to have ready before we talk
Bring these details to make the scope discussion useful.
- User problemDescribe the task people struggle with and who needs help.
- Success measurePick a signal such as searches ending in a click or support tickets avoided.
- Product dataProvide realistic records for prototyping and testing, with personal details masked if needed.
- Good outputGather examples of ideal search results, descriptions or answers to set the target.
- Usage and budgetShare expected use and any limit per user or per month.
Cost & time
What affects the cost and time of AI features
The work and running costs follow the scope and the tasks the feature handles.
Feature scope
Each feature adds work, especially when it connects deeply to existing screens, data and permissions.
Data preparation
Documents and records need cleaning, structure or indexing before search and retrieval can use them.
Model choice
Smaller models suit tasks such as tagging and short drafts; multi-step reasoning and long documents suit larger models. Routing tasks accordingly helps control cost.
Repeated usage
We select efficient models, cache repeated work and set usage limits to keep model costs under control.
Avoid
Mistakes to avoid with AI features
Trusting a polished demo
A few clean examples do not show how a feature handles real data. Test real inputs before committing to the build.
Skipping a test set
Without expected results, you cannot tell whether a model or prompt change improved the output or broke it. Keep a set for comparison.
Ignoring instructions hidden in files
Uploaded text can try to override a feature’s instructions. Put checks around any output that triggers an action.
Leaving usage unbounded
A popular feature or misuse can multiply the model bill. Monitor cost per request and use caching and limits.
Questions
Asked by businesses in United Kingdom
Page updated 5 November 2026
Which companies develop AI chatbots?
We build chat features that let users ask questions across their documents, records and history. For the underlying task, we work with OpenAI, Anthropic Claude, Google Gemini and open-source models. The choice of model follows the job your chatbot needs to do; you do not need to settle on a provider before discussing the feature.
How much does it cost to add AI to a product?
There is no fixed price. The state of your existing codebase and the availability of APIs affect the work needed to connect a feature. Risk affects it too: features touching money, health or legal content need larger test sets, more review and tighter limits. We would first need to understand the scope.
What can you include besides chat?
We can add classification and extraction to tag and sort text or pull structured fields from files. In a helpdesk, that could mean routing support tickets; for uploaded documents, it could mean reading specific values. We also select a model for each task, balancing quality, speed and cost.
Will we own the code?
Yes. You own your code, domain and content, with no lock-in. That gives you control over those product assets after the work is delivered. Discuss the existing code and the parts of the product the AI feature needs to connect to when you agree the scope.
What should we monitor after launch?
Track how often people use, edit, retry or abandon the feature, then use that evidence to decide what to improve or remove. Log inputs, outputs, user ratings and cost per request; review samples and add failures to the evaluation set. When a provider changes or retires a model, rerun that set.
How do we decide whether an AI feature is worth building?
Start with a clear user problem and a measure of success. A feature people did not ask for can be ignored while still costing money to run. For a helpdesk or CRM, a concrete task might be summarising a record, drafting a reply or classifying a ticket for staff.
Tell us what you need
Sohail, Founder, replies within 1 working day (Mon–Sat, IST). Free and no obligation.
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