Before the build, we collect a sample of your real inputs with the correct answers, checked by your team. Each version of the feature is scored against that set, and the score is tracked after launch.
Services · AI & machine learning
AI and machine learning development services
We add AI to work your team already does by hand, such as reading documents, answering questions from your own files and routing requests, with a measure of accuracy and a person reviewing what the model is unsure about.
Overview
AI features with a job, a measure and a fallback
Useful AI projects start with one task that costs your team time today: typing data out of invoices, searching old documents for an answer, or sorting incoming requests. We build that feature into your product or internal tools, measure how often it is right on your own data, and send uncertain cases to a person.
We work with OpenAI models, LangChain and custom Python pipelines, and keep the prompts, data flow and evaluation in your codebase, so the feature can be checked and improved after launch.
Problems this service solves
Manual data entry from documents
Staff retype details from invoices, forms, PDFs or scans into another system, which is slow and error-prone.
Knowledge that is hard to find
Answers sit in contracts, manuals, tickets and shared drives, and only a few people know where to look.
Repetitive triage
Every enquiry, ticket or order has to be read and routed by hand before anyone can act on it.
AI pilots that never reach production
A demo worked on a few examples, but nobody measured accuracy, cost, or what happens when the model is wrong.
How we solve them
Pick one measurable task
We agree what the feature does, how success is measured and what the fallback is before writing code.
Test on your own data
A short pilot runs on a sample of your real documents or questions, so accuracy is measured on your data rather than a vendor demo.
Keep a person in the loop
Low-confidence results go to a review queue. Staff approve or correct them, and corrections feed the next round of evaluation.
Ship it where people work
The feature runs inside your app, admin panel or internal tool, with logging and usage-cost tracking.
What's included
Document extraction
Fields pulled from invoices, forms, receipts and scanned PDFs into structured data your systems can use.
Search and assistants over your content
Question answering over manuals, policies, tickets and knowledge bases, with links back to the source.
Classification and routing
Enquiries, tickets and documents sorted by type, urgency or owner.
LLM features in existing apps
Summaries, drafts and assistants added to a product you already run.
Computer vision, forecasting and recommendations
Image checks, demand forecasts and ranking models, where you have the data to support them.
Evaluation and monitoring
Test sets, accuracy and cost tracking, and alerts once the feature is live.
Technologies we use
- Models & frameworks
- OpenAI
- LangChain
- Python
- Pipelines
- Custom ML pipelines
- Data pipelines
- Evaluation sets
- App layer
- Next.js
- Node.js
- TypeScript
- Data
- PostgreSQL
- MongoDB
How an AI project runs
Discovery call
We look at the task, the documents or data involved, and how the result will be used.
Pilot on your data
A small, time-boxed build on a sample of real inputs, scored against answers your team has checked.
Production build
Integration into your app, a review queue for uncertain results, logging and access control.
Launch and monitoring
Accuracy, cost and volume tracked after launch, with the test set re-run whenever prompts or models change.
The pilot is scoped after the discovery call. Full platforms with AI, admin panels and integrations typically take 12–20 weeks.
What you get
Less manual work
Staff check and correct results instead of typing everything from scratch.
Accuracy you can see
Every version is scored against a test set built from your own examples.
Controlled risk
Uncertain results go to a person, and the feature can fall back to the manual process.
Practical examples
Illustrations of how these features usually work, not client results.
Invoice and form processing
Supplier invoices arrive as PDFs or photos. The model extracts the supplier, date, amounts and tax fields; anything below a confidence threshold goes to a person to confirm before it reaches the accounting system.
Answers from your own documents
Sales or support staff ask a question in plain language and get an answer drawn from your policies and past proposals, with a link to the source paragraph so they can check it.
Enquiry triage
Website and email enquiries are tagged by service and urgency and routed to the right person, with the original message kept alongside.
Shipping AI features to production
Related work
We haven't published an AI case study yet. These are production platforms our team built: the kind of software an AI feature is added to.
Questions about AI projects
Something else? Ask us directly; we reply within one business day.
Results below an agreed confidence level go to a review queue instead of straight into your system. A person approves or corrects them, and the feature can fall back to the manual process.
That depends on the model provider and hosting you choose. We document which services receive data, limit access to what the feature needs, and discuss providers and regions that meet your requirements during discovery.
Not for document extraction or search over your files: a representative sample is enough to build and test a pilot. Forecasting and recommendation models need historical data, and we will tell you if there isn't enough yet.
Yes. Most AI features are added to an app or internal tool you already use, rather than built as a separate product.
The pilot is scoped and quoted after the discovery call, and budget ranges start from ₹5L for focused builds. Model usage costs are measured during the pilot, so you can see the running cost before committing to a full build.
Services that work alongside this one
Read before you start a project
Let's talk
Have a task you'd like AI to take on?
Describe the task and the documents or data involved. We reply within one business day with questions and a suggested pilot.
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