AI and machine learning development
Machine learning applied where it measurably beats a simpler solution — and honest advice when it does not.
Kolkata and West Bengal
You own the source code
Support after launch
A great deal of what is sold as AI would be better served by a database query and a well-designed form. Machine learning earns its cost when the rules are genuinely too numerous or too fuzzy to write down: reading varied documents, forecasting demand, classifying free text, spotting anomalies in transactions.
We start by asking what decision the model is supposed to improve and how you would know it had. If there is no clean answer, we say so before anyone spends money on it.
Problems this solves
Manual document processing
Staff reading invoices, forms and delivery notes and typing the contents into a system is slow, expensive and error-prone.
Forecasts based on intuition
Demand planning done from memory ties up working capital in the wrong stock.
Free text nobody analyses
Support tickets, reviews and complaints contain patterns that no one has the hours to read for.
Models with no way to be judged
Without a baseline and an evaluation set, there is no way to know whether a model is helping or quietly making things worse.
What the work covers
Feasibility assessment
Whether the data supports the question, before any modelling begins.
Data preparation
Cleaning, labelling and building an evaluation set that means something.
Document extraction
OCR and structured extraction from invoices, forms and scanned records.
Forecasting and classification
Demand prediction, categorisation and anomaly detection on your data.
Language features
Search, summarisation and assistants grounded in your own documents.
Monitoring
Tracking accuracy in production and detecting drift as conditions change.
Data through to monitored production
Machine learning is a loop, not a delivery. Monitoring feeds back into data and preparation, because a model’s accuracy decays as the world it was trained on changes.
Data
What exists, its quality and whether it answers the question.
Preparation
Cleaning, labelling and a held-out evaluation set.
Model
Baseline first, then the simplest approach that beats it.
Training
Fitting and tuning against measurable targets.
Evaluation
Accuracy measured against the baseline, not in isolation.
Integration
Exposed through an API into the actual workflow.
Monitoring
Production accuracy and drift detection over time.
Monitoring returns to data: production evidence is what drives the next round of preparation.
What you end up with
A feasibility answer before a development budget
Measured against a baseline, not a demo
Document handling that removes real manual hours
Models integrated into workflow, not left in notebooks
Accuracy monitored after launch
Clear advice when conventional software is the better answer
Technologies we commonly use for this
The stack is chosen per project — from your requirements, your existing systems and who will maintain it afterwards. This list is what we reach for most often, not a fixed answer.
Ways to work together
Model A
Fixed scope, fixed price
The scope is written down in detail before work starts, and the price is fixed against it. Changes are quoted separately as they come up.
Best for: Well-understood projects — a website, a defined module, a rebuild.
Model B
Monthly retained team
An agreed number of developer days each month, directed by your priorities. Scope can move without renegotiating a contract.
Best for: Products that will keep evolving after the first release.
Model C
Phased delivery
The system is split into phases that each ship something usable. Every phase is quoted before it begins.
Best for: Large ERP, SaaS and platform builds where the full scope is big.
Model D
Support and maintenance
Ongoing care for a system that already exists — ours or someone else’s — covering fixes, updates and small enhancements.
Best for: Live systems that need a reliable pair of hands.
Sectors we apply this in
Frequently asked questions
It depends entirely on the problem. Document extraction can work from a few hundred labelled examples; reliable demand forecasting usually needs a couple of years of history. We assess this first, and will tell you plainly if the answer is no.
No, and we will say so. If the rules can be written down, conventional software is cheaper to build, cheaper to run, easier to explain and does not degrade over time.
That is decided with you before anything is built. Models can run entirely within your own infrastructure where data sensitivity requires it, and any third-party service is agreed in advance.
By measuring it against a baseline on data it has never seen, and by monitoring the same measure in production so degradation is visible rather than assumed.
Topics people search for
Related services
Custom Software Development
Software shaped around the way your business already works, instead of a packaged product you have to reorganise around.
API Development
The interfaces that let your systems talk to each other, and let partners build on top of what you have.
Cloud Solutions
Hosting designed around what your application actually needs, with the monitoring and backups that make it safe to rely on.
Digital Transformation
Moving a business off paper and spreadsheets without stopping the business to do it.
Talk through your project with a developer
You will speak to someone who writes the software, not a salesperson working from a script. Bring your requirements, or just the problem.