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AI Development

AI solutions, built to be used.

We design and build AI-powered applications, from machine learning models to complete web platforms. Our own flagship product, MarketMentor, came out of this work, and we bring the same approach to projects for institutions and organisations.

What we build

Practical AI, from model to finished product.

Six kinds of work we take on. Each starts with a clear problem and ends with something your users can open and use.

AI and ML solutions

Predictive and classification models built on your data, from feature engineering to a tested model you can rely on.

  • Prediction and forecasting
  • Classification and scoring
  • Recommendation
Discuss a project: AI and ML solutions

Generative AI applications

Assistants and tools built on large language models, grounded in your own documents and data.

  • LLM-powered assistants
  • Retrieval over your documents
  • Content and summarisation tools
Discuss a project: Generative AI applications

Intelligent automation

Automating repetitive processes with RPA and AI, so teams spend time on work that needs judgement.

  • Process automation (RPA)
  • Document handling
  • Workflow integration
Discuss a project: Intelligent automation
Built by DeepLearnAILabs

Products from our own AI Development.

The clearest evidence of how we build is what we've shipped.

Flagship product

MarketMentor

Our AI-powered stock market learning and analysis platform: interactive education, Indian stock analysis, fair value calculators, financial valuation models and AI-powered insights.

  • Valuation models and fair value calculators
  • AI-assisted analysis of company numbers
  • Paper trading, live indices and market news
Education

Virtual Labs

An interactive learning platform for CBSE Class 10, with Science, Maths, Social Science and AI labs, a practice zone and a student dashboard.

See it in AI Learning
How we deliver

A clear path from idea to launch.

  1. Discover

    Understand the problem, the users and the data you already have.

  2. Design

    Choose the approach, define success measures and plan the build.

  3. Build

    Develop the model and the application around it in short, reviewable iterations.

  4. Test

    Validate accuracy, usability and edge cases before anything goes live.

  5. Deploy and hand over

    Launch, document and hand over so your team can run it with confidence.

Technologies we work with

  • Python
  • scikit-learn
  • TensorFlow
  • PyTorch
  • pandas and NumPy
  • Large language models
  • Power BI
  • HTML, CSS and JavaScript
  • Bootstrap
Need to prove an idea before building it?See AI Research: prototyping, experimental validation and consultancy

Have an AI product or tool in mind?

Describe what you want to build and who will use it. We'll come back with an approach and next steps.