AI & Data Solutions

Most companies already hold the data and have no dependable way to question it: sales rows exported from Tally, a MySQL table behind the billing app, six months of spreadsheets in a shared drive. This service covers the distance between that and a decision. The data gets checked and cleaned, put in front of people in a form they will actually open, and fed to a model where a model earns its place. It is built for Indian firms that want machine learning running in their business, not a research project.

What's included

• A data audit before anything is promised: what exists, what is missing, and which of your questions the data can honestly answer.
• Cleaning and feature engineering delivered as pandas code you can re-run next quarter, not one-off spreadsheet edits nobody can repeat.
• A dashboard in Power BI or Streamlit, with each KPI defined in writing so two readers agree on what a tile counts.
• Predictive models where one is warranted: demand forecasting, churn and lead scoring, classification, pricing support, recommendations. Scikit-learn and XGBoost for tabular data, PyTorch or Hugging Face for images and text.
• LLM work: a chatbot over your own documents with citations, field extraction from messy text, or an agent that calls your internal APIs, each with an evaluation set rather than a demo screenshot.
• Deployment as a REST API or a small web app, with request logging, drift checks and a written retraining procedure.

Every model comes with its baseline, its error on held-out data, and the cases where it fails. Nothing is called accurate without a number beside it.

How we work

A call of 30 to 45 minutes covers the decision you want to improve, what data exists, and who reads the output. A written scope follows with deliverables, milestones, a fixed price in rupees and a list of exclusions, and nothing starts until you approve it. The first milestone is usually the data report, because that is where the surprises live. The second is something you can click. Handover puts the code, model files, notebooks and setup notes in a repository you control, with a walkthrough call and 15 to 30 days of support.

Typical projects

• Demand forecast for a retailer. Two years of item-level sales, weekly per-SKU forecasts, delivered as an Excel feed and a dashboard. About three weeks.
• Sales dashboard for a distributor. Daily MySQL pull, beat-wise and party-wise views, outstanding ageing. Seven to ten days.
• Document assistant for an office. Around 400 policy PDFs, retrieval chat that cites the source page. Two to three weeks.
• Invoice reading. OCR and layout rules pulling line-item fields into a clean table, with exceptions flagged for review. Three to four weeks.

What it costs

Analytics and dashboard work starts at ₹7,999 for 5 to 10 days: cleaning, exploratory analysis, a dashboard and an insight document. A predictive model or a retrieval chatbot is the ₹24,999 tier over two to four weeks, which buys model comparison, an evaluation report and a deployed endpoint. Advanced work starts at ₹69,999 across 5 to 10 weeks, for deep learning, recommendation systems, agents and MLOps. Integration and upkeep move a project up a tier more than the choice of algorithm does. Prices exclude 18% GST. Earlier work is on the portfolio.

Frequently asked questions

Do we own the model and the data? Yes. On full payment the code, model files and documentation are yours in a repository you control, and your data is used only for your project.

How long until we see something usable? The data report lands in week one on almost every project, and a working dashboard or a first model inside two to three weeks. Delays are nearly always about data access rather than modelling.

Will it work with the systems we already run? Usually: MySQL, PostgreSQL, Excel and CSV drops, Tally exports, Google Sheets, anything with a REST API. A closed desktop application with no export path is where it stops, and that gets said before you pay. Send your stack list for a straight answer.