AI & Data
Models that hold up outside the notebook.
Forecasting, scoring and detection — validated against reality and monitored after deployment.
Sound familiar?
- “Your forecasting is a spreadsheet built on last year's averages and someone's intuition.”
- “A data scientist built a model that worked in testing and nobody could deploy it.”
- “You have years of operational data and no idea what it could tell you.”
If any of those land, this is the page for you. Here's how we approach it.
What you get
What machine learning actually changes.
Decisions based on evidence
Forecasts and scores grounded in your history rather than assumption.
Deployed, not just built
Models delivered into production behind an API your systems can actually call.
Validated honestly
Backtested against held-out periods, with the error range stated plainly rather than a single flattering accuracy number.
Monitored for drift
Model performance tracked after launch, because reality changes and accuracy decays.
Explainable where required
Feature attribution for decisions that affect people — necessary for lending, hiring and healthcare.
Told when it isn't worth it
If your data can't support a reliable model, we'll say so before you spend the budget.
What's included
Everything in the engagement.
Data assessment
An honest read on whether your data can answer the question.
Feature engineering
Turning raw operational data into usable signal.
Demand forecasting
Inventory, staffing and capacity planning.
Scoring models
Lead, credit and churn scoring with explainability.
Anomaly detection
Fraud, quality defects and equipment failure.
Recommendation
Product and content recommendations tuned to your catalogue.
Model deployment
Served behind an API with versioning and rollback.
Monitoring
Drift detection and scheduled retraining.
How we deliver
You'll know where it stands every week.
Discovery
1–2 weeksScope document, risk list and a fixed estimateDesign & architecture
2–3 weeksClickable prototype and system designBuild
6–16 weeksA working demo at the end of every sprintTest & harden
ContinuousAutomated test suite, UAT sign-off, security reviewLaunch
1 weekProduction deployment, monitoring and full handoverSupport
90 days includedSLA-backed fixes and a roadmap for what's nextTechnology
What we build it with.
- Python
- scikit-learn
- PyTorch
- TensorFlow
- pandas
- MLflow
- FastAPI
- AWS
- PostgreSQL
We choose on fit, hiring pool, total cost and how easily you could leave — not on what we most enjoy writing.
Industries
Where we've done this.
- Finance & FintechKYC, lending and payments built to audit.
- ManufacturingShop floor data in the same system as the ledger.
- Retail & E-commerceStock, storefront and marketplace in sync.
- Logistics & Supply ChainEvery consignment traceable from pickup to POD.
- HealthcareABDM-ready systems for hospitals, clinics and diagnostics.
- AutomotiveDealer, service and parts on a single platform.
Engagement models
Pick the risk model that suits you.
Indicative starting points. We give a firm number after discovery — a fixed price quoted before we understand the scope is a number designed to be revised.
Fixed scope
₹8,00,000
starting from
A clear brief you want delivered to a firm budget
- Fixed price agreed after discovery
- Defined deliverables and milestones
- Change requests quoted separately
- 90 days post-launch support
Dedicated team
₹3,50,000 / month
starting from
Evolving requirements, or an in-house team that needs capacity
- Senior engineers embedded in your standups
- Scale the team up or down monthly
- You set the priorities each sprint
- Direct access — no account manager layer
Retainer
₹1,20,000 / month
starting from
Ongoing improvement, maintenance and support after launch
- Agreed monthly hours
- Guaranteed response times
- Monitoring, patching and dependency upgrades
- Quarterly roadmap review
FAQ
Machine Learning questions.
Still have one? Talk to an engineer, not a salesperson.
It depends on the problem, but as a rough guide: a couple of years of history for seasonal forecasting, a few thousand labelled examples for classification. We assess this first and will tell you plainly if the answer is 'not enough yet' — that's a cheaper conversation than a failed model.
We can't promise a number before seeing the data, and anyone who does is guessing. What we commit to is honest validation: backtesting on held-out periods and reporting the error range, not a cherry-picked figure.
Because deployment, monitoring and retraining are treated as an afterthought. A model in a notebook is a prototype. We scope production from the start — serving, versioning, drift monitoring — because that's where the value is.
Frequently the latter, and we'll say so. A good dashboard and a clear rule solve a lot of problems that get pitched as machine learning, at a fraction of the cost and complexity.
Related services
Often needed alongside.
Start the conversation
Tell us what you need.
An engineer replies within 4 business hours — with questions, not a brochure.
NDA available before you share anything.