Machine learning that reaches production, not just the demo.
NetWizard builds custom ML models, forecasting and simulation systems, and integrates AI — including large language models — into the software and workflows your business already runs.
From raw data to decisions running in production.
Six practice areas, one team. Most engagements combine several — a model is only as good as the data pipeline under it and the deployment around it.
Custom Machine Learning
Models trained on your data, built to beat a measurable baseline.
Learn morePredictive Modelling & Simulation
Forecasting, optimisation and what-if models for planning decisions.
Learn moreAI & LLM Integration
Large language models connected to your systems, data and workflows.
Learn moreComputer Vision & Document AI
Read images, video and documents the way your experts do.
Learn moreMLOps & Production Deployment
Take models from notebooks to monitored, versioned production services.
Learn moreData Foundations
The pipelines and storage every reliable model depends on.
Learn morePut large language models to work — safely.
Most LLM pilots stall between the demo and production. We close that gap with retrieval over your own data, evaluation suites that measure accuracy, and the guardrails your security team will ask about.
How we integrate AI- Assistants that answer from your documents — with citations to the source
- Agents that take actions through your existing APIs, with human approval where it matters
- Choice of model: commercial APIs, or open-weight models hosted inside your network
- Automated evaluation before every release, so quality is measured rather than assumed
- Role-based access, audit logs, PII redaction and per-request cost tracking
- Support for Indian languages including Hindi, Tamil, Telugu, Kannada, Marathi and Bengali
Applied where the data — and the payoff — is.
Our methods are general; our problem framing is specific to how your industry makes money and manages risk.
Manufacturing
Fewer defects, less downtime and tighter production plans.
Banking, Financial Services & Insurance
Faster, better-explained decisions on risk and customers.
Healthcare & Life Sciences
Clinical and operational AI that keeps people in the loop.
Retail & E-commerce
Know what customers want and stock it in the right place.
Logistics & Supply Chain
Move more with the same fleet, warehouse and people.
Energy & Utilities
Balance supply and demand on increasingly variable grids.
Agriculture & Agritech
Data-driven decisions from field to mandi.
Public Sector & Enterprise IT
Secure AI that respects data-residency and audit requirements.
A clear path with a decision point at every stage.
You see evidence on your own data before committing to a full build.
Discover1–2 weeks
We map the decision you want to improve, audit the data available, and agree a baseline and a target metric. You get a written feasibility assessment — including an honest 'not yet' if the data isn't ready.
Prototype3–6 weeks
A working model on your real data, evaluated against the baseline. This is the point to stop or continue, with evidence rather than a slide deck.
Build6–12 weeks
Production-grade pipelines, APIs and interfaces, integrated with your systems, tested and security-reviewed.
Deploy1–3 weeks
Staged rollout on your cloud or on-premise infrastructure, with monitoring, alerting and a rollback plan in place before go-live.
Operate & improveOngoing
We monitor drift and accuracy, retrain on new data, and hand over to your team with documentation and training — or keep running it for you.
Start small. Scale when it works.
Pilot
A defined problem, a fixed timeline and price, and a go / no-go decision at the end. The lowest-risk way to start.
- 4–8 weeks
- Fixed price
- Working prototype + report
Dedicated team
A cross-functional pod — ML engineers, data engineers and a lead — working as an extension of your team.
- Monthly engagement
- Scales up or down
- Your sprints, your tools
Advisory
Senior guidance for teams building AI in-house: architecture reviews, model audits, vendor selection and roadmaps.
- Retainer or per-review
- Architecture & audits
- Hiring support
Engineering discipline, applied to AI.
Measured, not magical
Every project starts with a baseline and a target metric. If AI can't beat the simple approach, we'll tell you.
Your data stays yours
We work inside your cloud or on-premise where needed, sign NDAs as standard, and design for India's DPDP Act from day one.
Built to be handed over
Clean code, documentation and training, so your team can own what we build. No black boxes, no lock-in.
Production is the finish line
A demo is the beginning. We measure success by what runs reliably in front of real users.
How much data do we need to get started?
It depends on the problem. Some forecasting and classification tasks work with a few thousand historical records; LLM-based solutions over your documents often need no labelled data at all. The Discover phase answers this concretely for your case.
Can you work with sensitive or regulated data?
Yes. We can work entirely inside your cloud account or on-premise, use open-weight models that never send data to third-party APIs, and follow your security and compliance processes. NDAs are standard.
Do you work with companies outside India?
Yes. We're based in India and work with clients in India and internationally, with overlapping hours for US, UK, European, Middle-East and APAC time zones.
Which cloud and LLM providers do you use?
Whatever fits your constraints — AWS, Azure, GCP or on-premise, and commercial or open-weight models. We're vendor-neutral and will recommend based on accuracy, cost and data-residency needs.
Who owns the code and models you build?
You do. Intellectual property for project deliverables transfers to you on payment, and we hand over source code, model weights and documentation.
What does a first engagement typically cost?
Most clients start with a fixed-price pilot. Pricing depends on scope and data complexity — tell us about your problem and we'll send a written proposal within a few working days.
Have a problem AI might solve?
Tell us what you're trying to improve. We'll reply within one working day with honest next steps — even if that means AI isn't the answer yet.