VTP Altitude, Wakad, Pune, Maharashtra 411033, India
VTP Altitude, Wakad, Pune, Maharashtra 411033, India
Most "AI strategy" conversations skip straight to the model and miss the actual business problem. We start with the decision you're trying to improve — churn, fraud, forecasting, support volume — and work backward to a model worth building.
We build on your existing data, not a hypothetical clean dataset, and we tell you plainly when a rules-based system will outperform a model.
From predictive models and recommendation engines to LLM-powered automation and internal copilots, we ship models into production and monitor them, not just a notebook that proves a concept.
Years building software
Projects delivered
Client retention
Engineers on staff
We start with the business decision a model needs to improve, not a model looking for a use case.
Models get shipped into production and monitored, not left as a notebook that proves a concept.
Accuracy gets tracked after launch so performance doesn't quietly degrade over time.
We agree on a delivery date before work starts and build the plan around hitting it.
You get documentation and training, not just a finished product to figure out alone.
Working with us means fewer surprises and more control over the outcome, from the first estimate to the final handoff.
Fixed-scope pricing agreed before work begins
Weekly check-ins so you always know where things stand
Full documentation handed over at project close
No — most of our engagements start with messy, real-world data. Part of the first phase is assessing what you actually have and what needs cleaning before a model is worth training.
We estimate the cost of the current manual process against the cost and accuracy of a model, and only recommend building when the math clearly favors it.
We set up monitoring for model drift and accuracy from day one, and offer ongoing retraining support so performance doesn't quietly degrade after launch.
Both. Sometimes the fastest path to value is calling an existing API well; other times a custom model is what the accuracy or cost actually requires. We recommend based on the problem, not a default preference.
We agree on a minimum viable accuracy before training starts, so "this isn't good enough yet" is a decision point built into the plan, not a surprise at the end.
1. Problem Framing
We define the exact decision or metric a model needs to move, not just "use AI somewhere."
2. Data Assessment
We audit what data you actually have and what it will take to make it model-ready.
3. Model Development
We test the simplest approach that could work before reaching for something heavier.
4. Validation & Testing
Models get validated against real outcomes, not just a held-out test set.
5. Deploy & Monitor
We ship into production and monitor for drift, not hand off a notebook and disappear.
Forecasting, churn and risk models trained on your real data, not a generic industry benchmark.
Copilots, summarization and document automation built around your actual workflows.
Defect detection, inspection and image classification for physical and visual workflows.
Personalization and recommendation systems tuned to a metric you actually care about.
Years of experience
Building and supporting business-critical software.
Projects delivered
Across web, mobile, cloud and data platforms.
Client retention
Most clients stay on for ongoing support and new work.
Specialists on staff
Engineers, designers and analysts under one roof.
