Decisions your data already knows how to make.
Ranking, scoring and forecasting that arrive as a queue your team can work — not another chart nobody opens. Wired to a number you already report, with the evidence to show it moved.
Is this you?
“Every customer gets the same offer. Half ignore it, and I can’t tell which half was ever going to buy.”
You get a ranked list of who to reach, and a holdout that proves the lift was yours.
“Our forecast is a spreadsheet. We’re overstocked in one place and out of stock in another.”
Forecasts with a range you can plan around, inside the planning workflow your team already uses.
“We have good models in notebooks. None of them are in front of a customer.”
A path from notebook to production your team can walk again for the next ten models.
“Our rules catch the obvious fraud and drown the analysts in false alarms.”
A scored queue with the rules on top, so analysts spend the day on cases worth their time.
What changes
Your dashboard says churn is up.
Your team opens a ranked list of who to call this morning, strongest signal first.
The model’s accuracy is a number on a slide.
The model is judged on the business number it moves, per segment, every week.
Nobody notices when a model quietly goes wrong.
Drift is caught the day it starts, and the system says which segment moved.
What you get
A decision, not a dashboard
One decision picked, defined and measured — delivered as the queue or the score your team acts on.
A baseline you have to beat
We ship the simple version first, so nobody has to take the clever one on faith.
Scores where the work happens
Delivered into the tool your team already opens, fast enough that nobody waits for it.
Rules on top of the score
Thresholds, escalations and overrides the business can change without touching the model.
Proof it works per segment
Accuracy and error reported per customer group, not hidden inside a single average.
A warning before it breaks
Live monitoring on what goes in and what comes out, with an alert and a retrain path when they drift.
How it goes
Frame the decision
We pick one decision, agree the number it has to move, and audit the data behind it.
Baseline, then model
The simple version first, then the real one — both judged against the same written rubric.
Live, with rules on top
It runs alongside your current process, then takes over. The policy layer and the monitoring go live with it.
Handover and the next one
Your team owns the loop. The second model costs a fraction of the first, because the platform is already there.
A first model with the platform under it runs ₹50L–1.2Cr over 8–14 weeks. Models after that are typically ₹15–30L each. Stop, ship or extend at the end of every phase.
Questions we get on the first call
Got any questions?
Ask — a founder reads every message.
We already have a data team. What do you add?
Most data teams can build a model. Fewer have shipped the serving, the monitoring and the policy layer around one. We bring that shape; your team brings the domain — and keeps both when we leave.
What does it cost?
A first model with the platform under it runs ₹50L–1.2Cr over 8–14 weeks, depending on how messy the data is and how fast the answer has to come back. Models after that are typically ₹15–30L each, because the platform is already there.
How do we know it’s better than what we do now?
It runs alongside your current process first, on the same cases, and we compare. Nothing switches over until that comparison is written down and you have read it.
What if it’s unfair to one group of customers?
Error is reported per group from week one, not just as one average. If accuracy and fairness pull against each other, that is a decision you make in the open — not one buried inside a metric.
What happens when the world changes?
What goes in and what comes out are watched daily. When they drift past a threshold the system raises it and prepares a retrain; a person still approves it.
Can it run on our infrastructure?
Yes. It deploys into your cloud account under your access controls, and we never become something you cannot replace.
For your engineering teamarchitecture · stack · how we run it
Events, OLTP, warehouse, third-party — joined into a feature store with point-in-time correctness so training matches serving.
Champion in production, challenger in shadow. Versioned in a registry, served at p99 < 50ms, canary-deployed by default.
Rules layered on the score — thresholds, escalations, overrides. Audited and revertable, so the business can shape the decision without redeploying the model.
Drift, fairness, per-segment error, PSI/KS. The bottom lane is what makes the top lane safe in production.
Have an intelligent system to build?
Tell us about the messy bit — the legacy system, the model that won’t behave, the workflow no one wants to own. We’ll come back with a discovery plan inside two business days.
