01
Demand forecasting for retailers with stockouts and overstocks costing real money weekly.
Fits you
ML feasibility calls open
−38%
Inventory reduction
92%
Forecast accuracy
0
Stock-outs, top 500
−38%
Predictive Analytics
Your data is trying to tell you next month's revenue. Are you listening?
Demand forecasting, churn prediction, maintenance prediction, dynamic pricing — the business value is in anticipation, not reporting.
RetailManufacturingBFSISaaSLogisticsTravelRetailManufacturingBFSISaaSLogisticsTravelRetailManufacturingBFSISaaSLogisticsTravel
Who this is for
Prediction problems with clear ROI.
If any of these sound like you, we should talk.
02
Churn prediction in SaaS, telecom, insurance — where retention CAC is 5-7× acquisition CAC.
Fits you
03
Predictive maintenance in manufacturing where unplanned downtime is ₹ crores / hour.
Fits you
04
Dynamic pricing in travel, hospitality, ride-hailing.
Fits you
The proof
The data → decision pipeline.
LAYER 1
Feature store
Centralised, versioned features. Same feature in training and serving — no skew.
LAYER 2
Model registry
MLflow / Vertex. Every experiment tracked, every model reproducible.
LAYER 3
Monitoring
Drift detection, prediction latency, business-metric alerts. Models degrade silently. We make that loud.
What we build
Prediction use cases we've shipped.
01
Demand forecasting
SKU × store × week.
02
Churn / attrition
User, employee, customer.
03
Predictive maintenance
Equipment failure, preventive schedules.
04
Dynamic pricing
Real-time competitive, elasticity-aware.
ClientA retail chainRetail
<em>38% inventory reduction</em>, zero stock-outs on top 500 SKUs.
Demand forecasting at SKU × store × week. Replaced a Excel forecast with a proper ML pipeline. Buying cycles shifted from 4 weeks to 2.
01
38%
Inventory reduction
02
0
Stock-outs
03
92%
Forecast accuracy
04
₹0Cr/year
Working capital freed
What it's like working with us
We stopped buying based on vibes. We started buying based on next Tuesday's forecast. Working capital went down 38%.
SB
Suresh B.
COO · A retail chain
Retail · Pan-India
Tech stack
The tools. Chosen for your reasons.
09technologies in rotation
01Python
02Prophet
03XGBoost
04LightGBM
05PyTorch Forecasting
06MLflow
07Feast
08Airflow
09ClickHouse
Process
How we actually work.
06 stages
- 01
Use-case prioritisation
Weeks 1-2Which prediction has highest ROI and enough data?
- 02
Data readiness
Weeks 2-4Historic data quality, gaps, seasonality.
- 03
Baseline model
Weeks 4-6Simple first — beat it before going complex.
- 04
Production model
Weeks 6-10Better accuracy, pipeline, monitoring.
- 05
Integration
Weeks 10-12Wire into business workflow (ERP, BI, alerts).
- 06
Watch + retrain
Week 12+Drift detection, quarterly retraining.
Questions
Answers, without the fluff.
Still have questions? Talk to us — we answer within a business day.
07common questions
01How accurate is "accurate"?
Depends on the problem. Demand forecasting: 8-15% MAPE is excellent. Churn: 70-85% AUC. We set realistic targets based on problem complexity.
02Do we need a data scientist on our side?
Helpful, not required. We'll work with your analyst / ops lead. For ongoing retraining, we can operate the pipeline remotely.
03What if the model is wrong?
Models are always somewhat wrong. We ship with confidence intervals, degrade-gracefully fallbacks, and human-override paths for high-stakes decisions.
04How long does a typical engagement take?
Most projects run 10-18 weeks from kickoff to production launch. We share a milestone plan in week one and update weekly.
05Do you sign an NDA?
Yes. Standard mutual NDA on request, before the first technical conversation.
06Who owns the code and IP?
You do. Code is in your GitHub org from day one. All IP transfers unambiguously on delivery.
07What does your pricing model look like?
For v1 builds: fixed scope, fixed milestones. For ongoing work: monthly retainer with a defined team. We don't do time-and-material surprise billing.
Related
Explore more of what we do.
Predict the things that matter.
Tell us a business problem. We'll tell you if data + models can solve it.
Free ML feasibility call.