Predictive AI Solutions

Know what happens next, before it happens.

Demand, churn, fraud, failure, price: production-grade predictive models built on your data, deployed into your workflows, retrained as the world changes.

Forecast your first KPI →See how it works

30+ predictive models live in production across Retail, BFSI, Energy, and Manufacturing

Trusted across

BFSIHealthcareRetailManufacturingTelecomEdTechReal EstatePharmaLogisticsGovernmentBFSIHealthcareRetailManufacturingTelecomEdTechReal EstatePharmaLogisticsGovernment

The Problem

Most enterprises drown in data,
then decide on instinct.

Companies have 5+ years of operational data, but still plan quarter-to-quarter on gut feel and Excel rollups. By the time the dashboard shows churn, a stockout, or a failed asset, it's already a problem.

01

Reports are rear-view

BI shows what happened. Nobody tells you what's about to happen: which customer will leave next week, which machine will fail tomorrow.

02

Models live in notebooks

Your data scientists build them. Nobody outside the team can use them. The model never reaches the decision it was built for.

03

Static rules age fast

Hand-tuned thresholds break the moment the market or your customer behavior shifts. The world changes; the rules don't.

04

Forecasts arrive too late

Monthly forecasting cycles miss daily decisions: pricing, replenishment, fraud, maintenance. By the time the report lands, the window is gone.

Reactive vs predictive: chaotic alerts vs ranked priority queue

What We Build

What we build

Five predictive solutions, each shipped as a productionized model with monitoring, retraining, and drift alerts. Not a notebook handed over Slack.

01

Demand & Sales Forecasting

SKU-store-day granularity, promo and weather-aware, retrained weekly. Plugs straight into S&OP, replenishment, and finance planning workflows.

02

Churn & Lifetime Value Models

Predict who leaves, who grows, who to invest in. Scores pushed into your CRM as a column, not a slide deck.

03

Risk & Fraud Scoring

Credit, transaction, claim, application: real-time scoring with SHAP explainability. Approve faster, decline smarter, audit every decision.

04

Predictive Maintenance & Anomaly Detection

IoT, telemetry, sensor data: catch failures days before they happen. Cuts unplanned downtime, extends asset life, prioritizes technician routes.

05

Price & Yield Optimization

Elasticity-aware pricing models. Dynamic price, demand response, occupancy yield, tuned to your margin and growth targets, not a vendor's.

How We Deliver

From scoping to production
model in 4-8 weeks.

Predictive AI fails when it stops at a notebook. We deliver models that get used: wired into the decisions they were built for, monitored, and retrained on schedule.

01

1-2 weeks

Data & Problem Framing

We audit data quality, define the target variable in business terms, and agree on success metric (AUC, MAPE, lift) and decision threshold up front.

02

2-4 weeks

Modeling & Validation

Feature engineering, baseline, model selection, backtesting against your history. Champion-challenger comparison vs your current heuristic or forecast.

03

2-3 weeks

Production & Integration

Deployed as API or pushed into your data warehouse / CRM / ERP. Monitoring, drift detection, automated retraining schedules: wired into your stack.

04

Ongoing

Decision Adoption & Improvement

We measure not just model accuracy, but the business decision quality, and tune threshold, retrain cadence, and feature pipeline as the world changes.

Industries & Use Cases

Built for high stakes,
high volume decisions.

We focus on industries where every percentage point of forecast accuracy or fraud reduction translates directly to margin, risk, or customer outcome.

IndustryUse CasesOutcome

Retail & E-commerceSKU-level demand forecasting, churn, dynamic pricing, return prediction, basket recommendation25% inventory holding reduction

BFSICredit-risk scoring, transaction fraud, customer attrition, propensity-to-buy, collections priority35% fraud loss reduction

Energy & UtilitiesLoad forecasting, asset-failure prediction, theft and leakage detection, demand response20% fewer unplanned outages

ManufacturingPredictive maintenance, yield prediction, scrap minimization, supply-disruption forecasting40% lower unplanned downtime

Travel & HospitalityOccupancy forecasting, dynamic pricing, no-show prediction, customer LTV scoring12-18% RevPAR uplift

TelecomChurn prediction, network anomaly forecasting, plan recommendation, NPS-risk scoring30% churn reduction

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Impact & Outcomes

What predictive AI
actually moves.

Typical 12-month outcomes from our deployments, vs the customer's prior baseline: manual forecasts, heuristics, or last-year-plus-X planning.

25-0%

Forecast accuracy lift vs prior baseline

Reduction in unplanned downtime

Lower fraud loss

4-0 weeks

To first production model

Data pipeline uptime in production

3-0x

Decision velocity vs manual cycles

Tech Stack & Trust

Open architecture.
No lock-in.

Model and platform-agnostic. We use what fits your data and stack.

SOC 2 Type IIISO 27001EU AI Act–readyGDPR compliant

ModelingXGBoost, LightGBM, Prophet, PyTorch, TensorFlow, scikit-learn; foundation time-series models (Chronos, TimesFM) where they outperform

Data PlatformsSnowflake, Databricks, BigQuery, Redshift, Microsoft Fabric

Orchestration & FeaturesAirflow, Dagster, Prefect; feature stores like Feast and Tecton

MLOpsMLflow, Vertex AI, SageMaker, Azure ML; production monitoring via Evidently and Arize

IntegrationREST APIs, native connectors for Salesforce, SAP, NetSuite, Oracle, ServiceNow

TrustSHAP-based explainability, full lineage, drift alerts, model cards, DPDP/GDPR compliant

Case Study

CASE

Spotlight: National Retailer, 400 Stores

How a national retailer cut stockouts 40% while reducing inventory 18%.

We built a SKU-store-day demand forecasting system covering 80,000 SKUs across 400 stores, with weekly retraining and promo- and weather-aware features. The model replaced manual planner heuristics. Planners now spend their time on exceptions, not Excel.

40% fewer stockouts18% lower inventory holding2x planner productivityMAPE: 38% → 14%

Common questions.

BI tells you what happened. Predictive tells you what will happen, and what to do about it. They're complementary. We often deploy predictive outputs back into your existing BI tool so the same dashboard shows actuals and forward-looking forecasts side by side.

Yes. We bring the team: data engineering, modeling, MLOps. You bring the domain context and access to data. After production, we hand over runbooks and train your IT to operate. If you prefer, we keep ongoing model stewardship as a managed service.

We commit upfront to a measurable lift over your current baseline (manual forecast, heuristic, last-year-plus-X). If the validation phase doesn't beat that baseline, you don't pay for the productionization phase. We're aligned on outcome, not effort.

Most of our projects start there. We spend the first 1-2 weeks on data audit and pipeline cleanup. Often, fixing the data alone unlocks 60% of the gain, before any modeling. We tell you exactly what data quality work is needed before any model commitments are made.

Decide forward,
not backward.

Pick one KPI you'd want to see 4 weeks ahead. We'll show you a working forecast on your own data in 14 days.

Start with one KPI →