Do you build custom models or use AutoML platforms?

Both, based on the problem. Structured tabular forecasting: XGBoost / LightGBM with careful feature engineering usually wins. Complex time series with multiple seasonality: Prophet, NeuralProphet, or Temporal Fusion Transformers. Straightforward high volume scoring: AutoML platforms (Snowflake Cortex, Vertex AI) with monitoring. We pick per problem, not per vendor.

How do you handle model drift?

Every deployed model has a monitoring job: feature drift (PSI), prediction drift (distribution shift), and outcome tracking once actuals arrive. Drift alerts trigger a retrain PR: the model doesn't silently degrade for months before someone notices.

Can you build risk scoring models regulators will accept?

Yes. For credit/insurance risk models we ship model risk documentation (SR 11-7 / OCC 2011-12 aligned), adverse action reason codes traceable to specific features, and challenger model comparisons. Model cards are part of every enterprise engagement.

What's a realistic accuracy improvement over our current forecasts?

Depends on your baseline. Teams using spreadsheet or single variable methods typically see MAPE reductions of 30 to 50%. Teams already using decent ML see 10 to 20% improvement: which at scale still translates to significant inventory/margin gains.

AI & Machine Learning

Predictive Analytics Development

Forecasts your CFO trusts, calibrated on your own history: deployed inside your Snowflake, BigQuery, or Redshift.

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What is predictive analytics?

The Answer

Predictive analytics is time-series forecasting, churn/LTV modeling, and demand planning deployed inside your existing data warehouse (Snowflake, BigQuery, Redshift). We build models calibrated on your business history that come with confidence intervals, not false precision — and each forecast surfaces which business drivers moved the number so planners trust the forecast.

What you get

Three outcomes we commit to before we start.

Confidence intervals, not point estimates

Every forecast ships with prediction intervals. Your planners see 'demand for SKU X: 4,200 units ± 380' instead of a single false precision number: and plan buffer stock accordingly.

In warehouse execution

Models run inside Snowflake, BigQuery, or Redshift via Snowpark or dbt. No data egress to external ML platforms, no separate MLOps stack, no 'why is this number different from the warehouse'.

Explainable to your business team

SHAP values or attention weights are surfaced in the dashboard: planners see which drivers moved the forecast, not just the number. Decisions get made faster because trust is earned.

The Guaranteed Production Pilot

Fixed scope · Written target

One customer graph powering Predictive Analytics across every location.

Predictive Analytics running on a single unified customer graph: personalization, recommendations, and reporting that finally agree with each other, driving measurable AOV lift.

Speed

Intelligence audit in week one. First measurable lift inside the pilot window.

Zero effort

Fully managed. We unify the data, build the system, and wire it into your existing stack: your team just watches the numbers move.

Risk reversal

We agree the lift target up front. Don't see measurable movement in the pilot window and we keep working (free) until you do.

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