Production ML for forecasting, churn, risk and pricing — trained on your data.
We build predictive models that ship — with the MLOps, monitoring and drift detection needed to keep them accurate for years, not weeks.
Most ML projects stall in a Jupyter notebook. T7 Solution treats models as products: version-controlled, tested, monitored, and continuously retrained. We build the feature store, training pipeline, serving layer and observability alongside the model.
We work across classical ML (gradient boosting, time-series, causal inference) and deep learning — choosing the simplest approach that meets the business SLA. Every model ships with a business dashboard that translates predictions into decisions and dollars.
Our team has shipped forecasting, churn, fraud, credit-risk, pricing and recommendation systems across retail, manufacturing, fintech and healthcare.
Demand, inventory, revenue and staffing forecasts at daily/hourly granularity.
Churn, propensity, fraud, credit-risk and lead-scoring models.
Personalised product, content and next-best-action recommendations.
Uplift modelling, marketing-mix and attribution beyond correlation.
Feature store, CI/CD for models, canary rollout, monitoring and rollback.
SHAP, LIME and business-friendly explanations for every prediction.
Cut stock-outs and overstock across SKUs, stores and warehouses.
Identify at-risk customers weeks early and route retention offers.
Real-time scoring for payments, claims and account takeover.
Price optimisation that respects margins, competition and inventory.
Retail / Consumer
A 220-store retailer was losing margin to stock-outs on fast-movers and overstock on seasonal SKUs. Existing forecasts were spreadsheet-driven, updated weekly, and blind to promotions or weather.
T7 built a hierarchical demand forecasting system covering SKU × store × day with promotional and weather features. Predictions push nightly into the merchandising system and feed a replenishment dashboard for category managers.
Forecast error dropped by 27%, stock-outs on top-100 SKUs fell by more than a third, and working capital tied up in slow inventory shrank by ~$4.1M in the first year.
"We had forecasts before. What T7 gave us is a decision system — category managers now trust the numbers enough to change how they buy."
We start from the business decision, not the algorithm.
Feature engineering, baseline model and honest error analysis.
Serving, monitoring and canary rollout in 4–8 weeks.
Drift detection and automated retraining cadence.
Depends on the problem. Simple churn/propensity often needs 6–12 months; forecasting needs 2+ years of history. We tell you honestly if data is insufficient.
Not always. We can start with CSV extracts, but a warehouse (Snowflake, BigQuery, Databricks) makes production models far easier to operate.
Automated data + prediction drift monitoring, retraining triggers and champion/challenger evaluation.
Yes. We often partner with in-house teams and focus on the MLOps, evaluation and production layers.
We define the business metric first — forecast MAPE, churn precision at top-decile, fraud recall at fixed false-positive rate — then report it in a live dashboard and on monthly QBRs.
Whichever wins on your data. For tabular problems, gradient-boosted trees (XGBoost, LightGBM) usually beat deep learning. For sequences, images or text we go deep. We benchmark honestly.
AutoML gives you a model. We give you a decision system — feature store, drift monitoring, canary rollout, explainability, business dashboard and a retraining cadence — which is where 80% of ML value lives.
Yes — via low-latency APIs, batch writes to your warehouse, Kafka streams or direct pushes into Salesforce, HubSpot, SAP or your app. Predictions land where the decision is made.
Every prediction ships with SHAP-based attributions and a plain-English reason code. For BFSI and healthcare, we produce model cards, bias audits and validation packs your risk team can defend.
Typically a small monthly compute + storage bill plus a retainer for monitoring, retraining and model upgrades. Most clients see the model pay for itself in 3–6 months on the business KPI.
AI-native automation that reads, decides and acts across your systems.
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Use cases we ship it in, industries that buy it, insights behind it, and comparisons to reason through.
SKU-level forecasts that release working capital, not just improve MAPE.
Real-time fraud scoring across payments, claims and identity.
S&OP-grade forecasting, replenishment and scenario planning.
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Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.