Custom machine-learning models trained on your data — revenue, demand, churn, anomalies — delivered as live dashboards and APIs your team can act on. Every forecast comes with a confidence band, a backtest report, and drift monitoring so you'll know before the model starts to slip.
If your "forecast" is a spreadsheet someone updates on a Sunday, you'll recognise these. We've cleaned up plenty.
A junior analyst extrapolates last year's growth, hard-codes it as a percentage, and the board treats it as a fact. No seasonality. No drivers. No confidence.
"We'll do R12.4M next quarter." Will you? With what probability? Plus-or-minus what? A forecast without a confidence interval is a fortune cookie.
Reports tell you what already happened. Forecasts tell you what's coming. Most BI tools do (a) brilliantly and (b) not at all. You need both.
An AutoML tool spits out a prediction. Nobody can say why it predicted that. When it's wrong (and it will be), nobody knows what to fix.
If the question is "what will happen?" we can probably build a model for it. These eight cover ~85% of what SA businesses ask for.
Daily, weekly, monthly revenue by product/region/channel. With seasonality + promo lifts.
Per-customer probability of churning in the next 30/60/90 days. With top features driving the risk.
SKU-level demand for inventory + production planning. Right size your stock without stockouts.
Per-lead probability of converting + estimated deal size. Route the hot ones to your best reps.
Catch fraud, ops outliers, data quality issues before they hit the bottom line. Streaming + batch.
Call-centre volume by half-hour, store footfall, ops team load — staff to demand instead of guessing.
Elasticity models that recommend the price-per-product to maximise revenue, margin, or volume.
Daily cash position + 13-week rolling forecast. Knows your debtors' DSO patterns and seasonality.
Every Sitect model is auditable. You see the prediction, the confidence interval, the top features driving it — and a backtest of how accurate the model would have been on every past month.
/forecast from your app and embed predictions wherever they matter.We don't have a religion about modelling. We pick from the methods below based on your data shape, the horizon, and the explainability you need.
Solid baseline for trends with clear seasonality (weekly, monthly, holiday patterns). Fast to train, easy to explain to non-technical stakeholders.
Workhorse for forecasts with lots of features (price, weather, promos, day-of-week). SHAP values give per-prediction explainability your CFO can challenge.
For high-volume, long-horizon, or multivariate problems where classical models leave accuracy on the table. Used selectively — we don't over-engineer.
When the question is "how likely is X to happen for this user?" we use probabilistic classifiers calibrated to deliver real probabilities — not just rankings.
Unsupervised detection of unusual patterns — transactions, sensor readings, conversion drops. Configurable per-metric thresholds, streaming or batch.
Best predictions usually come from blending. We weight models by their backtest performance, and use an LLM to translate the numbers into board-ready commentary.
Five stages from raw data to your dashboard. Every stage is versioned in Git, every model run is reproducible, every prediction is logged.
You don't pay a per-prediction fee or a SaaS subscription. The pipeline runs in your infrastructure; provider tokens billed direct.
How accurate the model would have been on the last 24 months — MAPE, MAE, RMSE, plus the cases where it would've been most wrong. Read before signing off.
The model file + the exact code + the exact data slice that trained it. Re-run in one command. Roll back to any prior version instantly.
POST request, JSON response. Returns the prediction, the confidence interval, the top features driving it. Sub-100ms typical latency.
Metabase / Looker / custom React component — pick one. Pre-built tiles for the forecast, confidence bands, top features and recent prediction history.
Input data PSI score tracked daily. Output distribution monitored. When either drifts, you get a Slack alert + auto-PR to retrain.
Markdown runbook for the top incidents: model accuracy drop, drift fired, data source down. 2-hour live training for your data + product teams.
You'll spend ~12 hours of stakeholder time across the whole build. Most of it in week 1 (data audit) and week 5 (backtest review).
Map the data. Find the gaps. Quantify the signal. Stop here if the data is too sparse — saves you R22k.
Build the feature pipeline in dbt + SQL. Version it. You can read it and challenge it.
Multiple model families benchmarked. Backtest report drafted. You see the wins and losses before sign-off.
API deployed, dashboard live, drift monitor armed, Slack channel connected. Pilot users using it.
Adjust thresholds, train your team, document the runbook. 90 days post-launch monitoring included.
Median results across our deployed forecasts. Your numbers will be yours — these are the order-of-magnitude bracket to plan against.
Build fee is fixed in writing. Compute + cloud hosting bills direct to your AWS / Azure / GCP — typically R800–R3,500/mo depending on data volume. No per-prediction fee.
If yours isn't here, send us a sample data extract — we'll come back with a feasibility note in 48h. No sales pitch.
Drop a CSV or a SQL extract into our secure form. Within 48 hours one of our seniors writes a one-pager: signal strength, expected MAPE, recommended approach, indicative timeline + price. No sales pitch — sometimes the answer is "your data isn't ready yet, fix this first."