Forecasts embedded where the decision happens
A forecast that lives in a dashboard nobody opens is a research project. We build demand, pricing and risk models embedded in the tools where the decision is actually made — validated against your current method, honest about their error ranges, and retired without sentiment if they stop beating the baseline.
Decisions we model
Wherever a person currently decides from last year's numbers plus a hunch.
Demand forecasting
Sales, load and capacity predicted at the granularity you plan at — with uncertainty ranges, not single numbers.
Pricing & revenue
Price sensitivity and revenue impact modelled before you commit — scenario answers in minutes, not quarters.
Risk scoring
Credit, churn and fraud risk scored with explanations a reviewer — or a regulator — can follow.
Capacity & planning
Staffing, inventory and scheduling driven by forecast instead of last year plus a safety margin.
The baseline has to lose, fairly
Your current method — however informal — is the incumbent. The model deploys only if it beats it on your own history.
Name the decision and the baseline
What decision, made by whom, how often — and what the current method's error actually is. That's the number to beat.
Backtest before build
The model competes against the baseline on your historical data. If it can't win there, we tell you and stop.
Embed in the workflow
The forecast appears in the scheduling screen, the pricing tool, the planning sheet — where the decision is made, with confidence shown.
Monitor honestly
Error tracked continuously against actuals and against the baseline. Models drift; ours say so out loud.
What ships with every model
Evidence before deployment, honesty after it.
Common questions
How accurate will the forecast be?
The backtest answers that with your data before you commit — accuracy claims made any earlier are marketing. What we promise up front is the method: beat your current approach on a fair historical test, or we recommend not deploying.
Our planners won't trust a black box.
They shouldn't. That's why the forecast ships with error ranges, driver explanations and its track record visible — and why planners can always override, with overrides logged and learned from.
How much historical data do we need?
Typically 2–3 years for seasonal patterns, but the honest answer comes from the feasibility sprint — sometimes external signals compensate for thin history, sometimes they don't.
What happens when the world shifts and the model breaks?
Drift monitoring catches it: error against actuals is tracked continuously, alerts fire when it degrades, and retraining is a pipeline, not a project. In genuinely unprecedented conditions the system says "low confidence" — which is itself useful information.
A forecasting model the trading desk actually uses, daily
Embedded in the scheduling workflow and honest about its error ranges — which is exactly why it's still in use.
Read the case study →More AI solutions
Document intelligence →
Contracts, claims, invoices and filings — extracted and reviewed at machine speed.
Copilots for teams →
Sales, support and ops assistants grounded in your data — with citations and permissions.
Agentic workflows →
Multi-step processes executed end-to-end — with human checkpoints and full audit logs.
The full practice →
Everything we do in AI solutions.
What's your current forecast error?
If you don't know, that's the first finding — the diagnosis session is free.