Plainsong connects what's happening in your business to what's happening out in the world, through driver-based forecasting with a board-ready narrative built in. Unlock the insight your best analyst carries in their head: made explicit, auditable, and ready to present.
Built by a commercial finance professional, not a Silicon Valley coder.
Your best analyst understands intuitively which factors move the P&L. Their potential is wasted if they spend their time reconciling spreadsheets.
When the CFO asks "why has the forecast moved?", the best answer is a clear narrative grounded in quantitative analysis, not a finger in the air.
The day that analyst walks out, years of hard-won commercial intuition walk out with them. Nothing transferable is left behind.
A typical forecast looks like this:
Plainsong works differently. Plainsong uses machine learning and modern visual analytics so your team can bring what they know about the business straight into the forecast.
Five moving parts, one number you can defend. Pick a part to see it work.
Every series arrives with its own release calendar and forward projection, so the forecast never quietly runs on stale data.
Search interest scored below the retention threshold and was dropped from the model.
The dotted line holds the unchanged base case. Illustrative figures.
Adding drivers cut the backtest error from 5.9% to 3.2% — a 2.7% accuracy gain over trend-and-seasonality alone. Measured on six windows the model never saw.
// AI applied where it earns its place
Cantor explains what moved, drafts the commentary, and suggests drivers worth testing. The boring work, done. Ask Cantor in plain language or reach for a command.
The numbers come from Plainsong's deterministic, glass-box engine, not a language model guessing. Cantor reads those workings and puts them into words. Cantor never invents a figure, and never makes the call for you.
Combine the analyst's judgement and the machine's rigour, and you beat either on its own.
The largest review of modern forecasting finds that structured human judgement consistently improves accuracy, and that human-guided variable selection beats automated approaches when real domain knowledge is present.
How information is represented changes how well people reason. Plainsong lays the drivers out so your judgement has more to work with.
Humans directing a machine-learning system outperform purely automated ones. Plainsong is built to iterate with your expertise, not to replace it.
Accuracy only counts on data the model never saw. Plainsong backtests every forecast and never marks its own homework.
In finance, trust comes before everything. So we're clear about exactly where the line sits.
Machine learning can surface deep, statistically significant relationships with your key commercial drivers, but never replaces human judgement and intuition.
Refining the drivers becomes the team's work — using the deep knowledge of the business only they have, to evolve the model rather than update a spreadsheet. What your best analyst knows stops living in their head, and starts living in the model.
Plainsong lets your team work to their full potential: fertile ground for a strategic conversation, not another spreadsheet to reconcile.
We're working with a small group of commercial teams ahead of launch. Tell us what you forecast and, once we're ready, we'll show you how Plainsong can help.