Felix predicts revenue eight weeks ahead, retrained nightly on your accounts. Confidence narrows over time as he learns your business.
An analyst builds a forecast on Monday. By Wednesday, the inputs have shifted. By Friday, nobody trusts the numbers. Repeat weekly.
Pulling data from five platforms, cleaning it, running regressions, sanity-checking. 15 to 20 hours per forecast cycle that could be 15 seconds.
Last quarter's forecast was off by 18%. Did the model learn? No. Same model, same blind spots, same miss. Just with newer data.
Seasonal shifts, competitor moves, creative fatigue. A spreadsheet cannot detect emerging trends three weeks before they hit revenue.
Each capability maps to a specific job: predict the line, learn from the miss, surface the trend, alert on the anomaly, and stay aware of the rest of the workforce.
Channel-level predictions with confidence intervals that narrow as Felix learns your business.
Example. $420K-$460K next month at 87% confidence
Every prediction gets scored against reality. Felix dissects misses and never makes the same one twice.
Example. 78% accuracy on day one, 91% by week eight
Spots emerging patterns two to three weeks before they show up in your dashboard.
Example. Hook fatigue flagged 18 days before performance dropped
When reality diverges from forecast, Felix surfaces it within 24 hours, not at month-end close.
Example. Spend spike at 6:47 AM, resolved by noon
Reads Maya's brand context, Sam's scenarios, Dana's reconciled data. No silo, no manual reconciliation.
Example. Sam tested a 30% Meta shift; Felix priced it in 4 seconds
One workweek, hour by hour. Specific numbers vary by account.
Runs nightly retrain on the last 12 months of account data. Eight models in ensemble, one prediction per channel.
Forecasts revenue 8 weeks out. Channel-level ranges, confidence narrows on Meta and Google, widens on TikTok.
Detects creative fatigue forming on Hook_v4. Routes to Olivia for variant generation before performance drops.
Anomaly: spend up 34% overnight. Cross-checks with Parker. 23% real lift, 11% platform inflation.
Sam asks for a +30% Meta shift scenario. Felix prices the outcome in 4 seconds with confidence bands.
Re-scores last week's predictions against actuals. Off by 4% on Wednesday; logs the miss for next cycle.
Delivers the weekly recap to Slack. Three forecasts logged with confidence bands, two anomalies surfaced.
Forecast accuracy is not a static number. Felix's predictions compound against your business, and the gap closes every cycle.
Week 1
Cold start on your historical data. Baseline ensemble across ARIMA, Prophet, Greykite. Already sharper than spreadsheet forecasts.
Week 4
First prediction cycles get scored against actuals. Felix identifies which features drove the gap and adjusts weights.
Week 8
Compound effect kicks in. Cycles reinforce each other. Confidence bands narrow on stable channels.
Week 24+
Mature on your account. Predictions read as decisions, not guesses. Maya holds the why, Felix holds the what.
ARIMA + Prophet + Greykite ensemble, nightly retrain
Felix runs on a purpose-built forecasting stack: ensemble models, nightly retraining, scored backtests, and shared memory across the workforce.
Meta, Google, TikTok, and Shopify via OAuth. Dana feeds reconciled, deduped data so Felix never trains on noise.
OAuth-based, no warehouse copy
Slack for daily anomaly alerts, Sheets for weekly forecast tables, Notion for the long form. Format adapted per surface.
Anomaly alerts within 5 minutes of breach
Maya supplies brand memory and CAC ceilings. Parker validates spend lift. Sam runs scenarios. Dana keeps inputs clean.
Shared memory, not message-passing
ARIMA for seasonality, Prophet for trend changepoints, Greykite for the long horizon. Eight models in ensemble, one confidence-scored output.
Nightly retraining, scored backtests every cycle
Ready when you are
The pilot connects your accounts to all seven agents. Felix's first forecast finishes in under five minutes.
Looking for something specific? Parker validates lift →, Dana keeps inputs clean → or Sam tests scenarios →.
