SalesForecast – The Forecasting API That Powers Your AI

Use Case: Platform Lead — Forecast Enterprise AI Token Usage Before the Budget Breaks

Every business unit is adding copilots, agents, and AI-assisted workflows. Token consumption is now one of the fastest-growing line items in the technology budget, and it does not move smoothly: weekly usage cycles, adoption ramps, and step changes when a new team onboards. Finance wants a spend forecast. Platform wants to know when usage crosses the rate-limit ceiling. Spreadsheets handle neither.

The problem

An enterprise platform team is six months into an AI rollout. Daily token usage has nearly doubled, a new support organization just onboarded and added a visible step to the curve, and the CFO wants a defensible number for next quarter. Naive trendlines miss the weekly cycle (weekend usage drops by a third) and overreact to the onboarding step. The team needs a forecast that separates structural growth from noise, with confidence intervals that can back a budget commitment.

What this looks like in Claude

Geneva ships a native MCP server, so Claude calls the forecasting engine directly from a conversation. Get a free API key at portal.roadmap-tech.com, add Geneva to Claude as a connector, and the workflow looks like this. The numbers below are a real Geneva run, not mock copy:

Claude · Geneva MCP connected

You: Forecast our daily AI token consumption for the next 30 days from the attached platform export. Flag when we cross the 1.8B tokens/day rate-limit ceiling and project monthly spend at our blended $4/M internal rate.

⚙ geneva.forecast · 90d history · horizon: 30 days · select: auto · done in 4s

Claude: Adoption is compounding. Forecast is 47.0B tokens over the next 30 days, +22% vs the trailing 30, roughly $188K at your blended rate. Geneva separated the weekly cycle from the adoption ramp and the support-org onboarding step. Midweek peaks first cross your 1.8B/day ceiling on day 18; if you do nothing, every midweek day from then on is a throttling risk. Snapshot:

ROADMAP TECHNOLOGIES | Geneva Forecasting Engine

Forecasting · Demand Planning · Analytics Automation

June 10, 2026 · FinOps Snapshot
90d history · 30d horizon · 80% CI

AI token usage · 30-day daily forecast

Enterprise rollout · weekly cycle + adoption ramp auto-detected

MAPE 3.53%

Next 30 days

47.0B tokens

+22% vs trailing 30d

Forecast spend

$188K

Blended $4 / M tokens (internal rate)

Peak day

1.90B tokens

D+25 · first ceiling break D+18

Band width

±66M

30 calibration residuals

Day	Fcst	Low	High
D+1	1,054M	988M	1,121M
D+4	1,618M	1,551M	1,684M
D+18 (ceiling break)	1,806M	1,740M	1,873M
D+25 (peak)	1,899M	1,833M	1,966M
30-day total	46,977M	44,983M	48,972M

Model: Non-Linear Regression (Type 2), auto-selected · Transform: Seasonal · MAPE: 3.53% in-sample · RMSE: 49.5M tokens · CI: 80%

What Geneva is doing under the hood

Geneva backtests its model library against the 90-day history and auto-selects the best fit, here a non-linear regression with a seasonal transform that captures both the weekly usage cycle and the accelerating adoption trend. Every forecast ships with 80% confidence intervals calibrated on actual residuals, which is what turns “usage is growing” into “we cross the ceiling on day 18, with this much uncertainty.”

The actual value

Budget you can defend: a spend forecast with intervals, not a trendline. Finance gets $188K ± a quantified band, refreshed daily.
Capacity before throttling: the ceiling-crossing date arrives two-plus weeks in advance, time to negotiate rate limits or stagger workloads instead of firefighting.
Chargeback per business unit: run the same call per BU series and allocate forecast spend to the teams driving it.
Renewal leverage: walking into a vendor negotiation with a calibrated 12-month consumption forecast beats guessing a commit tier.

Try it on your own data

Export daily token usage from your platform dashboard, point Claude + Geneva at it, and ask the same question. First forecast in under five minutes.

For teams that want per-BU chargeback wired into the FinOps workflow, Contact RoadMap. A diagnostic engagement is two to three weeks against your data.