// AI Agents

A finance team that never sleeps.

Drivepoint AI agents work alongside your team — answering questions, drafting variance commentary, flagging anomalies, and rebuilding scenarios on demand. Grounded in your data, not generic models.

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Inside AI Agents

01 — Ask anything

Your AI analyst, on demand.

Ask in plain English. “Why did Q3 contribution margin drop?” “What was Amazon CAC last week vs. trailing 8?” The agent queries your live model, shows the work, and links to the cells it used.

  • Natural-language Q&A across the model
  • Every answer cites its source cells
  • No SQL, no formula building
Why did Q3 contribution margin drop?
✨ Drivepoint AI

Contribution dropped 2.1 pts in Q3, mainly:

  • Amazon promo mix · −1.4 pts [REV.AMZN.Q3]
  • 3PL rate increase · −0.5 pts [OPEX.LOG.Q3]
  • Returns spike, OATS-12 · −0.2 pts [RTN.SKU12]
Revenue
draftUp $214K on Prime Day; underlying DTC flat WoW.
✓✎
Gross margin
draftDown 1.4 pts, driven by Amazon promo mix.
✓✎
Opex
acceptedHeld flat; agency fee reclassified to brand.
✓
Cash
draft$4.2M EOP; runway extends to Q3 FY27.
✓✎
02 — Variance commentary

First drafts, not blank pages.

Every month, the agent drafts the “what changed and why” for every line item — sales, GM, opex, cash. Edit, accept, send. The next month it remembers your tone and the framings your board cares about.

  • Drafts variance commentary line-by-line
  • Learns your house style and tone
  • You stay in control — accept or rewrite
03 — Always-on agents

Watching while you sleep.

Background agents monitor anomalies, threshold breaches, connector health and data quality 24/7. When something matters, you hear about it — with context, a suggested action, and a one-click handoff to a human.

  • Anomaly + threshold + data-quality monitors
  • Slack and email handoff with context
  • You configure the rules, in plain English
Anomaly
●
Watching · 14 metrics
Connectors
●
All 12 healthy
Threshold
●
Meta CPM +38%
Data quality
●
99.4% match
✨
Sent to #finance-alerts2 min ago · context attached
✨“Build me a recession case — flat DTC, −20% wholesale, hold opex.”
→ Generated · “Recession FY26”
DTC ordersflatauto
Wholesale revenue−20%auto
Opexholdauto
Implied EBITDA$48Kcomputed
Save scenarioTweak
04 — Scenario builder

“Build me the recession case.”

Describe a scenario in a sentence. The agent branches the model, sets the drivers, runs the math, and hands you back a clean side-by-side. Tweak it, save it, share it — or throw it away, no harm done.

  • Plain-language scenario generation
  • Branches a real, editable scenario
  • You review every assumption before it lands
// OUTCOMES

What changes when an AI analyst joins the team.

01

Get answers in seconds, not stand-ups.

Ask in plain English; the agent queries your live model, shows the work, and cites the cells. No more waiting two days for an analyst to “look into it.”

02

Variance commentary, drafted for you.

Every month the agent writes the first pass — line by line, in your tone. You edit and accept. Reporting goes from blank-page panic to twenty-minute review.

03

Anomalies caught before they hurt.

Watchdog agents monitor connectors, drivers and cash 24/7. The first sign of drift lands in Slack, not in next month’s board pack.

04

Scale your team without hiring.

One analyst on Drivepoint covers what three did on spreadsheets. The repetitive work goes to agents; your people focus on the calls only humans can make.

// THE PLATFORM

Explore the full platform.

Drivepoint is a single platform with five pillars. They share one model, one data layer and one set of permissions, so the work flows.

Data & Context

Connect your financials, ops, and market data.

Explore →

Modeling

Driver-based scenarios your whole team can use.

Explore →

Reporting

Board-ready reports, generated and distributed.

Explore →

AI Agents

Agents that answer, draft, and watch, 24/7.

Explore →

MCP Server

Ask your numbers anything, right inside Claude.

Explore →
// FAQ

AI agent questions, answered.

What finance teams ask before they put Drivepoint agents to work.

Drivepoint's AI agents do four things. First, they answer finance questions in plain English against your live model: “Why did Q3 contribution margin drop?” gets a cited, cell-level answer in seconds. Second, they draft variance commentary for every line item automatically, so the monthly reporting cycle starts with a first draft instead of a blank page. Third, they watch your data 24/7 and flag anomalies, threshold breaches, and connector issues before they become problems. Fourth, they build scenarios on demand: describe an assumption change in a sentence and the agent branches the model, sets the drivers, and hands back a side-by-side comparison for your review.

Generic AI tools like ChatGPT or Claude are powerful, but they are not trained on your business. When you ask them a finance question, they reason from general knowledge, not your actual model, actuals, or historical data. Drivepoint's AI agents are grounded in your live data. Every answer cites the specific cells in your model it used. Drivepoint also ships with 20+ pre-built finance skills for consumer brands, covering cohort math, margin analysis, retail forecasting, and inventory planning, so the AI understands retail deductions, new-vs-returning customer logic, and multi-channel margin structures without you having to define them from scratch.

Yes. Drivepoint's AI works on your data only: it is not trained across customers, and it is not sending your numbers to a shared model. Your data, context, and finance skills are isolated to your instance. Drivepoint maintains full data lineage and audit trails, so every AI-generated answer is traceable to its source. Permissions in Drivepoint are granular: row-level, tab-level, and scenario-level, so the AI only answers within the boundaries your team defines.

Drivepoint AI takes over the repetitive, mechanical work that consumes most of an analyst's time: rolling forecasts forward, generating variance commentary, monitoring data quality, and answering ad hoc questions from the model. That frees human analysts to focus on interpretation, business partnering, and decisions that require judgment. Most Drivepoint customers find that a lean finance team, sometimes one or two people, can cover the workload that previously required a larger team. The AI handles volume; your people handle nuance.

Yes. Drivepoint's data and context layer is AI-model-agnostic. Your data, finance skills, and model context sit above the LLM layer, so you can work with Claude, ChatGPT, Gemini, or whatever frontier model your team prefers. Drivepoint integrates directly with Claude and other tools, so you can ask finance questions in the AI interface you already use, backed by your Drivepoint data. If the AI model landscape shifts, you do not have to rebuild your data or context layer.