Drivepoint connects all your data into one clean, structured layer your AI can reason over instantly. Pre-loaded with finance skills built for consumer brands, this is the foundation that makes agentic reporting, conversational analysis, and automated financial workflows actually work.
Pick from a library of pre-built integrations across eCommerce, finance, logistics, advertising and retail. Most brands are connected end-to-end inside a day, not a quarter — and we maintain the pipes so your team doesn’t have to.
Most AI projects stall because the data underneath isn’t ready. Drivepoint cleans, organizes, and shapes every input into a form your agents can reason over instantly. Lineage, version history, and permissions built in. Every number traceable, every answer defensible.
20+ pre-built finance skills. The cohort math, margin analysis, retail forecasting, and operational work consumer brands always end up doing. None of it your team has to teach an AI from scratch.
Claude, ChatGPT, Gemini, whatever’s next. Your data, context, and finance skills sit above the model layer. Switch the AI without losing what you’ve built.
See your full P&L, cash flow, and inventory position any day of the month, not just at close.
Save up to 40 hours of work every month and avoid hiring extra headcount.
The reason most finance AI experiments stall: the data isn’t ready. With Drivepoint, your agents start on clean, finance-aware data from day one.
Your data, context, and skills aren’t bound to any one LLM. When the model landscape shifts, you don’t rebuild.
Drivepoint is a single platform with five pillars. They share one model, one data layer and one set of permissions, so the work flows.
Connect your financials, ops, and market data.
Explore →Driver-based scenarios your whole team can use.
Explore →Board-ready reports, generated and distributed.
Explore →Agents that answer, draft, and watch, 24/7.
Explore →Ask your numbers anything, right inside Claude.
Explore →What finance and data teams ask before connecting their stack to Drivepoint.
Drivepoint has 75+ prebuilt integrations. The ones that matter most for consumer brands include Shopify, Amazon, and TikTok Shop (eCommerce), Target, Walmart, Whole Foods, Sephora, Ulta, and Costco (retail portals), NetSuite, QuickBooks, and Xero (accounting and ERP), Cin7 (inventory), and Meta and Google Ads (marketing). For DTC-first brands, Shopify plus your accounting ERP typically covers 80% of the model. Brands expanding into retail add the relevant portal connections on top.
Most brands are connected end-to-end within a day, not weeks or months. You pick your integrations from the library, authenticate, and Drivepoint begins syncing. Pre-built aggregations handle the common finance use cases (orders, revenue by channel, COGS, inventory), so you are working from structured, finance-ready data, not raw API output. Drivepoint maintains the integrations after setup, so your team does not manage pipelines.
Drivepoint cleans, normalizes, and structures every data source into a single query-ready layer. Full lineage is maintained from the raw source through to every number in your model and reports, so every answer is traceable. Data is versioned and permissioned at a granular level, and the entire layer is auditable. The structured layer powers your models, AI agents, and reports, so rather than each tool querying raw data differently, everything runs from one consistent foundation.
Nothing breaks. Your data, context, and finance skills in Drivepoint sit above the LLM layer. You can switch from Claude to ChatGPT to Gemini without rebuilding anything. The same finance context (your cohort definitions, margin logic, channel structure, and historical data) is available to any AI you point at it. You also get direct access to your underlying data for your own BI tools or data warehouse if you want to run queries outside the platform.
Finance skills in Drivepoint are pre-built analytical capabilities trained on the specific financial logic of consumer brands: cohort analysis, LTV and repeat purchase curves, margin decomposition, retail trade spend, inventory turns, variance analysis, and demand planning. Generic AI tools have to be taught this logic from scratch every time. With Drivepoint's skills built in, your AI agents can answer questions like “what is our blended LTV by acquisition channel” or “how much trade spend did we accrue vs. settle last quarter” without you defining terms or building formulas. It is the difference between a finance analyst who knows consumer brands and one who is learning the industry on the job.