What it is
HockeyStack is a B2B revenue attribution platform that stitches web sessions, ad spend, CRM records, and sales activity into one account-level journey, then lets you interrogate that journey in plain English through an AI analyst called Odin. The ingestion and modeling layer is named ATLAS; the account-level graph it builds is the thing every other feature reads from.
It is the first attribution vendor in the ooligo catalog, and it sits at the decision-system end of the category rather than the dashboard end. The 2026 product line is organized around Revenue Agents that act on the attribution graph — the company’s own framing after it repositioned from an attribution tool to an enterprise revenue-agent platform.
HockeyStack is Y Combinator-backed, passed 300 customers in under two years, and announced a round on 15 April 2026 that took total funding past $50M, with Bessemer Venture Partners, Y Combinator, and Uncorrelated Ventures participating. Named customers — Mastercard, RingCentral, Rakuten, Justworks, AppsFlyer, Mimecast, StackAdapt — read enterprise and upper mid-market, which matches the post-raise move upmarket.
Why it shows up in RevOps stacks
- Attribution is account-level, not lead-level. B2B deals carry 6-10 stakeholders, and a lead-scoped model splits one buying group into ten unrelated rows. ATLAS resolves touchpoints to the account, including cookieless capture of dark-funnel activity that never fills in a form, so the journey a CFO is shown matches the deal that actually closed.
- Odin answers the question instead of building you a dashboard. Every response runs a multi-agent chain: interpret the question, plan the steps, retrieve governed data, execute deterministic analysis, validate the result before returning it. That structure is what makes the output auditable rather than a plausible-sounding number. Odin runs in the app or in Slack by tagging
@hockeystackodin, which is where funnel, lift, and attribution-breakdown questions actually get asked. - The agent layer is the 2026 differentiator. Twelve prebuilt agents cover named GTM jobs — Closed Lost Deal Resurrector, Account Pre-Call Brief, Win/Loss Analyzer, Multi-Touch Attribution Anomaly Detector, Marketing Budget Optimizer, Weekly Rep Coaching Report, AE Forecast Challenger — plus a custom agent builder for playbooks that don’t match a template. Forecast Challenger is the one that changes a meeting: it tests rep-reported commits against historical deal patterns from HockeyStack’s Blueprint model and flags weak commits before the number is missed.
- Warehouse and paid-media coverage are wide enough to be the system of record. Snowflake, BigQuery, Amazon S3, Azure Blob, and ClickHouse on the warehouse side (Databricks listed as coming soon); LinkedIn, Google, Meta, Bing, TikTok, Reddit, X, StackAdapt, AdRoll, G2, and Capterra on the spend side; 6sense, Demandbase, Bombora, and RollWorks for intent, and Gong for conversation data.
Pricing
Quote-only, annual contract, no published price. Two plans: GTM Intelligence (attribution, reporting, Odin, scoring, audience sync, enrichment, and 2 out-of-the-box agents) and GTM Execution (all 12 prebuilt agents, the custom agent builder, and workflow automation).
Third-party procurement and vendor-comparison data — not vendor-published, so treat it as a band rather than a rate card — puts GTM Intelligence near $1,399/month and GTM Execution near $2,200/month, with a median annual contract around $28,000 and most deployments landing between $16,800 and $60,000 per year. Contact and account volume moves it more than seat count does.
For calibration against the alternatives: Dreamdata publishes a free tier and a $750/month Activation Starter, with a median annual contract near $27,000 per Vendr — similar landing zone, much lower entry point. Factors.ai starts at $399/month, with multi-touch attribution gated to its $899-$1,200/month Growth tier, roughly a third of HockeyStack’s reported entry. HockeyStack’s premium is bought with the agent layer, not with the attribution model.
Best for
RevOps and demand-gen leaders at $50M+ ARR B2B companies running multi-stakeholder deals across paid, events, and outbound, where the binding problem is defending spend to a CFO with account-level evidence, and the team will fund an agent layer that acts on the model rather than a reporting layer that describes it.
Pick something else when the shape is wrong. Choose Dreamdata if you want the warehouse-first, account-level pole with a published entry price and a free tier to prove the model before committing budget — it is the other required entry in this category and the closest structural substitute. Choose Adobe Marketo Measure (formerly Bizible) if you are already standardized on Adobe and Marketo and attribution is a line item on an existing enterprise agreement rather than a new vendor. Choose Factors.ai if you are under $10M ARR and need multi-touch attribution plus ABM workflows on a budget that cannot absorb a $28,000 contract. Choose RevSure if the requirement is forward-looking pipeline readiness — predicting whether the quarter closes — rather than explaining where the last quarter’s revenue came from.
Watch-outs
- Quote-only pricing on an annual-only contract, with the agents behind the higher tier. Teams demo the agent layer and then budget from the GTM Intelligence number. Guard: get GTM Execution quoted from day one if the agents are why you are buying, ask for the contact-volume and account-volume tier boundaries in writing before signing, and benchmark the quote against Dreamdata’s published $750/month entry so you know what the premium is buying.
- Attribution output is capped by CRM hygiene, and the vendor cannot fix that. Inconsistent opportunity stages, missing close-won dates, and undisciplined UTMs produce a confident model of the wrong funnel. Guard: audit opportunity stage definitions and UTM conventions before the trial starts, and run a 30-day parallel period against your existing reporting — if the two disagree, resolve the source data before you present either number to finance.
- Revenue Agents are a 2026 product on a 2023 company. The attribution engine has years of production behind it; the agent layer that justifies the price does not. Guard: scope the pilot to 2 named agents tied to a metric you already track — Forecast Challenger against last quarter’s commit accuracy is the cleanest test — and define what success looks like before the contract, not after.
- No published public API or MCP server, and Databricks is not shipped. Automation is bounded by the native integration list and warehouse sync. Guard: confirm during the trial that the sync direction covers the writes you need, and if Databricks is your warehouse, get a delivery date in writing rather than accepting the coming-soon label.
For the deal-inspection and forecasting layer this feeds, see Gong and Clari; for the account-signal sources upstream of it, see 6sense, Demandbase, Common Room, and Warmly.