Less Data Wrangling. More Revenue Impact.

Because it comes from too many places — each system counts things differently. Pipeline coverage in the CRM doesn't match the attribution report from the MAP. Closed revenue in billing doesn't reconcile with what the CRM shows. Querri brings everything into one place, cleans the inconsistencies, and gives you a single consistent view you can actually defend in a CRO review.

Querri has native connectors for HubSpot CRM (auto-sync every 4 hours), Salesforce (early access), QuickBooks, BigQuery, Google Drive, PostgreSQL, MySQL, SQL Server, and Redshift. For billing systems like Stripe, Chargebee, or Zuora — or MAPs like Marketo and Pardot — you upload CSV or Excel exports. Querri joins and standardizes them automatically, even when stage names and field formats don't match across systems.

BI tools like Tableau and Looker are powerful in the right hands — but at most SMBs, the right hands aren't available. Building a new report requires a data engineering ticket and a two-week wait. Querri adds the interpretation and workflow layer: ask a question across your CRM, billing, and MAP data in plain English, and get an answer with charts, tables, and a plain-language explanation — no SQL, no data engineering queue, no waiting.

Yes. That's the core workflow Querri is built for. Connect HubSpot or upload a Salesforce export, bring in your billing data from QuickBooks or a CSV, and pull your MAP data — Querri joins them automatically with intelligent column matching. Ask attribution questions in plain English: 'Which channels are driving the most closed revenue, not just leads?' and get a data-grounded answer across all three sources.

Once your data is connected, running a weekly pipeline summary takes a few minutes. The first-time setup — connecting sources and defining your key metrics — is a one-time investment. After that, save it as a scheduled workflow and the Monday morning pipeline summary runs automatically, delivered to Google Sheets or your Querri Library without anyone touching it.

Yes. Querri was built for people who understand the revenue engine, not people who write SQL. Ask questions the same way you'd ask them in a forecast call — 'What's our pipeline coverage by stage and rep for Q2 vs. Q1?' — and get answers with charts, tables, and narrative. And for the RevOps managers who do know SQL: every step Querri takes is visible and inspectable, so you can verify the logic before you put the number in front of the CRO.

Yes. Querri is SOC 2 Type II certified, with encryption in transit and at rest, tenant isolation, RBAC, SSO/MFA support, and audit logging. Your revenue data is never used to train AI models. Role-based access controls let you manage exactly who can see what — so finance sees the forecast, reps see their attainment, and sensitive compensation data stays where it belongs.

Querri's agentic preprocessing automatically detects and fixes the formatting issues that make real-world CRM exports unreliable for analysis — extra header rows, inconsistent stage names, duplicate records, missing close dates, merged cells. It handles this at ingestion, before any analysis begins, so you're not spending half your day cleaning data before you can start the actual work.

Revenue operations analytics is the practice of connecting, cleaning, and analyzing the data that runs the revenue engine — pipeline, funnel, forecast, attribution, quota attainment — to give RevOps teams and revenue leadership a reliable, current view of what's happening and why. The challenge isn't access to data: RevOps teams have more data than any other function. The challenge is that the data is scattered across the CRM, the MAP, the billing system, and a dozen spreadsheets — making it time-consuming to consolidate, impossible to trust, and unavailable when the CRO asks a question mid-meeting. Querri solves this by connecting those sources, cleaning the data automatically, and letting RevOps ask questions in plain English instead of filing a ticket and waiting two weeks for an answer.