Announcing o11's backing from Y Combinator.
AI Sales Pipeline Management in Google Sheets
Every sales team has a pipeline spreadsheet. Usually several. One per region, one per rep, one the VP maintains “just for themselves.” The data lives across tabs with inconsistent column names, deal stages that don’t quite match, and close dates that haven’t been updated since the prospect went dark three weeks ago.
The problem is not that sales teams use Google Sheets for pipeline management. Sheets is flexible, shareable, and fast to update in the field. The problem is that getting any real insight out of those spreadsheets requires hours of manual work. Rolling up regional pipelines into a company view means copying data between tabs, normalizing stage names, and building pivot tables that break the moment someone adds a row. Forecasting means staring at deal amounts and close dates, applying gut-feel discounts, and hoping the number you present to the board is in the right ballpark.
CRMs solve some of this, but most sales teams still export to Sheets for the analysis they actually care about. The CRM is the system of record. The spreadsheet is where decisions happen.
o11 For Google Sheets turns that decision-making spreadsheet into something that works as hard as the team using it.
Pipeline Forecasting From Deal Data Across Regional Tabs
The most painful part of sales forecasting is aggregation. Your East Coast team tracks deals one way, the West Coast team another, and EMEA uses completely different stage names. Before you can forecast anything, you spend an hour just getting the data into a common shape.
o11 understands multi-tab workbook structure natively. It reads across your regional sheets, identifies the relevant columns even when naming conventions differ, and builds a consolidated view without you manually mapping fields.
“Look at the East, West, and EMEA pipeline tabs. Normalize the deal stages to our standard funnel (Qualified, Proposal, Negotiation, Closed Won, Closed Lost). Build a weighted forecast by region using historical win rates from the Win Loss tab.”
Instead of building a forecast formula by hand, referencing cells across six sheets and hoping nothing breaks, you get a consolidated forecast that reflects your actual close rates. o11 applies the win-rate data you already have, weights each deal by stage and probability, and produces a number grounded in your team’s real performance rather than a blanket 30% discount on total pipeline.
When deal data changes, you can re-run the analysis in seconds rather than rebuilding pivot tables from scratch.
Automated Pipeline Health Scoring
Pipeline coverage ratios and velocity metrics are the vital signs of a sales org, but most teams only calculate them when forced to, usually the night before a board meeting. The manual work involved in scoring pipeline health means it happens monthly at best, when it should happen weekly.
o11 can score your pipeline health on demand, applying the metrics that actually matter to your business.
“Score our pipeline health. Calculate coverage ratio against Q2 target of $4.2M. Flag any deals that haven’t moved stages in more than 21 days. Show average days in each stage versus last quarter.”
This gives you three things at once: a coverage number that tells you whether you have enough pipeline to hit target, a list of stalled deals that need attention now, and a velocity comparison that shows whether deals are moving faster or slower than the prior period. All pulled from the data already in your sheets, no formulas to maintain.
You can also ask o11 to flag specific risk patterns.
“Identify deals over $100K that are past their expected close date and have no activity notes in the last 14 days. List them with the rep name and days stale.”
That query across a manual spreadsheet would take 20 minutes of filtering and cross-referencing. With o11 it takes seconds, and the output is a clean table you can drop into your next pipeline review.
Win/Loss Analysis With CRM Export Data
Most sales teams export win/loss data from Salesforce or HubSpot into Sheets for deeper analysis. The CRM gives you the raw records, but understanding why you win and lose requires slicing the data in ways the CRM reporting tools make tedious: by competitor, by deal size band, by sales cycle length, by industry vertical.
o11 works directly with those CRM exports to surface the patterns hiding in your historical data.
“Analyze our Closed Won and Closed Lost deals from the Salesforce export. Break down win rate by deal size ($0-50K, $50-150K, $150K+), by competitor mentioned in the loss reason field, and by average sales cycle length. Show trends over the last four quarters.”
This is the kind of analysis that typically gets assigned to a sales ops analyst and takes half a day. The value is obvious, you need to know where you win and where you lose, but the manual effort means it gets done once a quarter instead of continuously.
o11 also connects your Sheets analysis to Google Slides, so the win/loss insights you uncover can flow directly into your quarterly business review deck without exporting charts and reformatting tables.
“Take the win/loss analysis and create a summary with the key charts. Include the win rate by deal size table and the competitor breakdown.”
Your analysis stays connected to the source data rather than becoming a static snapshot the moment it lands in a presentation.
Before and After: Pipeline Management Workflow
Before o11:
- Export CRM data into Sheets manually each week
- Spend 1-2 hours normalizing regional data into a common format
- Build pivot tables and forecast formulas that break when data changes
- Calculate pipeline metrics manually before each review meeting
- Copy charts into Slides by hand for board and QBR presentations
- Win/loss analysis happens once per quarter because it takes too long
After o11:
- CRM exports land in Sheets; o11 normalizes and consolidates in seconds
- Weighted forecasts use actual historical win rates, updated on demand
- Pipeline health scores and stalled deal alerts available anytime
- Win/loss patterns surfaced continuously, not quarterly
- Analysis flows directly into Google Slides for reviews and board decks
- Sales ops spends time on strategy instead of spreadsheet maintenance
Why o11 Instead of ChatGPT or a Generic AI Tool
You could paste your pipeline data into ChatGPT and ask for a forecast. But you would need to copy the data out of Sheets, format it so the model understands the structure, paste it into a chat window, interpret the response, and manually put the results back into your workbook. Every time the data changes, you repeat the entire process.
o11 sits inside Google Sheets. It reads your tabs, understands the relationships between them, and writes results back into your workbook directly. There is no copy-paste loop. There is no context window limit cutting off your data at row 200. And when your analysis needs to become a slide deck or a document, o11 handles that connection natively within Google Workspace.
For sales teams whose pipeline lives in Sheets and whose insights need to reach stakeholders in Slides, that native integration is the difference between analysis that happens and analysis that stays on someone’s to-do list.
