Data to AI™ / Copilot/Pipedrive
Let sales leaders and finance ask a Teams agent about pipeline by stage, slipping close dates and won revenue, straight from current Pipedrive data.
Less tokens, better answers.
Microsoft 365 Copilot
Which reps carry the most weighted pipeline expected to close in September?
Open deals expected to close in September add up to €412,000, or €171,500 weighted. Marta Novak holds €82,000 of the weighted value, almost half.
| Owner Name | Value | Weighted Value |
|---|---|---|
| Marta Novak Deals | €168,000 | €82,000 |
| Tom Reyes Deals | €141,000 | €54,500 |
| Lena Brandt Deals | €103,000 | €35,000 |
Deals pulls all current records on every run, so open deals reflect the last extraction rather than a date window.
Pipedrive is a CRM for sales teams that manages leads, deals and the sales pipeline. Dataddo extracts deals, stages, pipelines, activities, leads and deal subscriptions, so RevOps can ask about pipeline and forecast in plain language.
@Pipeline Desk How much open Value sits in each Stage of the main pipeline, and which open deals are past Rotten Days?
Sales managers ask the agent in their Teams channel, so the weekly sales sync starts from current pipeline numbers rather than last Friday's screenshot.
@Pipeline Desk List Deals won this month by Won time with Title, Value and Owner Name, so billing can check them.
At month end, the finance lead asks Microsoft 365 Copilot and compares the list with what billing expects before invoices go out.
Use the dataddo tools to describe Pipelines and Stage and generate a stage-probability config from Deal Probability and Pipeline Name.
A RevOps engineer uses GitHub Copilot in Agent mode to keep the forecast service's stage probabilities in line with Pipedrive.
Data to AI™ is free while in private pre-release. We onboard teams in waves and shape the product with their feedback. At launch, it will be priced like the AI assistants your team already uses. Need tailored contracts or volume pricing? Talk to us about Enterprise.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Pipeline in the weekly sync | A manager screenshots the Pipedrive board before the weekly sync, and the picture is out of date by the afternoon. | Sales managers ask the agent in Teams, and it reads Deals and Stage from the latest scheduled extraction. | Sync starts from current numbers |
| Stalled deals | Rotten deals are spotted one at a time on the board, and those in other reps' pipelines slip by. | Stage carries Rotten Days and Rotten Flag and Deals carries Rotten Time, so the agent lists stalled deals across all owners. | Stalled deals surfaced |
| Handover to finance | Finance receives won deals by email at month end and re-keys them before invoicing. | The finance lead asks Microsoft 365 Copilot for won Deals with Value and Owner Name and pastes the list into the billing check. | Won deals handed over cleanly |
| Token consumption | Copilot spends tokens exploring raw Pipedrive columns, sampling rows and retrying until it understands the data. | Field definitions and relations are supplied up front, so the model skips the exploration and goes straight to the answer. | Less tokens per answer |
| Answer quality | Prone to wrong joins, invented fields and numbers that are hard to check. | Answers grounded in defined fields. In the Dataddo benchmark, 78.5% of questions were answered correctly, against 65.5% with plain CSV files. | Better answers |
The Pipedrive datasets finance and RevOps teams use most, with their real field names. Copilot queries them by name through the Dataddo semantic layer.
List of curated datasets
Need a dataset, metric, or attribute you don't see?
Tell us what's missing and we'll add it to the connector.
Runs on your schedule, for example daily. Deals, Stage and Leads pull all current records each run, while Deals Timeline and Activities read a sliding date window, so older periods need a one-time full data re-sync.
Pipedrive connector →We asked 43 pre-registered questions of three real datasets - Google Search Console, HubSpot deals and Google Ads - and delivered the same rows to the same model in four ways. Only the data layer changed.
of questions answered correctly - the best of the four delivery methods tested
fewer confidently wrong answers - 14.0% of runs against 24.0% and 26.3%
more correct answers on messy CRM data - better on 8 questions, worse on none
Same model, prompts and rows in every condition; 759 graded runs, ground truth frozen before any run. On Google Search Console alone, documented CSV files did as well as Data to AI™ - the advantage comes from messier data such as HubSpot deals.
Read the benchmark →Create a Pipedrive source in Dataddo with Deals, Stage and Pipelines and attach it to a Copilot destination or AI Model. In Copilot Studio, add a Model Context Protocol tool for https://headless.dataddo.com/mcp-data with OAuth 2.0 Dynamic discovery, then publish the agent to Teams and Microsoft 365 Copilot through Channels.
Through a Copilot Studio agent. Add Dataddo Data Access as a Model Context Protocol tool with OAuth 2.0 Dynamic discovery, then publish the agent via Channels > Teams and Microsoft Copilot.
From the server description you enter in Copilot Studio. Say it holds Pipedrive deals, stages and pipelines, so pipeline and forecast questions trigger a Dataddo call.
Yes. Once published to Teams, people type @ and pick the agent. The agent answers from the Pipedrive flows attached in Dataddo.
Yes. Deals carries Weighted Value and Probability, and Stage carries Deal Probability, so the agent can report weighted and unweighted pipeline side by side.
Yes. Add the Dataddo server to .vscode/mcp.json, start it, sign in and use Copilot Chat in Agent mode. Business and Enterprise orgs must turn on the MCP servers in Copilot policy.