Data to AI™ / ChatGPT/HubSpot
Check forecast accuracy and deal velocity in a ChatGPT chat, approving every Dataddo tool call before HubSpot deal data is read.
Better answers, fewer tokens.
ChatGPT
How long did the deals we won in September spend in each stage?
The 23 deals won in September took 31 days on average from qualification to contract. Decision maker bought-in was the slowest stage, at 12 days or 39% of the cycle.
| Stage Label | Deals | Avg days in stage | Share of cycle |
|---|---|---|---|
| Qualified to buy Properties History | 23 | 6 | 19% |
| Presentation scheduled Properties History | 23 | 9 | 29% |
| Decision maker bought-in Properties History | 23 | 12 | 39% |
| Contract sent Properties History | 23 | 4 | 13% |
Days are measured between dealstage change Timestamps in Properties History, with Stage IDs mapped to Stage Label through Deal Pipelines.
HubSpot is a marketing, sales and service platform. Dataddo extracts its CRM records, such as deals, line items, quotes and owners, with custom fields on every object, plus pipeline stages and property change history.
Check known_gaps first, then compare the open Deals snapshot from 1 September with the deals that reached closed won since.
With daily snapshots appended by extraction timestamp, a finance analyst sees how much month-start pipeline closed, and ChatGPT says up front if any snapshots are missing.
From Properties History, which deals moved back to an earlier dealstage in the last 30 days, and which Updated By User ID made each change?
RevOps approves each lookup in developer mode, then pastes the list into the team channel so managers can go through the reasons with the deal owners.
Using Source Type in Properties History, how many dealstage changes last month came from WORKFLOW and how many from CRM_UI?
An operations lead wants to know how much pipeline movement comes from workflow rules rather than reps before redesigning those workflows next quarter.
Data to AI™ is priced per seat, like the AI assistants your team already uses, with ~25% off on annual billing. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
$10.49/month
per seat
3 seats minimum
Flexibly deploy Dataddo in any cloud or hybrid environment.
Custom
We have a payment model that works for you
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Pipeline history | Each export is a fresh list, so last month's pipeline survives only if someone happened to save an old file. | Append every run with the Dataddo extraction timestamp, and ChatGPT can compare snapshots from any day since you started. | Month-over-month comparisons |
| Reviewing what the model reads | Uploading a deals spreadsheet hands the entire sheet to the conversation in one go, with no checkpoint. | In developer mode each query, describe_entity or known_gaps call waits for your confirmation, and unused tools can be disabled. | Reviewed step by step |
| Form and page reports | Form and landing page numbers sit in separate HubSpot reports that someone exports one at a time. | The same connector offers Forms report and Landing pages report datasets, so ChatGPT can read them next to deal data. | Forms and deals together |
| Token consumption | ChatGPT spends tokens exploring raw HubSpot 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. | Fewer 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 HubSpot datasets finance and RevOps teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
| Dataset | Metrics and attributes |
Ask ChatGPT
|
|---|---|---|
| Deals |
|
Which deals have not had hs_lastmodifieddate updated in the last 21 days? |
| Deal Pipelines |
|
Which Stage ID maps to which Stage Label in each of our pipelines? |
| Properties History |
|
Which Updated By User ID made the most deal property changes last week? |
| Owners |
|
What First Name and Last Name belong to each Owner ID shown on our deals? |
| Line Items |
|
How many line items were modified per week this quarter, based on hs_lastmodifieddate? |
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 →Set up a HubSpot source in Dataddo with Deals, Deal Pipelines and Properties History, then attach its flow to a ChatGPT destination or AI Model. In ChatGPT on the web, enable developer mode under Settings > Security and login, create an app for https://headless.dataddo.com/mcp-data with OAuth, and select it from the + menu.
One with developer mode: Pro, Plus, Business, Enterprise or Education, on the web. On Business and Enterprise, check that your workspace admin has allowed developer mode before adding the Dataddo app.
ChatGPT asks you to confirm every tool call, so you see whether it wants to run query, describe_entity or known_gaps. In the app settings you can also disable Dataddo tools you never want used.
Not as past snapshots. The HubSpot API only returns the current list of deals, so history starts when you append runs with the extraction timestamp. Properties History adds property changes within its date range.
Yes, if you add them. Besides CRM objects, the HubSpot connector lists report datasets such as Forms report and Blog posts report, which read a relative date window on each run instead of the full record list.
No. ChatGPT reads deals that Dataddo has already extracted, by entity and field name. Dataddo never accepts SQL from the model and writes nothing back to HubSpot.
When you create the source, untick personal fields such as name or email. They never reach the flow, and ChatGPT only sees deal and stage data.
