Data to AI™ / ChatGPT/Instagram Public Data
Vet creators and read what rival brands publish on Instagram from ChatGPT, approving each lookup so you know exactly which public posts shaped the answer.
Better answers, fewer tokens.
ChatGPT
How often did each competitor post in September, and whose posts drew the most likes?
@northpeak_coffee posted least but averaged the most likes per post. @harbor_roasters published almost daily, with about a third of the likes per post.
| Username | Posts | Avg Like Count | Avg Comments Count |
|---|---|---|---|
| @northpeak_coffee Posts | 9 | 4,820 | 212 |
| @harbor_roasters Posts | 27 | 1,610 | 48 |
| @bluestone_brew Posts | 14 | 2,370 | 95 |
Posts are counted by Timestamp; Like Count and Comments Count are the public totals at extraction time, averaged per post.
Instagram Public Data reads what anyone can see on a public Instagram profile you name by username, such as a competitor, a peer brand or a creator: the profile with its follower and post counts, and its posts with likes, comments and views. It is not for personal accounts.
For the creators on our shortlist, compare Followers Count and Media Count from Profile with average Comments Count per post from Posts. Who gets real conversation for their size?
The influencer manager approves each query, then takes a ranked shortlist into the agency briefing, so the budget goes to creators whose audiences reply, not only to the largest follower counts.
List the posts one competitor published in the two weeks around their launch by Timestamp, with Caption, Like Count, Comments Count and Permalink.
Brand marketing reads the launch sequence post by post, sees which message drew the most comments and plans its own response campaign with the links at hand.
Run data_status on the Instagram Public Data flows, then group last quarter's competitor posts by the main offer in their Caption and total Like Count per theme.
The content strategist confirms the data is fresh, then gets a list of recurring promises such as discounts or new products and how strongly audiences reacted to each.
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.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Creator vetting | An influencer manager opens every creator profile, notes followers and a few post likes by hand and uploads the sheet to ChatGPT. | ChatGPT reads Followers Count from Profile and Like Count and Comments Count from Posts for the whole shortlist from the latest extraction. | Shortlist ranked on evidence |
| Trust in the answer | Nobody can tell which rows of a pasted competitor sheet ChatGPT relied on for its summary. | ChatGPT asks you to confirm each Dataddo tool call, so you see every lookup of Posts or Profile before the answer appears. | Every lookup approved |
| Finding the original post | A summary mentions a strong competitor post, and someone has to scroll the profile to find it again. | Posts carries the Permalink of each post, so ChatGPT returns the link along with the counts and the Caption. | One click to the source post |
| Duplicate posts | Repeated exports of the same profile stack up, and the same post is counted twice in the totals. | The dataddo_hash column is built from Media Id, so each post stays one record when the destination upserts on it. | Each post counted once |
| Token consumption | ChatGPT spends tokens exploring raw Instagram Public Data 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 Instagram Public Data datasets marketing teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
| Dataset | Metrics and attributes |
Ask ChatGPT
|
|---|---|---|
| Posts |
|
Which competitor posts from the last 30 days drew the most Comments Count, and what does each Caption promise? |
| Profile |
|
Which creators on our shortlist have the most Followers Count relative to their Media Count? |
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 an Instagram Public Data source per competitor or creator Username, with Posts and Profile, and attach the flows to a ChatGPT destination or AI Model. In ChatGPT on the web, switch on Developer mode in Settings > Security and login, create a Dataddo Data Access app with https://headless.dataddo.com/mcp-data and OAuth, and select it.
Developer mode in ChatGPT on the web, available on the Pro, Plus, Business, Enterprise and Education plans. On Business and Enterprise, a workspace admin may first need to allow developer mode for the workspace.
Only public data. Posts includes Like Count, Comments Count, View Count, Engagement, Media Type, Caption and Timestamp, and Profile includes Followers Count, Follows Count, Media Count, Biography and Website. Reach and Saves of other accounts are not part of it.
Create another Instagram Public Data source with that Username, for example the handle from the profile URL, and attach the new flow to your ChatGPT destination. Each source covers one public profile.
Dataddo reads public profiles through the Instagram account on your authorizer, and that account must be connected to a Facebook account. If none is found, the source stops with an error, so use another authorizer.
Yes. In the app settings you can turn individual Dataddo tools on or off, so you can keep querying Posts and Profile while switching off tools you do not want ChatGPT to run.
No. ChatGPT only queries the extracted data through the semantic layer by entity and field name. Dataddo never accepts SQL from the model and nothing is written back to Instagram.
