Use Microsoft Teams with an AI Browser for Data Extraction

If you use Microsoft Teams and you regularly need to extract structured data from websites, the bottleneck is usually the same: Microsoft Teams holds part of the context, but data extraction also needs signals that live outside it - on the public web, in LinkedIn, in news, in other connected apps. Strawberry is built to combine the Microsoft Teams context with the rest of the browser, and run the full workflow as a companion you can re-trigger every week.

This page describes specifically how Strawberry handles data extraction when Microsoft Teams is one of the inputs. It names the Microsoft Teams surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a researcher, ops manager, analyst, founder doing market analysis is trying to do

The goal of data extraction is to turn unstructured pages into a clean table or dataset. The success metric is concrete: extraction accuracy above 95% on spot-checked rows, dedup rate above 95%, completeness above 90%. That definition matters because it shapes what Microsoft Teams needs to contribute to the workflow.

What signals data extraction actually needs

For each signal below, here is whether Microsoft Teams can contribute directly or whether Strawberry has to find it via the browser:

• Source URL pattern (one page, paginated, search results) - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Target schema (which fields per row) - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Completion criteria (how many rows expected) - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Validation rules (which fields must be present) - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Login or paywall barriers - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Rate-limit posture of the target site - Microsoft Teams does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Microsoft Teams

Strawberry can read channel context and send approved messages; meeting prep can include recent channel activity.

Microsoft Teams surfaces Strawberry uses for this workflow: teams, channels, chats, meetings, files.

How Strawberry runs data extraction with Microsoft Teams

1. Strawberry opens the Microsoft Teams teams that contains the relevant context.
2. The companion pulls related context from Microsoft Teams (channels, history, attached files) where it exists.
3. For the parts Microsoft Teams does not store, Strawberry uses the browser - web search, LinkedIn, news, the prospect's website.
4. Strawberry synthesises the output in the shape this workflow needs: A CSV or sheet with one row per extracted entity and a confidence column.
5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Microsoft Teams or your system of record.

Example Strawberry prompt

Paste this in a new Strawberry chat with Microsoft Teams connected. Adjust the specifics to your actual ICP, role, or topic.

Read this Microsoft Teams teams and any linked context.
Then run a full data extraction workflow on it. Use the browser to fill any gaps not in Microsoft Teams.
Return the output in the shape we use for data extraction: A CSV or sheet with one row per extracted entity and a confidence column.
Do not send anything externally. Save the draft to me to review.

What a good data extraction output looks like

Here is what a finished output for data extraction should look like in practice. The specifics will change for your use case, but the shape should look similar:

• Source: company directory at example.com/companies, 30 pages of 50 companies each
• Target schema: name, website, employee count, HQ city, sector tag
• Expected rows: ~1500 (50 x 30)
• Validation: name + website required; sector tag from a fixed list
• Output: ./companies.csv with 1485 rows after dedup, 12 rows flagged for human review

Why Microsoft Teams for this, and where to use a different tool

Microsoft Teams is strong for this workflow because Strawberry can read channel context and send approved messages; meeting prep can include recent channel activity.

Where Microsoft Teams falls short Tenant permission policies often restrict cross-team visibility; meeting transcripts need separate licensing.

Consider also a CRM or project tool for tracked follow-up.

Common mistakes when running data extraction

• No schema defined upfront, leading to inconsistent rows
• Ignoring pagination and missing 80% of the data
• Extracting from logged-in pages without confirming the cookies are valid
• Hammering the target site without rate-limiting

Connecting Microsoft Teams to Strawberry

Microsoft Graph OAuth - scope set per env (engineering pending Infisical update). Once connected, the companion can read the surfaces above without re-authenticating, and any write action still requires explicit human approval the first time the workflow runs.

Caveats

Do not let any AI agent send emails, update CRM records, or change shared systems without a clear approval step. Strawberry is strongest when the workflow combines browser context with connected-app context and a human review for sensitive actions.

When Microsoft Teams + Strawberry is the right combo for data extraction

Strawberry can read channel context and send approved messages; meeting prep can include recent channel activity For data extraction specifically, that means the agent already has teams, channels, chats, meetings, files as starting context - you do not need to brief it from scratch.

When it is NOT a fit

• You need a single number, not a synthesised brief. A SQL query against your warehouse is faster.
• The decision is happening in the next 60 seconds. The agent is fast but it is not instant; for hard real-time use, do it manually.
• The Microsoft Teams data you would feed in is stale or wrong. Garbage in, confident garbage out.

Three mistakes to avoid

1. no schema defined upfront, leading to inconsistent rows
2. ignoring pagination and missing 80% of the data
3. extracting from logged-in pages without confirming the cookies are valid

Honest tradeoff

Tenant permission policies often restrict cross-team visibility; meeting transcripts need separate licensing If you are running this at scale (10+ briefs per day), batch the inputs and let Strawberry process them as a routine instead of one-by-one prompts - cheaper per brief and the output stays consistent.

What a real output looks like

Source: company directory at example.com/companies, 30 pages of 50 companies each,Target schema: name, website, employee count, HQ city, sector tag,Expected rows: ~1500 (50 x 30),Validation: name + website required; sector tag from a fixed list,Output: ./companies.csv with 1485 rows after dedup, 12 rows flagged for human review