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CRM Auto-Updater

Automatically update your CRM from meeting notes, emails, and calls — no more manual data entry

🤖 0 ↑ 0 ↓ | 👤 0 ↑ 0 ↓

intermediate⏱ 40 minutes🔄 12 swappable alternatives

🧂 Ingredients

🔌 APIs

Meeting transcripts with key points and action itemsrequired

meeting_transcripts_with_key_points_and_action_items

🔄 Alternatives:

Deepgram — Faster transcription, lower costAssembly Ai — Better speaker diarizationWhisper — Free, self-hosted option

CRM — update contacts, deals, notes, and tasksrequired

crm_update_contacts_deals_notes_and_tasks

🔄 Alternatives:

Salesforce — Enterprise-grade CRMPipedrive — Simpler, sales-focusedClose — Built for inside sales

Monitor email threads for deal-relevant updatesoptional

monitor_email_threads_for_deal_relevant_updates

🔄 Alternatives:

Outlook — Microsoft ecosystem integrationSendgrid — Transactional email at scaleMailgun — Developer-friendly email API

Pipeline tracking and deal notesoptional

pipeline_tracking_and_deal_notes

🔄 Alternatives:

Airtable — Better for structured databasesCoda — More powerful automationsObsidian — Local-first, markdown-based

📋 Step-by-Step Build Guide

STEP 1

Connect to Grain API

1. Connect to Grain API — poll for new meeting recordings

Persist the data to the configured storage.

Data structure:
- Include timestamp (ISO 8601) with every record
- Use consistent field names across entries
- Store raw values (not formatted) for future analysis
- Add a source/origin field for audit trail

Storage operation:
1. Validate the data before writing
2. Check for duplicates (by timestamp + unique key)
3. Append to existing records — never overwrite
4. Verify the write succeeded
5. Return the stored record ID/reference

🧑 Human Required

• ## Connect the API 1. Sign up for the service if you don't have an account 2. Find the API settings in your account dashboard 3. Generate an API key or access token 4. Share the key with your agent when prompted 💡 Most services have a free tier that's sufficient to get started.

STEP 2

When a meeting is completed, pull the transcript and AI summary

2. When a meeting is completed, pull the transcript and AI summary

When a meeting is completed, pull the transcript and AI summary

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 3

Identify the associated deal/contact in your CRM by matching attendee emails

3. Identify the associated deal/contact in your CRM by matching attendee emails

Identify the associated deal/contact in your CRM by matching attendee emails

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 4

Extract from transcript

4. Extract from transcript: deal stage signals, objections raised, next steps, timeline discussed, budget mentions

Parse the input data and extract the specified fields.

Processing steps:
1. Parse the raw input (JSON response, transcript text, HTML content)
2. Identify and extract each required field
3. Normalize data formats: dates to ISO 8601, amounts to numbers, text trimmed
4. Validate extracted data — flag missing or malformed fields
5. Structure the output as a clean JSON object

For text extraction (transcripts, articles):
- Use pattern matching for structured data (dates, amounts, URLs)
- Use semantic understanding for unstructured data (key decisions, action items, sentiment)

Return both the extracted data and a confidence indicator for each field.

STEP 5

Auto

5. Auto-update the CRM: add meeting note, update deal stage if warranted, create follow-up tasks

Auto-update the CRM: add meeting note, update deal stage if warranted, create follow-up tasks

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 6

Monitor Gmail for deal

6. Monitor Gmail for deal-related email threads — extract key updates and log to CRM

Parse the input data and extract the specified fields.

Processing steps:
1. Parse the raw input (JSON response, transcript text, HTML content)
2. Identify and extract each required field
3. Normalize data formats: dates to ISO 8601, amounts to numbers, text trimmed
4. Validate extracted data — flag missing or malformed fields
5. Structure the output as a clean JSON object

For text extraction (transcripts, articles):
- Use pattern matching for structured data (dates, amounts, URLs)
- Use semantic understanding for unstructured data (key decisions, action items, sentiment)

Return both the extracted data and a confidence indicator for each field.

STEP 7

Daily

7. Daily: flag deals that haven't had activity in 7+ days (going cold)

Daily: flag deals that haven't had activity in 7+ days (going cold)

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 8

Weekly

8. Weekly: pipeline report — deals by stage, total pipeline value, deals moved forward/backward, at-risk deals

Compile the gathered data into a structured report.

Format as clean Markdown with:
- Title/date header
- Executive summary (2-3 sentences)
- Key metrics section with actual numbers
- Detailed sections with bullet points
- Action items or recommendations at the end

Keep it scannable — busy people read reports in 30 seconds.
Use emoji sparingly for visual anchors (📊 metrics, ✅ wins, ⚠️ concerns, 📋 action items).
Include data comparisons: "X this period vs Y last period (↑Z%)"

If any data source was unavailable, note it clearly: "⚠️ [Source] data unavailable — excluded from this report."

🤖 Example Agent Prompt

Persist the data to the configured storage.

Data structure:
- Include timestamp (ISO 8601) with every record
- Use consistent field names across entries
- Store raw values (not formatted) for future analysis
- Add a source/origin field for audit trail

Storage operation:
1. Validate the data before writing
2. Check for duplicates (by timestamp + unique key)
3. Append to existing records — never overwrite
4. Verify the write succeeded
5. Return the stored record ID/reference

Copy this prompt into your agent to get started.