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Design Spec: AI-Driven CRM Summaries (v0.9)

1. The Actual Problem We're Solving

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Let's be honest for a second. Sales reps hate data entry. It's the number one complaint we hear during feedback sessions. They'd rather be selling than typing out notes after a call. Currently, our CRM requires manual logging of call outcomes, next steps, and sentiment. The result? Incomplete records, outdated pipelines, and managers who can't actually forecast because the data is garbage.

We aren't building this AI feature to be "cool." We're building it to remove friction. The goal isn't to replace the rep; it's to give them a draft they can tweak in ten seconds instead of writing from scratch in ten minutes. If the AI summary takes longer to edit than to write manually, we've failed.

2. Input Pipeline and Context

The quality of the summary depends entirely on what we feed the model. We can't just dump a raw audio file into an LLM and hope for the best.

• Transcription First: We need a dedicated speech-to-text layer before the summarization step. Accuracy here is critical. If the transcript misses a key number or a competitor's name, the summary is useless. We should flag low-confidence transcript segments to the user.
• Context Window Management: We aren't just summarizing one call. We need to inject context from previous interactions. If a client mentioned a budget constraint three months ago, the AI should know that. However, we have to be careful with token limits. We can't send the entire history every time. The logic should pull the last three relevant tickets and the current account status.
• Metadata Injection: The prompt needs to know who is talking. Is this a discovery call or a renewal negotiation? The summary structure should shift based on the call type tagged in the dialer.

3. Generation Logic and Prompt Strategy

Don't trust the model to decide what's important. We need to constrain the output structure strictly. A free-form paragraph is hard to scan. We want structured data extraction mixed with narrative.

The output should look like this:

• One-sentence hook: What was this call about?
• Key Decisions: Bullet points of what was agreed upon.
• Action Items: Who needs to do what by when? (This needs to be parsed into actual CRM tasks, not just text).
• Sentiment/Risk: A simple flag (Green/Yellow/Red) based on tone and keywords.

We need to engineer the prompt to avoid hallucination. If the AI isn't sure about a deadline, it should say " TBD" rather than making up a date. We'll use a system instruction that penalizes fabrication heavily. Also, we should run a second pass where a smaller model checks the output against the transcript for factual consistency. It adds latency, but accuracy is worth the wait.

4. Privacy and Security

This is where legal will kick down the door if we aren't careful. We are processing sensitive customer data.

• PII Masking: Before any text leaves our infrastructure, phone numbers, email addresses, and credit card info need to be masked or tokenized.
• Data Residency: We can't send EU customer data to a model hosted in the US if it violates GDPR. We need region-specific endpoints for the LLM.
• Retention: The transcripts used for generation shouldn't be stored indefinitely if the customer opts out. We need a purge mechanism tied to the privacy settings.

5. UI/UX Integration

Where does this live? It shouldn't be a separate page. It needs to appear in the activity timeline, right below the call recording.

• Editability: The summary must be editable. Always. The rep needs to feel in control. If they change the AI's text, we should log that change. Why? Because that's training data for us to fine-tune the model later.
• Confidence Score: Maybe we show a small indicator if the AI was unsure about something. "Low confidence on action items" prompts the user to double-check that section.
• One-Click Accept: Make it easy to approve. A single checkmark should save the summary and create the tasks. Don't make them click "Save" after editing.

6. Edge Cases and Failure Modes

What happens when the call is five minutes of small talk? The AI shouldn't generate a fake profound summary. It needs to recognize low-content calls and output a standard "Check-in call, no major updates" template.

What about multi-party calls? If there are three people from the client side, the transcription might get confused about who said what. The summary should avoid attributing quotes unless the speaker diarization is high confidence.

Also, consider offline modes. If the network drops during the upload, the summary generation shouldn't block the rep from saving the call log manually. The AI feature is an enhancement, not a dependency.

7. Measuring Success

We aren't measuring "AI accuracy" in a vacuum. We are measuring behavior change.

• Adoption Rate: How many reps are using the auto-generate button vs. writing manually?
• Edit Distance: How much are they changing the text? If they are rewriting 50% of it, the model isn't helping enough.
• Time-to-Log: We need to track the time between call end and log save. This should drop significantly.



• Data Completeness: Are the "Next Step" fields getting filled out more often?

8. Next Steps

We're starting with a beta group of ten senior reps. They know the product well enough to spot errors quickly. We'll run this for two weeks, collect the edit logs, and tweak the prompt temperature. Don't launch this to everyone until the edit distance is below 20%.

There's a temptation to make the AI sound too human, too conversational. Resist that. Sales reps want brevity. They want facts. Keep it dry, keep it useful. If we can save each rep thirty minutes a week, that's hundreds of hours back into selling. That's the only metric that matters to the business.

Let's build the pipeline first, worry about the fancy UI later. Get the data right, and the rest follows.

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