Meetings to CRM - Automatically

Contributors

Ferris Kleier
Software Developer
Estimated Implementation Time
30 Minutes
Key Libraries Used
  • @operaide/ai
  • @operaide/aktor
  • axios
  • zod
LLM Providers & Models
  • Azure GPT-4o-mini
  • Configurable via settings
External Services
  • ERPNext
TAGS
Enterprise Automation

Introduction

Record a meeting, click a button, get a structured CRM report posted to your ERP — fully automated. The AI transcribes the recording, extracts a summary, action items, and key dates, and posts a finished report directly to your ERP. Unstructured audio becomes structured, actionable business data — from meeting room to CRM without manual data entry.

Business Impact

Challenge

After every customer meeting, sales reps spend 30-60 minutes writing up notes, extracting action items, and manually entering data into the CRM

Solution

A single audio recording triggers automated transcription, AI-powered extraction of summaries, TODOs, and dates, and direct ERP integration

Outcome

Meeting follow-up goes from an hour of manual work to a single audio recording; reports land in the CRM within seconds

What It Does

  • Record in the Browser — A built-in interface lets users record meeting audio and submit it with one click. No extra apps, no file juggling.
  • Automatic Transcription — The recording is transcribed instantly. Supports common audio formats like MP3, WAV, and M4A.
  • AI-Powered Extraction — The AI extracts a structured meeting summary, action items with checkboxes, an email subject, and key dates — all in parallel, within seconds.
  • Ready-Made CRM Report — Everything is assembled into a formatted report and posted directly to your ERP. The user receives a direct link to the created entry.
  • Instant Confirmation — The user gets a clear confirmation that the report was saved — or a plain-language explanation if something went wrong.
Operaide AI App Custom UI

How It Works

  • Orchestration with defineAktor — The main aktorCreateCRMReport uses defineAktor to compose a subgraph: transcription feeds into five parallel extraction branches, which converge into a report assembly step. The framework handles execution order automatically.
  • Configurable Prompt Templates — Every LLM call uses aktorCompletePrompt with {{variable}} placeholders. All prompts are exposed as aktorSetting values, allowing non-developers to customize extraction behavior, output structure, and language through the Operaide UI.
  • Robust Output Parsing — Helper aktors clean up AI-generated output before use: stripping markdown code fences from JSON, handling multiple checkbox formats (with and without dashes), and falling back to sentence-level splitting if the AI ignores formatting instructions.
  • Typed REST Integration — The ERP call is composed from atomic aktors: aktorCompleteUrl for URL templating, aktorHeaderFormatted for auth headers, aktorAxiosPost for the HTTP call, and aktorResponseUrl for extracting the report link from the response. Each step is independently testable.
  • Integrated Custom UI — Operaide Reaktor apps can ship their own web UIs in a public/ directory, served directly by the platform. The recording interface calls the Reaktor's REST endpoint with authentication handled via the platform — no separate frontend infrastructure required.
  • File Upload via UI Schema Tags — The [file-upload] tag in the input schema's .describe() tells the Operaide UI to render a file upload component instead of a text field. The uploaded file is automatically converted to a base64 data URL.
  • Date-Aware Prompting — The summary and TODO prompts inject the current date via aktorGetDate, instructing the LLM to convert relative time references ("next month", "in two weeks") into concrete calendar dates.

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Reaktor Architecture

The CRM Report Reaktor follows a funnel pattern: one wide input (raw audio) progressively narrows into structured, validated output (ERP entry). The audio enters aktorSpeechToText, producing a transcript that fans out into five parallel LLM branches — summary, TODOs, subject, start date, and end date. These branches have no mutual dependencies, so the framework executes them simultaneously. Once all five resolve, aktorCreateReport assembles them into a single JSON payload, formatting text into ERP-compatible HTML along the way. The payload then flows through the REST integration chain — URL construction, header formatting, HTTP POST, and response parsing — before a final LLM call generates the user-facing confirmation message with the report link.

CRM Reaktor Level 1 Architecture
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