Automate Internal Knowledge for Your Consulting Firm

Most consulting firms start with a shared Google Drive or Notion workspace for knowledge management. While simple, these tools become data graveyards. Search is keyword-based, meaning you cannot ask a conceptual question like, “What were the key takeaways from the Q3 kickoff with Client X?” A one-hour video recording is an opaque file, its contents unsearchable without manual summarization.

Consider a 7-person firm where the lead consultant for Client A is on vacation. A junior consultant needs to find a specific scope change decision made two months ago. They waste an hour digging through Google Drive folders, scanning poorly named documents, and searching Slack channels. The information is unavailable because the meeting notes were never properly summarized, tagged, and filed. This delays the project and creates a single point of failure.

Tools like Asana or Monday.com are built for task management, not knowledge retention. Key discussions in comments are ephemeral and nearly impossible to search for across projects. The structural problem is that these are document storage or task management platforms, not knowledge extraction systems. Their architecture requires disciplined, manual data entry from busy consultants, which is the very administrative overhead firms aim to reduce.

The first step is a discovery process to map your firm's existing information flow. We would audit how meetings are recorded, where project documents are stored, and what your most frequent information retrieval challenges are. This audit produces a data flow diagram and identifies the highest-value sources for automation, such as Zoom Cloud recordings and client email threads.

The core of the system would be an automated data pipeline built on AWS Lambda. When a new meeting video is saved to a service like Zoom, a Lambda function is triggered. This function uses the Claude API to transcribe the audio, identify speakers, and generate a structured summary with action items. The summary and transcript are then converted into vector embeddings using a library like `sentence-transformers` and stored in a Supabase Postgres database with the pgvector extension. This serverless architecture processes a 1-hour meeting in under 5 minutes and typically costs under $50 per month to operate.

The delivered system provides a simple, secure web interface where any consultant can ask questions in plain English. For example, a query like 'What did Client Y say about budget concerns in our last call?' would hit the Supabase vector database to find relevant text chunks. The Claude API then synthesizes a direct answer with citations to the source meeting. The result is a concise answer, not a list of 10 links to documents.