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The Challenge

Teams working with clients know the familiar rhythm: Meetings generate pages of notes, action items and follow-ups, but the process of translating that information into organized, trackable work remains fragmented. Notes often live in personal files, emails or chat threads, making it difficult to maintain a consistent record of decisions and next steps.

The result isn’t a lack of insight — it’s a lack of structure that creates bottlenecks in client delivery, slows down follow‑up, and fragments accountability across systems. Valuable context from conversations gets lost between systems. We built the GTM Planning App in Sigma to address that gap by turning meeting output into connected, actionable data that links directly to the client, project and task level of work.

What We Built

The GTM Planning App acts as a unified command center for client operations. Built on top of your data warehouse, it brings together all key elements of client delivery:

• Client details
• Active projects and task tracking
• Meeting transcripts with AI-generated summaries
• Email follow-ups

Every piece of data lives within a shared Sigma environment — eliminating disconnected tools and ensuring that meeting insights can be traced directly to actions.

The Workflow

1. Start with a Unified Client View

Users begin at an overview dashboard that consolidates client information, from industry and region to active projects and recent communications. Selecting an individual client drills deeper, revealing the projects the client owns as well as AI generated analysis of the client itself. When users select a project the app opens a project-level view, showing tasks, key milestones, and a short AI summary of recent activity and sentiment. Project tasks are managed from this view. They can be assigned or edited and viewed from a tabular format or as a Gantt chart. The project view also tracks meetings assigned to the project and presents a view of meeting sentiment over time. This structure allows teams to navigate seamlessly from broad portfolio health down to the details of Individual meetings

2. Upload Transcripts, Derive Insights

With Sigma’s file upload functionality, meeting transcripts in WEBVTT format (converted to .txt before uploading) can be uploaded directly into the app. Behind the scenes, Sigma stores the file securely and triggers a Snowflake stored procedure for processing.

That procedure performs NLP and sentiment analysis, extracting structured data such as:

• Meeting duration and participants
• Speaker balance and tone metrics
• Summaries of main discussion points
• Suggested action items

What begins as an unstructured file becomes organized, queryable information inside the warehouse — ready for review and analysis in Sigma.

3. Review, Refine, and Assign

Users can review AI-generated summaries and action items and convert them into structured tasks. Each task automatically links back to the originating meeting, client and project, maintaining a consistent data lineage from discussion to execution. This short feedback loop ensures accountability without creating extra administrative work.

4. Generate Polished Follow-Up Emails

Once action items are finalized, the app can draft follow-up emails using information from the meeting summary. Users can draft the emails in a simple way, or they can customize the AI prompt parameters for tone and focus. The drafts are concise, consistent and aligned with the agreed-upon next steps. Users can review, edit and send directly from within Sigma, ensuring every meeting closes with a clear record of commitments.

5. Close the Loop in One Environment

Because everything happens within Sigma, teams can move from raw meeting input to structured project tracking within a single platform:

Transcript → Analysis → Tasks → Follow-Up → Performance Tracking.

Visualizations, filters and dashboards leverage Sigma’s standard analytics layer, allowing teams to explore patterns such as client sentiment over time.

How It Works Behind the Scenes

From an implementation perspective, the app combines several Sigma and Snowflake capabilities:

• File Uploads: Sigma file columns store WEBVTT files’ associated metadata. The files themselves are stored in a preconfigured S3 bucket.
• Automated Processing: Snowflake stored procedures use the metadata from Sigma to access the files from the S3 bucket and transform the raw text into structured tables through NLP.
• AI Summarization: Text processing and sentiment scoring extract highlights and key themes. Meeting level summarizations happen within the Snowflake Stored procedure. Project and client level summarizations are performed through the Sigma layer. Summarizations are returned in a JSON package which is then parsed in Sigma. This allows maximum flexibility without creating a duplicated table in Snowflake. This approach is perfect for our demo environment. In a production version, processing logic would more likely write directly to the warehouse tables rather than rely on JSON parsing within Sigma.
• Relational Modeling: Tasks, meetings and clients are connected via shared identifiers.
• Visualization and Collaboration: Sigma provides controlled access, filtering, and analysis directly in the user interface.

Together, these components create a lightweight but fully integrated workflow for managing ongoing client relationships.

Extending the Model

Although designed for go-to-market planning, the architecture supports other applications where unstructured data needs to connect with analytic workflows:

• Processing receipts or invoices for financial dashboards
• Linking PDF contracts or proposals to project records
• Managing uploaded assets, such as images or reports, as part of operational pipelines

The same pattern applies: upload → process → enrich → act.

Why It Matters

Client meetings generate the most valuable insights in a services business, but those insights rarely survive beyond the conversation. By embedding meeting capture, analysis, and task creation into Sigma, teams can transform discussions into structured, consistent, and trackable workflows.

The GTM Planning App demonstrates how combining Sigma’s interactivity, Snowflake’s data processing and AI’s language understanding can reduce administrative friction and strengthen client engagement — all without leaving the analytics environment.

Looking Ahead

This app represents what’s possible today, but there’s room to extend it into a more connected and intelligent client‑operations platform. Some next areas of exploration include:

• Sales and pipeline integration: Connect transcripts and sentiment data to CRM objects, making it easier to track how conversations influence pipeline progression and conversion likelihood.
• Communication ecosystem plugins: Integrate with tools like Outlook or Teams to automatically attach meeting artifacts and synchronize follow‑ups across systems.
• Customer data and contact management: Enrich client records with live data from CRM or ERP systems, ensuring that dashboards and summaries stay up to date as real‑world conditions evolve.
• App templating and deployment: Package the GTM Planning App so that organizations can adopt it as a standard internal tool or integrate it into existing SaaS ecosystems.
• Intelligence over time: Use aggregated sentiment and topic trends to identify client satisfaction risks, recurring challenges or opportunities for proactive engagement.

These enhancements will move the app from a meeting‑productivity tool toward a full‑fledged client intelligence platform — one that continuously learns from every conversation and improves how teams plan, deliver and communicate.