Key Takeaways

• AI Sales Copilot

• Enterprise Discovery

• Deal Intelligence

• Sales Knowledge Retrieval

• Pipeline Visibility

Challenge

Enterprise sales teams generate large amounts of valuable information during every deal, but much of it remains unstructured.

Discovery calls contain customer requirements. RFPs contain detailed technical and compliance questions. CRM records contain opportunity context. Internal documents contain case studies, product information, and approved answers.

Sales representatives must continuously connect these sources to understand:

• What does the customer need?

• What questions have already been answered?

• What information is still missing?

• Which internal resources support the deal?

• What should happen next?

For managers and revenue leaders, the same fragmentation makes it difficult to consistently evaluate deal quality, discovery completeness, and pipeline risk.

Strategy

Build the product around the sales opportunity as the central unit of intelligence.

Rather than create another standalone sales assistant, SF AI Labs structured the platform to continuously organize information around each active deal.

The product roadmap focused on five connected sales capabilities:

1. Discovery Intelligence — structure customer questions and requirements.

2. Sales Knowledge — retrieve relevant answers and internal resources.

3. Deal Intelligence — identify gaps, risks, and progress.

4. Sales Copilot — help representatives prepare, answer, and take next actions.

5. Management Visibility — give managers and CROs a consistent view across opportunities.

Solution

• AI-assisted enterprise discovery workflows

• Automated ingestion of sales call transcripts and notes

• Import and normalization of RFPs, Excel files, Word documents, and questionnaires

• Structured tracking of customer questions and answers by opportunity

• Retrieval-augmented generation across company knowledge and prior deal information

• AI-generated answers grounded in relevant sales materials

• Automated identification of unanswered or incomplete discovery questions

• Deal-level risk, progress, and gap indicators

• Recommended next actions for sales representatives

• Sales copilot for deal summaries and opportunity questions

• Role-specific dashboards for sales reps, managers, and revenue leadership

• Architecture for integrating CRM, email, meetings, and collaboration tools

Execution

• Mapped the enterprise sales and technical discovery process

• Assessed the existing product and codebase for pilot readiness

• Prioritized workflows for sales representatives, managers, and CROs

• Designed the data model around products, opportunities, discovery questions, and answers

• Built pipelines for converting unstructured sales information into structured deal data

• Designed retrieval workflows for finding relevant internal knowledge

• Integrated AI-assisted answer generation into the product architecture

• Developed deal intelligence views for progress, risks, and missing information

• Designed sales dashboards and copilot workflows around day-to-day rep activity

• Strengthened the underlying platform for enterprise testing and staging

Results

• Advanced the product toward an enterprise-ready sales intelligence platform

• Created a structured system for organizing discovery information by sales opportunity

• Connected fragmented sales inputs into a common deal-intelligence architecture

• Established the foundation for automatically identifying discovery gaps and deal risks

• Designed AI workflows for finding relevant answers and supporting sales materials

• Created role-specific intelligence for representatives, managers, and revenue leadership

• Progressed the product into a staging environment for continued enterprise validation

Business Value

The platform is designed to help enterprise sales teams spend less time reconstructing what happened in a deal and more time advancing it.

For sales representatives, AI can surface customer context, relevant answers, missing discovery, and next actions.

For managers, structured deal intelligence creates a more consistent way to understand where opportunities are progressing or getting stuck.

For revenue leadership, the same data layer creates visibility into discovery quality, pipeline health, and patterns across the sales organization.

The result is a sales system designed around improving execution throughout the entire enterprise deal cycle.

Why SF AI Labs

SF AI Labs combines AI engineering with sales workflow and product strategy.

For this engagement, we worked across the complete sales intelligence stack—from discovery workflows and product positioning to RAG, structured deal data, dashboards, integrations, and enterprise platform development.