• Client

    Bosch

  • Service

    AI Sales Intelligence

  • Industry

    Industrial Technology

  • Company Size

    400,000+

The client

A global industrial technology giant with a significant and growing enterprise sales operation in India was facing a challenge familiar to any organisation running complex, multi-stakeholder B2B sales cycles: the signals predicting deal risk were buried in the unstructured communication flows of email and meeting transcripts, invisible to the sales leaders who needed them. With high-value presales conversations generating rich but unanalysed data across India and globally, the organisation was making deal progression decisions on incomplete information and by the time risks became visible through traditional reporting, response cycles had already fallen behind. 

The problem

Bosch’s presales team in India was managing an active pipeline of complex enterprise opportunities, each involving extended email communication threads and meeting interactions containing rich intelligence about deal health, customer intent, and objection patterns. But without a system to analyse this communication data systematically, deal risks were discovered late, coaching opportunities were missed, and forecasting accuracy that sales leadership needed remained consistently out of reach. 

 

  • No Structured Analysis of Presales Communication Data — Presales email conversations and meeting transcripts were generating valuable intelligence about customer tone, intent, and objection signals but none of it was being systematically captured or analysed. Sales managers relied on individual recall and manual thread review to assess deal health, producing inconsistent visibility across the active pipeline. 
  • No Unified Dashboard for Account-Level Sentiment — With deal intelligence scattered across individual inboxes and meeting platforms, there was no consolidated view of account-level sentiment or deal maturity. Managers could not quickly assess which accounts were warming, cooling, or showing signs of competitive vulnerability — limiting ability to intervene at the moments that mattered. 
  • Manual Communication Review Causing Late Detection of Deal Risks — The manual effort required to review communication threads across multiple ongoing opportunities meant early warning signals were routinely identified too late for effective intervention. By the time a deal risk became visible through traditional pipeline review, the optimal window for coaching or course correction had often already closed. 

OUR SOLUTION

Worxwide implemented a secure API integration with Outlook and Microsoft Teams to extract and filter inbound and outbound presales emails and meeting transcripts in real time. LLM-based sentiment modelling was deployed to detect tone, intent, and deal-critical phrases — including objections, interest signals, and next-step commitments — across every communication touchpoint. Predictive deal insights were surfaced through a sentiment scoring dashboard with risk indicators and AI-led coaching recommendations, giving sales leaders the visibility needed to act earlier and more effectively. 

 

  • Secure Outlook and Teams Integration for Communication Capture — API integration with Outlook and Microsoft Teams enabled automatic extraction and filtering of presales communication — emails, meeting transcripts, and call recordings creating a comprehensive, real-time dataset of deal interactions without requiring manual data entry or workflow disruption. 
  • LLM-Based Sentiment Modelling and Intent Detection — Large language models were applied to detect sentiment, tone, and intent signals across communication data identifying deal-critical phrases like objections, expressions of competitive evaluation, interest in next steps, and urgency signals, transforming unstructured communication into actionable deal intelligence at scale. 
  • Predictive Deal Insights Dashboard with AI Coaching Recommendations — Sentiment scores and risk indicators were surfaced through a unified dashboard, enabling sales managers to see account-level deal health at a glance and drill into specific communication signals. AI-generated coaching recommendations guided sales representatives on the most effective interventions for each deal situation. 

Our work in action

The Impact

2-3 Weeks Earlier Flagging of At-Risk Deals

AI-driven sentiment analysis surfaced deal risk signals 2 to 3 weeks earlier than traditional pipeline review processes, giving sales managers significantly more time to intervene, coach, or escalate before opportunities were lost across Bosch’s presales operation.

20% Shorter Response Cycles

Faster identification of deal risks and customer intent signals translated into a 20% reduction in response cycle length as sales teams acted on accurate, timely intelligence rather than waiting for the lag between communication and manual review to resolve.

Improved Forecasting Accuracy for Sales Leadership

 Consistent, data-driven deal health visibility across the active pipeline gave sales leadership the confidence to make more accurate forecast commitments reducing variance between predicted and actual outcomes and improving the quality of resource allocation decisions.

Download