THE PROBLEM
The client, a leading institutional sales and marketing solution provider for real estate companies, worked with top developers to sell residential and commercial properties across asset classes and price points.
The company captured large volumes of audio, video, and text data from regular customer interactions. While they had an existing system to analyze this data, it struggled to handle the volume and complexity of unstructured, multimodal inputs. The system was slow and failed to generate high-quality insights needed to support rapid sales conversion.
To address these challenges, the client aimed to upgrade their current setup and build a new platform. The objective was to create a more robust and scalable solution that could process large volumes of interaction data quickly and translate it into accurate, actionable intelligence to accelerate sales outcomes.
INXITE OUT APPROACH
Inxite Out took a consultative approach to understand the client’s end-to-end sales cycle, including its operational nuances, strategic business priorities, and specific analytical needs. The team conducted a comprehensive gap analysis to evaluate the current state of insight generation in terms of speed and quality, and compared it with the desired future state.
Based on these insights, Inxite Out designed a generative AI-powered platform capable of rapidly analyzing multimodal data from voice, video, and chat interactions to extract timely and actionable intelligence.
The workflow included the following components:
Translation and Transcription Engine
The platform ingested unstructured multimodal data including audio, video, and text files through an API, along with the associated processing request. These inputs were translated and transcribed to generate structured text outputs with high confidence scores. This process ensured accurate transcriptions, preserved contextual richness, and minimized information loss.
Sales Enablement Insight Engine
Once the transcripts were generated, the structured text was processed through a GenAI-powered engine that extracted key signals to support and accelerate the sales process. The outputs were organized into three focused categories to guide downstream action.
a. Conversation snapshot
- Interaction recap: This combined a refined transcript with a concise summary to present a clear view of the conversation. It retained essential information, removed redundancy, and highlighted the main themes discussed.
b. Buyer signals and sales opportunities
- Engagement highlights: Captured important discussion points such as key figures, preferences, and inputs shared by the customer.
- Customer pain points: Identified concerns or objections raised by the customer, including pricing, product gaps, availability issues, or other decision barriers.
c. Conversion cues and next steps
- Sales commitments: Listed follow-up actions committed by the salesperson, along with the customer’s preferred communication mode and timing.
- Purchase intent and justification: Assigned a sentiment tag based on the conversation tone (positive, neutral, or negative) and included a short explanation grounded in specific cues from the discussion.
Lead Intelligence Extraction Engine
Alongside insight generation, the platform also featured a lead intelligence extraction engine that captured key information from the ingested files and organized it into a structured format. The extracted data included fields such as customer name, contact details, location, purchase preferences, budget, unit information, inventory discussed, and payment timelines.
This structured output enabled quick filtering and segmentation of leads, supported enrichment of existing databases, and allowed users to perform targeted analytics through straightforward queries.
Platform infrastructure and client integration
A scalable and automated infrastructure supported the three core engines: transcription, insight extraction, and lead intelligence. It ensured consistent performance with minimal manual effort. A queuing mechanism enabled efficient processing of large data volumes, and dynamic scaling helped optimize costs.
The platform was integrated into the client environment through a secure API, without requiring direct access to internal systems. All data transfers were encrypted to maintain enterprise-grade security.
The infrastructure was modular and reusable, making it easy to replicate for similar use cases.
RESULT
The Generative AI-based sales intelligence platform processed over 500 customer conversations, totalling approximately 20,000 minutes, each week. The system was designed for scale and flexibility, with a peak processing capacity of 80 conversations or 3,200 minutes per hour.
The solution delivered the following benefits:
- Improved sales productivity: Helped sales managers focus on high-potential leads, prioritize opportunities more effectively, and reduce the overall time to conversion.
- Consistent and objective decision-making: Replaced subjective judgment with structured, insight-driven evaluation, resulting in a more standardized and reliable sales process.
- Operational efficiency: Delivered approximately 60 percent faster insight generation and nearly 50 percent cost savings compared to the client’s existing system
The client adopted this scalable solution as an integrated sales intelligence platform, enabling a customer-centric sales approach, reduced time-to-conversion, and driving sales performance across distributed teams.
