Project Name
Ksolves Transforms Account Prep From Hours of Manual Research to Real-Time AI Insights
A large enterprise technology organization with a B2B customer base had rich account data scattered everywhere and connected nowhere. Before every account review, Account Managers and CSMs opened the CRM, pulled the support case log, checked system health, searched for network incidents, then switched over to find the customer’s latest earnings release and strategic priorities, a ritual that ate one to two hours per account and still left the picture partially stale. Through an AI ML consulting engagement, Ksolves built an AI-powered Account Intelligence Platform that aggregates all seven internal and external data sources into one conversational interface, delivering real-time health scores and upsell recommendations before the call even starts. Account preparation time dropped by roughly 85%, compressing hours of research into a single conversational query.
- Extensive Pre-Engagement Research Consuming Hours of CSM and Account Manager Time: Before every account review or renewal conversation, CSMs manually aggregated data from multiple internal platforms and external sources, routinely burning one to two hours per account that should have gone toward relationship development instead.
- Internal Account Data Fragmented Across Disconnected Systems: System health data, active support cases, network incidents, and usage telemetry each lived in separate platforms with no unified view, forcing manual system-switching and synthesis that was slow and inconsistent across team members.
- External Commercial Intelligence Requiring Separate Manual Research: Understanding a customer's financial position, workforce signals, and strategic priorities meant independently researching earnings calls and company announcements, work that sat entirely outside internal tooling with no connection to account health data.
- Upsell and Cross-Sell Recommendations Generated Inconsistently Across the Team: Without a structured approach, expansion recommendations varied by individual CSM familiarity and available research time, so high-potential accounts got missed by time-pressured team members.
- No Real-Time Account Health Visibility for Portfolio Risk Management: Sales VPs had no consolidated, real-time view of account health, so identifying at-risk accounts required manual aggregation too slow to enable proactive intervention before churn risk actually materialized.
- Manual Research Quality Inconsistent Across Team Members and Account Tiers: Account preparation quality depended heavily on individual CSM tenure and thoroughness, producing uneven customer experience and revenue performance across the portfolio.
Through this AI ML consulting engagement, Ksolves built an Account Intelligence Platform that pulls all seven of the organization's key data sources, four internal, three external, into a unified account model, surfacing insights and recommendations through a conversational interface CSMs can query before and during customer calls.
- Unified Account Intelligence Aggregation: A data aggregation layer consolidates system health telemetry, support case data, network incidents, and usage metrics from internal platforms, then integrates customer financials, workforce signals, and strategic initiatives from external sources into one continuously updated account model.
- Conversational Account Intelligence Interface: An LLM-powered interface lets Account Managers and CSMs ask plain-language questions about any account, current health, open issue trends, adoption gaps, recent external developments, and get synthesized answers drawn from the full account model instead of a multi-system manual search.
- Real-Time Account Health Scoring: A multi-dimensional scoring engine combines system health indicators, case severity, network incident frequency, and usage adoption into a continuous, ranked health score, giving Sales VPs real-time portfolio risk visibility.
- Data-Driven Upsell and Cross-Sell Recommendation Engine: The platform cross-references product usage gaps against external growth signals and stated strategic initiatives, generating specific, ranked upsell and cross-sell recommendations in place of generic expansion playbooks.
- CRM and Support System Continuous Sync: Automated bidirectional sync with the CRM and support platforms keeps account health scores, engagement history, and usage metrics continuously current, so every conversation runs on live data instead of a stale manual extract.
Technology Stack
| Category | Technology |
|---|---|
| AI / NLP | Conversational Account Intelligence Engine (LLM) |
| Architecture | Account Health Scoring Model |
| Platform | Internal Data Aggregation Layer |
| Integration | External Intelligence Connector |
| Database | CRM and Support Data Sync Pipeline |
| Methodology | Upsell and Cross-Sell Recommendation Engine |
- 85% Faster Account Preparation: What used to take one to two hours of manual aggregation per account now compresses into a single conversational query, cutting prep time by roughly 85% across the portfolio.
- Real-Time Account Health Visibility Across the Full Portfolio: The continuous scoring engine now monitors system health, case trends, network incidents, and usage data live, surfacing at-risk accounts immediately instead of after manual aggregation catches up.
- Systematic, Data-Driven Upsell Recommendations: Every Account Manager now enters expansion conversations with the same quality of evidence-backed recommendations, regardless of tenure or available research time.
- Seven Data Sources Unified in One Interface: Four internal and three external data sources are now accessible through a single conversational query, replacing the fragmented multi-platform research process entirely.
- CSM Capacity Redirected From Research to Revenue: With aggregation, scoring, and recommendation generation automated, CSMs now spend their time on relationship development and commercial execution instead of assembling facts already sitting in the organization's own systems.
“Our account managers used to spend half their day preparing for calls. Now they ask the platform a question and get a complete picture in minutes: health score, open issues, what the customer announced last quarter, and the best expansion opportunity to bring up. It’s changed how we run account strategy.”
– Sales VP, Enterprise Software
Account Managers at this organization spent one to two hours before every call assembling a picture from systems that never talked to each other, and by the time they finished, half of it was already stale. Ksolves AI ML consulting services helped the client replace that ritual with a single conversational interface pulling from all seven internal and external data sources at once.
Account prep time dropped by roughly 85%, at-risk accounts now surface in real time instead of after the fact, and upsell recommendations come from evidence rather than whichever CSM had time to dig deepest. Every Account Manager now walks into a call with the same quality of intelligence, not just the ones with the most tenure or the most spare time that week.
The same aggregation and scoring pattern is ready to extend to any new data source the organization adds to its account intelligence stack going forward.
Is Your Sales Team Still Spending Hours on Manual Account Research Before Every Call?
