CASE STUDY 01 — AUTOMATION
Illustrative Case StudyThe Finance Team That Stopped Chasing Documents.
Industry Multi-entity services company
CHALLENGE
The finance team spent hours every week collecting invoices, checking documents, updating spreadsheets, following up with departments and preparing management reports. Information arrived through email, WhatsApp, shared folders and accounting systems. The process depended heavily on people remembering what needed to happen next.
WHAT GET-LENVOR BUILT
An AI-driven finance workflow connecting incoming documents, approval rules and internal reporting. The system: captured invoices automatically; extracted key information; checked required documentation; routed approvals; flagged exceptions; followed up on missing information; updated the finance workflow; prepared management summaries. Humans remained responsible for final exceptions and sensitive approvals.
REPRESENTATIVE OUTCOME
70% less manual document handling45+ hrs of repetitive finance work removed each month3× faster invoice approval cycle100% workflow visibility
CONNECTED
Email · Accounting · Documents · Approvals · Reporting
THE SHIFT
Before: People moved information between systems.
After: Information moved itself.
CASE STUDY 02 — AI AUTOMATION
Illustrative Case StudyEvery Lead Answered. Every Follow-Up Remembered.
Industry B2B services company
CHALLENGE
New leads were arriving through the website, email, referrals and WhatsApp. Response times depended on staff availability. Some leads were followed up immediately. Others disappeared inside inboxes and spreadsheets. Sales teams also spent significant time qualifying inquiries and updating the CRM manually.
WHAT GET-LENVOR BUILT
An AI sales agent connected to the company’s lead channels and CRM. The agent could: respond to incoming inquiries; understand customer requirements; ask qualification questions; classify opportunities; create CRM records; route high-value leads; schedule follow-ups; remind sales representatives; generate conversation summaries; reactivate inactive opportunities.
REPRESENTATIVE OUTCOME
<2 min average first response100% new leads automatically logged60% less manual CRM administration24/7 lead qualification
CONNECTED
Website · Email · WhatsApp · CRM · Calendar
THE SHIFT
Before: Salespeople managed the process.
After: Salespeople managed the opportunity.
CASE STUDY 03 — MANUFACTURING INTELLIGENCE
Illustrative Case StudyFrom Machine Monitoring to Manufacturing Intelligence.
Industry Industrial manufacturing
CHALLENGE
The factory already collected production and equipment data. But machine information, maintenance records and production schedules existed separately. Teams could see individual machine events but struggled to understand how those events affected overall production. Problems were often investigated after downtime had already occurred.
WHAT GET-LENVOR BUILT
A manufacturing intelligence layer connecting: machine signals; production records; shift data; maintenance history; downtime events; operational thresholds. The system continuously analysed machine behaviour and identified abnormal patterns. When something required attention, the relevant team received the event together with operational context rather than another isolated alarm.
REPRESENTATIVE OUTCOME
31% reduction in unplanned stoppages18% faster maintenance response420+ hrs of potential downtime identified annually24/7 equipment intelligence
CONNECTED
Machines · IoT · Maintenance · Production · Alerts
THE SHIFT
Before: A machine generated an alarm.
After: The business understood what that alarm could affect.
CASE STUDY 04 — ENERGY INTELLIGENCE
Illustrative Case StudyFinding the Energy the Factory Couldn’t See.
Industry Multi-line manufacturing facility
CHALLENGE
The company knew its monthly electricity bill. It did not know precisely which machines, operating patterns and production decisions were driving unnecessary consumption. Energy analysis happened after billing. By then, the opportunity to intervene had already passed.
WHAT GET-LENVOR BUILT
An energy intelligence system connecting smart-meter data with machine and production activity. The system continuously compared: energy consumption; production output; machine status; shift patterns; operating schedules; peak-demand periods. AI identified abnormal consumption and highlighted the operational conditions causing it.
REPRESENTATIVE OUTCOME
14% reduction in avoidable energy consumption11% lower peak-demand exposure22% improvement in energy intensity visibilityReal-time energy anomaly detection
CONNECTED
Energy Meters · Machines · Production · Operations
THE SHIFT
Before: The company knew how much energy it used.
After: It knew why it used it.