01. Context
The Challenge
Despite significant development, a mid-market manufacturing-as-a-service company ("IndustriQ") has outdated internal systems. IndustriQ relied on a fragmented mix of antiquated software, disconnected spreadsheets, and paper-based approvals for sales, operations, and customer service. Leads were recorded in infrequently updated CRM fields, sales teams manually generated quotes, and supervisors used gut feel and Excel sheets to plan production. Customer service representatives navigated various screens and chat windows without a consolidated view of orders, inventories, or SLAs. This turmoil posed significant commercial risks: - Missed revenue opportunities: Up to 30% of high-value leads were not followed up on time or became stuck in handover gaps between sales and operations. - Inefficient operations: Manual data entry and approval processes slowed production planning, resulting in inventory misalignment and missed delivery windows. - Poor customer experience: Service teams were unable to answer basic questions (e.g., "Where is my order?" or "When will the next batch ship?") without escalating and waiting for hours. - Decision-making lag: Executives lacked real-time visibility. They relied on monthly reports, which were already out of date by the time they arrived on their desk. Leadership wanted to quadruple income in two years, but they recognised that scaling on this manual base would only add complexity and errors. They hired Aelius Venture to create an AI-powered revenue engine that automates workflows, unifies data, and enables real-time decision-making.
02. Solution
How We Solved It
Aelius collaborated with IndustriQ to create a centralised AI-powered workflow that integrated sales, operations, and customer service into a single intelligent platform. The system follows an "AI-first, automation-second" paradigm, with AI interpreting context and making recommendations, while automation handles execution. 1. Integrating Data and Processes Aelius defined and digitised the company's revenue lifecycle,which includedg lead capture, quote creation, order confirmation, production planning, fulfilment, support, and retention. They established a central data lake that received data from: - Legacy CRM. - ERP and production systems. - Phone/CRM logs and live chat. - Email and contract repositories This unified data foundation enables AI models to "see" the entire customer experience and business context, rather than just isolated silos. 2. AI-driven sales orchestration For sales, Aelius implemented the following: - Intelligent lead scoring and routing: Using previous conversion statistics, lead source, engagement level, and deal size, an ML model prioritised leads and routed them to the appropriate sales people. - Automated quote generation: An AI assistant generated price models and quotations based on product configuration, volume, and agreed terms, lowering quote turnaround time from days to minutes. - AI-generated nudges (via CRM alerts or Slack messages) helped reduce blocked deals by prompting follow-ups, document requests, and approval reminders. Sales staff can now prioritise high-value discussions over paperwork, while leaders can monitor funnel health in real time from any device. 3. Intelligent Operations and Production Planning For operations, Aelius introduced: - Real-time demand forecasting: An AI model used historical orders, seasonality, and market signals to estimate short- and medium-term demand, allowing for proactive capacity planning. - Automated production scheduling: The system recommends ideal production runs, material requirements, and machine time allocations, with manual overrides available for plant management. AI triggers alerts and recommends corrective measures when inventory thresholds, delivery timelines, or quality-control flags exceed their limits. This lowered planning cycle time by more than 60%, allowing the company to respond quickly to urgent orders and disruptions. 4. AI-enabled customer service and retention For client support and retention, Aelius created: - Agents had access to a unified 360° customer view, including order history, open tickets, SLAs, and production status, with real-time AI insights. - Conversational AI assistant: A chatbot answered basic questions (order status, delivery ETA, policy questions) and escalated only complex issues with full information, cutting handling time by 40%. The predictive algorithm identified accounts at risk of churn based on support frequency, declining order volume, and satisfaction levels, enabling proactive retention marketing. The end result was a consistent, rapid, and intelligent customer experience that strengthened trust and loyalty. 5. Real-time dashboards for "AI-First" decision-making. Aelius implemented interactive dashboards that allowed executives to: - Track live KPIs (revenue, conversion rates, lead velocity, on-time delivery, and CSAT). - Test "what-if" scenarios by changing pricing, capacity, and marketing expenditure parameters. - Evaluate AI-generated recommendations for inventory, staffing, and marketing improvements. This environment transformed gut-feel decisions into AI-guided solutions, reducing the decision loop from weeks to minutes.
03. Impact
Results
Within 14 months of implementing Aelius Venture's AI-powered revenue engine, IndustriQ experienced significant gains in company performance and operational efficiency. - Revenue: The company saw a 32% rise in yearly recurring revenue. - Improved sales efficiency: Reduced quote turnaround time by 75% and boosted average transaction size by 18% using AI-powered upselling and cross-selling suggestions. - Operations: Reduced manual planning and coordination by 65% and increased on-time delivery from 78% to 94%. The AI chatbot and guided processes improved customer service by cutting the average first-response time by 50% and resolving 27% of escalations without needing human help. - Cost savings: Automation and improved resource allocation decreased internal operational overhead by 1.5 full-time equivalent roles for each function. IndustriQ now has a scalable AI-native platform that can adapt to changing business needs. AI-powered workflows may accommodate new goods, markets, and compliance requirements without needing to rewrite core systems.
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