Live deployments. Real numbers.
Five systems in production with paying customers, across three distinct implementation patterns, with zero security incidents. Client names are withheld under NDA and the work is shown by pattern. Every number below is from a real deployment.
- 5in production
- 3patterns
- 0security incidents
Case 01 · Supply chain · Demand planning
Accurate forecasting to minimize cost and inventory.
Statistical forecasting with auto-tuned models for accurate capacity balancing and inventory management.
The situation
Demand planning was driven by financial plans with some sales input. Distribution and purchasing signals were not SKU-specific and were biased, which drove high inventory and annual write-offs.
What we did
Deployed an AI-assisted platform that automated most of the work of sales-history cleansing, SKU classification, and forecast tuning. In use by week two, with fully automated data feeds by week four.
- 7-10pp
improvement in forecast accuracy (SKU / DC / week), where each point added around 0.1 point to EBIT.
- 1 day
to create the first real-data forecast. One week to start using the demand plan in operations.
- $0
upfront cost to get accurate forecasts. A tangible improvement on zero budget.
Case 02 · Inventory · Balancing service, cash, and cost
Minimized inventory needed to maintain service.
A consumer goods manufacturer reduced inventory while improving service and gaining visibility into issues.
The situation
Safety stocks were set in "weeks of supply" based on planner experience. That bloated inventory for high-volume SKUs and left others short.
What we did
Established a monthly, data-driven recalibration of safety stocks, adjusting by normal-distribution factors based on the gap between target and observed service levels, and escalating infeasible outliers.
- -11%
cash released from in-stock inventories, without hurting service or cost.
- +0.2%
service rate for customer orders, from improved inventory efficiency.
- $100-150K
reduction in annual write-offs, with visibility of infeasible stock positions.
Case 03 · Voice AI · Inbound demand capture
Every overflow call, now a booking.
A leading skin-aesthetics group was losing high-value bookings to overflow and after-hours calls.
The situation
Around 9,000 calls a month, with roughly 6% lost to overflow and overnight gaps. About 540 high-intent bookings ringing out every month.
What we did
A voice AI integrated with their scheduling and CRM answers 100% of overflow and after-hours calls, books the visit, and lets them right-size live agents onto high-touch service.
- +$1.4M
annual bookings recovered. Calls that used to go unanswered, now booked.
- 100%
answer rate, including overflow calls. Up from 94%, every after-hours call covered.
- 70%
of callers book a visit. Average basket $300, now fully protected.
Case 04 · Deployment · LTL to FTL aggregation
Freight consolidation and carrier selection.
A multi-warehouse replenishment problem solved to maximize weight, volume, and cost while controlling delivery time.
The situation
Deployment planning started from an unconstrained, fractional-pallet, FTL-unaware set of requirements. Planners used common sense to form shipments, worked overtime, still missed delivery windows, and booked costly carriers.
What we did
Deployed a scenario-based optimization engine that prescribes the best plan and supports real-time adjustments to maximize loads, ship more frequently, and eliminate the team's overtime.
- Up to 15%
reduction in number of shipments, while improving delivery timeliness.
- 5x / week
shipping instead of once or twice, for smoother warehouse utilization and better carrier rates.
- No overtime.
Balanced team capacity and a smaller payroll as the toolset picked up the heavy load.
Case 05 · Voice AI · Speed-to-lead
Every lead reached, in seconds not days.
A leading playground manufacturer with four brands could not keep pace with inbound lead volume.
The situation
550 inbound leads a week across four brands, with agents able to reach only 37%. Most of the pipeline went cold before anyone called.
What we did
Voice AI fires the instant a lead enters Salesforce. It calls, pre-qualifies, logs and categorizes the ticket, follows up by email up to four times, then routes a warm lead to the right brand's sales team.
- 37% to 62%
of leads reached. Persistence, not magic: the AI tries every lead up to four times instead of once.
- Instant
first call on every lead, triggered the moment it lands in Salesforce.
- 550 / week
leads worked across four brands, up to four attempts each.
Case 06 · Supply chain · Production planning and scheduling
Optimized production planning and scheduling.
The situation
Capacity planning and scheduling ran on rolling-average forecasts and complex spreadsheets. Planners produced weeks of simplified plans at a time and took overtime every cycle.
What we did
A fully automated toolset synchronizes statistically tested demand plans with capacity plans and production schedules, reducing overtime, minimizing changeovers, and supporting radical inventory optimization.
(in progress)
A demand-aligned supply plan and production schedule to manage purchasing, improve throughput, and reduce overtime. (In progress, improvements being measured.)
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