AI products in production, not decks.
Predictive Wealth Management
Relationship managers spent 6+ hours per client each quarter manually assembling portfolio reviews across 12 legacy systems, and only the top 8% of clients received proactive rebalancing advice.
We built a retrieval pipeline over the bank's holdings, transactions and research desk notes, then fine-tuned a mid-size model for portfolio commentary constrained by an in-house compliance rulebook. A human-in-the-loop review UI lets RMs approve or edit every recommendation before it reaches the client.
- →72% reduction in review preparation time
- →11× more clients receiving quarterly proactive advice
- →0 compliance incidents across 14,000+ generated reviews
Autonomous Inventory Tracking
Cycle counts across 40+ distribution centers were performed manually every 6 weeks, causing 3-4% inventory shrinkage and blocking same-day fulfillment SLAs for the client's largest retail accounts.
We trained a YOLO-based detector on 180k labeled pallet images and deployed it to Jetson edge devices mounted on existing forklifts. Detections stream to a Kafka pipeline that reconciles against the WMS in near real time, with a supervisor dashboard for exceptions.
- →Inventory accuracy from 94.1% → 99.6%
- →Cycle count labor reduced by $2.4M/year
- →Same-day fulfillment SLA hit 98.9%, up from 91%
Ambient Clinical Scribe
Clinicians reported 2.1 hours of after-hours charting per day ("pajama time"), the leading driver of burnout attrition. Off-the-shelf scribes failed specialty-specific terminology and lacked defensible evals.
We built a HIPAA-compliant pipeline: on-device audio capture, Whisper-based diarized transcription, and a specialty-aware note generator producing SOAP notes mapped to ICD-10 and CPT codes. A physician-led eval suite scored every model change against 1,200 gold-standard encounters before promotion.
- →Documentation time reduced 63% (2.1h → 47min/day)
- →94% of notes accepted without edit by attending physicians
- →Provider NPS +38 after 90 days
Claims Triage Copilot
Adjusters manually reviewed 40-page PDF claims packets, spending 45 minutes per claim just extracting facts before any decisioning could happen. Backlog was growing 8% quarter-over-quarter.
We built a document understanding pipeline that extracts structured claim facts, cross-references policy terms, and drafts a triage recommendation with citations back to source pages. Adjusters review a side-by-side UI and approve, edit, or escalate.
- →Time-to-triage cut from 45min → 6min per claim
- →Backlog cleared in 11 weeks
- →$1.8M projected annual savings in loss adjustment expense
Predictive Maintenance for CNC Fleets
Unplanned CNC downtime across 3 plants cost roughly $9,000 per hour and disrupted just-in-time deliveries. Existing SCADA alerts fired too late to intervene.
We instrumented 220 machines with vibration and current sensors, trained anomaly detection models per spindle class, and shipped a shop-floor tablet app that surfaces the top 3 at-risk assets each shift with recommended actions.
- →Unplanned downtime reduced 41%
- →Mean time to detect degradation: 14 days → 38 hours
- →Payback achieved in month 4
Merchandising Intelligence Assistant
Buyers and planners waited days for BI teams to answer questions like "which SKUs are underperforming vs. last season in EMEA?" — slowing weekly assortment decisions.
We built a semantic layer over the retailer's data warehouse and a governed RAG assistant that answers merchandising questions in natural language, returning charts, SQL, and confidence indicators. Every answer is traceable to the underlying query.
- →Analyst request queue reduced 68%
- →Weekly assortment decisions now made in 1 day vs. 5
- →Adopted by 340 merchandising users in first quarter