Customer Results

Real Workflows.
Measurable Outcomes.

Customer deployments across food, industrial, and discrete manufacturing. Each engagement began with a specific operational problem and a clear success metric.

3 Industries
MRO • Pharma • Production
< 4 wk
Avg. Time to First Value
Plug-In
Works on Your Existing Data
Natural Language
Query Interface
Industrial Manufacturing Multi-Plant MRO & Spare Parts

From Invisible Risk to
Proactive MRO Intelligence

A manufacturer tracked spare parts across three disconnected data sources—inventory, pricing, and consumption. Planners manually cross-referenced them to answer basic questions: strict stock levels and risk.

4 wk
To full deployment
Zero
Unplanned line stops post-deploy

Before

  • Data fragmented across three disconnected data sources (inventory, pricing, consumption)
  • Critical "at-risk" parts only identified after a line stopped
  • Unreliable valuations due to $0 cost errors in the inventory system
  • Hours spent manually cross-referencing sources to check stock levels

What changed

  • Data sources merge automatically every shift—zero manual work
  • Proactive alerts for critical parts falling below safe stock levels
  • Automatic price correction using last-purchase data for accurate valuations
  • Instant answers for floor managers via plain English queries

What the team actually asks it

"Which critical parts at our main plant will run out before the next delivery arrives?"
⚡ Found 4 at-risk critical parts — Bearing SKF-type (8 days remaining, 14-day lead time), Seal Kit A (3 days remaining, 12-day lead time)… Auto-reorder recommended for 3 of 4.
"What's our total critical parts inventory value across both plants this week?"
📊 Critical inventory: Plant 1: $512K, Plant 2: $335K. Total: $847K. 6 parts using price fallback (inventory showed $0). Non-critical: $241K.
"Show me parts with more than 180 days of stock — possible overstock?"
🔍 18 non-critical parts exceed 180-day runway. Top by value: Gasket Set ($18K, 340 days), Filter Kit ($12K, 290 days)… Recommend review for excess disposal.
"What percentage of critical parts are in stock vs. out of stock right now?"
✅ Critical in-stock: 94.2% | Out-of-stock: 5.8% (7 parts). Prior week: 91.3%. Trend improving. 3 of 7 have active POs already in transit.
94%+
Critical Parts In-Stock Rate
vs. reactive discovery only before
Zero
Unplanned Line Stoppages
due to parts shortage since go-live
3 Sources
Auto-Joined in Real Time
inventory • pricing • consumption
Seconds
To Answer Any Parts Query
was hours of manual work
Pharma Manufacturing Multi-CMO Supply Planning

Drug Substance Planning
That Answers “What If”

Four CMO sites, four separate spreadsheets. Because data was siloed, modeling a single demand change required days of manual recalculation across every product and site.

Days → Seconds
Scenario modeling time
4 CMOs
Unified DS/DP/FPP view

Before

  • Siloed data: each planner monitored their own site, blocking a unified view
  • Demand changes required days of manual recalculation
  • Blind batch additions caused overstock or shortages
  • Inventory projections were weeks stale by review time

What changed

  • Unified live view of inventory and batches across all CMOs
  • Instant scenario modeling for demand changes and impact analysis
  • Immediate answers to complex multi-year inventory questions
  • Smart caching provides instant answers to recurrring questions

Scenarios the planning team now runs in seconds

Scenario A — Demand Surge
"If sales increase 20% starting Q2, when does our primary product inventory drop below 3 months on hand at each site?"
⚡ MOH breaches 3.0 at Site A in Aug and Site B in Oct under this scenario. Recommend adding 2 DS batches at Site A in Q1 to buffer. Want me to model that addition?
Scenario B — Batch Addition
"Add 4 DP batches at our secondary CMO in 2027. Show ending inventory impact vs. baseline."
📊 Adding 4 DP batches at Site B increases ending inventory ~18% vs. baseline by Dec 2027. MOH improves from 4.2 → 5.1 months. Overstock risk low. Side-by-side chart ready.
Seconds
Scenario Modeling Time
was 2–3 days per scenario
4 CMOs
Unified in One View
DS • DP • FPP pipeline
Live
Batch & Inventory Tracking
across all products and sites
Real-Time
MOH & Ending Inventory
was always weeks stale
Process Manufacturing OEE / Uptime Maintenance Planning

Machine Uptime Tracking
That Runs Itself

Every shift, someone at this process manufacturer was manually comparing two sources — the maintenance master plan and the actual execution log — then calculating production impact by hand for each machine. By the time the numbers were ready, the shift was already over.

Auto
Uptime calc from maintenance codes
Per-Line
OEE visibility every shift

Before

  • Two separate data sources — a Master Plan and an Execution Log — maintained by different teams, reconciled manually each shift
  • Maintenance codes each carried different hour deductions. Engineers calculated production impact manually per machine, per shift
  • When execution deviated from the master plan, actual uptime was only known the following day after a supervisor reviewed both sources
  • No machine-level OEE tracking — production impact reported as a plant average, masking which lines had chronic downtime patterns

What changed

  • The execution log and master plan merge automatically each shift. When actual execution differs from what was planned, the actual always wins
  • Each maintenance code (weekly service, tool swap, monthly shutdown, major stoppage) has a defined hour deduction. Uptime % is calculated per machine, per shift, automatically
  • Shift supervisors see uptime per line in real time — not a plant-wide average. Machines with patterns of chronic downtime show up immediately instead of hiding in the aggregate
  • Questions like "Which lines had more than 2 hours of unplanned stoppages this week?" go from a 30-minute lookup to an instant answer

How the uptime engine works

Step 1
Ingest Both Sources
Execution log + Master plan loaded each shift automatically
Step 2
Reconcile
Execution overrides master plan. Standard shifts = 0 deduction. Heavy maintenance = hours deducted per code.
Step 3
Calculate Uptime %
Uptime = (24 − maintenance hours) ÷ 24. Per machine, per shift, per day.
Step 4
Surface on Dashboard
Machine-level OEE live for every shift supervisor. Alerts fire when lines drop below threshold.
Standard Shift
Production / No Impact
0 hrs deducted
Weekly Service
Scheduled Maintenance
−1.5 hrs
Monthly Shutdown
Planned Overhaul
−3.17 hrs
Major Stoppage
Bi-Monthly / Quarterly
−19.66 hrs
Real-Time
Uptime per Machine
was known only next-day
Auto
Plan vs. Execution Reconcile
zero manual cross-referencing
Line-Level
OEE Visibility
not just plant averages
Catalog
Driven Rules
update hours, not code

More Case Studies in Progress

Deployments completing now — detailed write-ups publishing Q2 2026

Coming Q2 2026

Automotive Tier 1 — JIT Orchestration

Coordinating just-in-time parts delivery with Tier 1/2 suppliers based on live production pace. Deployed across multiple plants.

Coming Q2 2026

CPG — Multi-Vendor Spend Intelligence

Unifying fragmented packaging vendor data, surfacing invisible spend, and automating reorder across 10+ suppliers.

Coming Q2 2026

Food & Bev — FSMA Compliance Autopilot

Automating FSMA 204 traceability, supplier cert management, and audit trail generation across multiple SKU lines.

Your plant could be next.

Every engagement is scoped around a specific workflow, with documented outcomes — numbers, before state, and final result. No vague claims.

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