Field Notes From Real Deployments.
No vendor-sponsored surveys. No AI hype. What follows are findings from actual enterprise engagements — sanitized of client names where needed, but intact on the facts.
How a Tier-1 European Bank Cut Fraud Losses 43% — Without Replacing Its Oracle Warehouse.
A top-5 European retail bank was losing an estimated €180M/year to card-not-present fraud. Its existing rule-based system ran on a 14-year-old Oracle warehouse nobody wanted to touch. A full platform replacement was projected at €60M and 3 years — neither of which the CFO was prepared to approve.
We deployed Linkswave's anomaly-detection layer directly alongside the existing Oracle instance. Read-only access. Zero downtime. Zero data migration. The AI layer learned the bank's transaction patterns, customer behavior, and fraud typology from 3 years of historical data in its first 6 weeks.
By week 9, the model was running in production alongside the rule engine — flagging suspicious transactions for human review. By month 6, fraud losses had dropped 43% vs. the trailing 12-month average. False-positive rates fell 67%, meaning fewer legitimate customer calls to the fraud-ops team.
Total project cost: under 4% of the proposed replacement. Time to measurable value: 9 weeks.
What We're Seeing This Quarter.
How AI Is Rewriting the Economics of Legacy Banking Cores
A practical brief on deploying AI without replacing 30-year-old mainframes — what works, what doesn't, and the ROI playbook.
Read the brief →Regional Hospital Network Cuts Readmissions 21% With Risk Scoring on Existing EHR
Eleven hospitals, one shared Cerner instance, 420k patient encounters per year. Models deployed in 11 weeks with zero clinical workflow changes.
Read the case →Global Fashion Retailer Recovers $18M in Lost Margin Through Dynamic Pricing
1,400 stores, 12 e-commerce channels, SKU-level pricing optimization on Oracle Retail. 3.2-point margin lift in the first full season.
Read the case →The Hidden Cost Curve of Enterprise LLM Deployments
Original analysis across 47 production LLM deployments — what inference really costs, which use cases pay back, which ones never will.
Read the research →Automotive Tier-1 Supplier Lifts Yield 3.4 Points Without Touching MES
Eighteen plants, four continents, SCADA-to-AI pipeline deployed in parallel to existing Siemens stack. Payback in 14 weeks.
Read the case →Global 3PL Cuts Last-Mile Cost 22% With AI on Top of Existing TMS
No TMS replacement. Real-time optimization layer reading from Oracle OTM and customer order feeds. Scaled to 18 countries in under 6 months.
Read the case →Why "AI Governance" Is Mostly About Data Lineage
A short, opinionated piece on why the hardest problem in enterprise AI is tracking which data shaped which decision — and how to solve it.
Read the article →National Grid Operator Forecasts Load 18% More Accurately
Regulated utility, SCADA + smart-meter data, forecasting layer running on-prem for data-sovereignty reasons. Savings passed to ratepayers.
Read the case →Text-to-SQL in the Wild: What 200 Production Deployments Taught Us
The failure modes vendors don't advertise, the guardrails you actually need, and why "conversational BI" is harder than it looks on a demo.
Read the research →One Email, Once a Month.
New case studies, original research, and architecture notes from the field. Written by the practitioners who ran the engagements — no marketing layer in between.