Systems we have built and operate.
Every project in this list is in production. Real clients, real workflows, real outcomes. We do not show concepts, prototypes, or slide decks here.
Agentic dispatch system that cut exception handling time by 74%
A US-based freight operator was handling thousands of daily exceptions manually. We built an autonomous dispatch agent that detects, classifies, and resolves exceptions without human intervention.
Read case studyAI underwriting platform processing 3,000 applications per day
A fintech lender needed to scale underwriting without scaling headcount. We built a multi-model risk assessment pipeline with human-in-the-loop escalation for edge cases.
Clinical document intelligence system for a multi-site healthcare group
Manual document review was creating compliance risk and slowing patient intake. We built an OCR and entity extraction pipeline that classifies, validates, and routes clinical documents automatically.
Dedicated engineering pod that shipped 14 features in 90 days
A Series B SaaS company needed to triple engineering output without tripling burn. We stood up a 12-person embedded engineering pod in 3 weeks, operating in US timezone with weekly ship cadence.
Real-time inventory intelligence platform for a multi-channel retailer
Disconnected inventory data across 4 systems was causing stockouts and overstock simultaneously. We built a unified data lakehouse with real-time demand forecasting and automated replenishment triggers.
Zero-downtime cloud migration for a 200-person enterprise
A legacy on-premise system was blocking AI adoption across the organization. We migrated the full stack to AWS with Kubernetes orchestration and CI/CD pipelines, with zero downtime across 6 months.
Every system follows the same delivery model.
Regardless of sector or complexity, every MetaSys engagement runs on the same four-phase model. It is why our outcomes are consistent.
Scoping Call
30 minMap your problem to a system architecture. Walk away with a clear engagement scope and a named engineer assigned.
Architecture Proposal
3-5 daysWe spec the system: agent topology, data flows, integration points, and evaluation framework. Fixed-price document.
Build and Deliver
4-12 weeksWeekly deploys. Engineers embedded in your tools and standups. Working agent in production by sprint two.
Operate and Improve
ongoingPost-launch monitoring, retraining cycles, and capability expansion. We do not hand off and disappear.
Scoping Call
30 minMap your problem to a system architecture. Walk away with a clear engagement scope and a named engineer assigned.
Architecture Proposal
3-5 daysWe spec the system: agent topology, data flows, integration points, and evaluation framework. Fixed-price document.
Build and Deliver
4-12 weeksWeekly deploys. Engineers embedded in your tools and standups. Working agent in production by sprint two.
Operate and Improve
ongoingPost-launch monitoring, retraining cycles, and capability expansion. We do not hand off and disappear.
The stack behind every project.
Every case study above was built using one or more of our five core capability pillars.
I cannot emphasize enough how impressed I am with MetaSys professionalism and commitment to excellence. Their transparency, honesty, and genuine dedication to our project made all the difference.
Zika, GMetrics Solutions, Germany
MetaSys team is exceptionally skilled in their craft, and their ability to tackle complex technical challenges has been a major asset to our company. We are grateful for their unwavering support and technical prowess.
Anja, Train Bullet Proof, United Arab Emirates
The level of integrity and work ethic displayed by MetaSys is truly commendable. Their passion for delivering top-notch solutions while maintaining the highest standards of honesty and transparency is inspiring.
Nick Burton, Legacy Wealth Holdings, United States
Ready to add your system to this list?
Talk to an AI Architect. Get a scoped proposal within 48 hours. No commitment required.
Frequently asked questions
What results has MetaSys delivered for clients?
MetaSys builds agentic AI, automation, and data platform systems that run in production, not prototypes on a slide. Results vary by project, but every case study on this page lists the actual metrics: cycle time reduced, throughput increased, manual review eliminated. Ask about your use case on a scoping call and we will size the likely outcome before you commit to anything.
Are MetaSys case studies from real clients?
Yes. Every case study on this page describes a system MetaSys built and, in most cases, still operates for a paying client. NYMCARD, Legacy Wealth Holdings, and LTS Connecting Things are named where the client approved it. Other studies are anonymized at the client's request, which is standard for enterprise engagements.
Why are some case studies anonymized?
Most enterprise clients, especially in fintech and healthcare, require confidentiality as a condition of the engagement. MetaSys anonymizes those case studies by industry and project type instead of naming the company, so we can still show the architecture and the results without breaking a client agreement. The work and the numbers are real either way.
What industries do MetaSys's case studies cover?
The case studies on this page span logistics and transportation, fintech and banking, healthcare, retail and ecommerce, and SaaS. MetaSys applies the same core capabilities across sectors: agentic AI systems, intelligent automation, and data and AI platforms, adapted to each industry's compliance and integration requirements.
Can I get references before signing a contract?
Yes, ask on your scoping call. MetaSys can arrange a reference conversation with a past or current client where confidentiality terms allow it, and we sign NDAs on request during evaluation. For anonymized projects, we can usually share more architectural detail once you are under NDA.
How do I evaluate an agentic AI vendor?
Ask for production systems, not pilots: request case studies with named metrics, then verify the vendor still operates what they built. Every case study on this page lists real results, cycle time cut, throughput increased, manual review eliminated, and MetaSys still operates most of them for paying clients. A vendor that cannot show a system running in production after launch is a bigger risk than one whose price is higher.
Why do most AI transformation initiatives fail to reach production?
Most AI initiatives stall between pilot and production because the team that builds the prototype hands off to someone else, or the system was never designed for the monitoring, drift detection, and incident response production requires. Every case study on this page reached production, and in most cases is still operated by MetaSys today, because build and operate stay with the same team instead of splitting across a handoff.