Relytic — Reliable AI, engineered.
Relytic designs, builds, and evaluates production AI systems for companies that need more than a convincing demo.
RAG & Knowledge Systems / AI Agents / Document AI / AI Evaluation / Computer Vision
Built for production. Evaluated on real data. Designed around measurable outcomes.
The problem
Relytic designs, builds, and evaluates production AI systems for companies that need more than a convincing demo.
Built for real-world scale
Experience with production knowledge systems operating across large enterprise document collections.
English, Arabic, Chinese, Italian, Portuguese, German, and Spanish in the same enterprise environment.
Production enterprise AI used by hundreds of employees and handling thousands of questions every day.
Improved through structured failure analysis, model benchmarking, data improvements, and production optimization.
Current Mknoon AI chest X-ray classification development benchmark.
Development benchmark
What we build
Our approach
A system that works in a demo is not necessarily a system you can depend on.
Relytic evaluates throughout development so failures become engineering inputs rather than surprises in production.
Depending on the system, we measure retrieval quality, groundedness, extraction accuracy, model performance, latency, cost, failure behavior, human-review boundaries, and production regressions.
Read our approachSelected work
A selection of projects across enterprise knowledge systems, document intelligence, computer vision, and medical AI.
Fortune Global 500 industrial company / RAG / Document AI / Evaluation
A multilingual enterprise RAG platform operating across hundreds of thousands of documents in 7 languages, used by hundreds of employees and handling thousands of queries per day.
Fortune Global 500 industrial company / Computer Vision / Production ML
Model benchmarking, structured error analysis, targeted dataset improvements, and production optimization increased detection performance from 84.5% to 92.0% mAP@50.
84.5% 92.0%mAP@50
Mknoon AI / Medical AI / Computer Vision / Cloud Deployment
End-to-end medical-imaging AI work with current development benchmarks including 95.5% F1 for chest X-ray classification, 85% F1 for dual-view mammography classification, and 65.1% mAP for tumor localization.
Development benchmarks
Engineering decisions are tied to representative tests, measurable outcomes, and observed failure modes.
We work across the complete system - data, models, retrieval, workflows, APIs, evaluation, deployment, and monitoring.
The problem comes before the model. We choose the simplest architecture that can solve the actual business problem reliably.
Latency, cost, security, permissions, maintainability, traceability, failure handling, and monitoring are part of the engineering problem from the start.
How we work
Understand the users, workflow, data, systems, risks, and constraints.
Choose the architecture and define what success means.
Develop the system iteratively and measure whether each change improves the behavior that matters.
Connect the system to the real environment, data sources, and users.
Track production behavior, failures, regressions, latency, and cost where appropriate.
Understand the users, workflow, data, systems, risks, and constraints.
Choose the architecture and define what success means.
Develop the system iteratively and measure whether each change improves the behavior that matters.
Connect the system to the real environment, data sources, and users.
Track production behavior, failures, regressions, latency, and cost where appropriate.
Next step
Book a 30-minute conversation to discuss the problem and what a practical engineering approach could look like.