CASE STUDIES · PROVENANCE FIRST
Systems you can inspect, evidence you can trace.
How we build, shown end-to-end. Four in-house architectures we run on ourselves, documented system by system. Representative builds for healthcare, legal, property management, and e-commerce operations. And named client case studies, with the measurement method stated next to every number.
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Explore case studies organized by the type of challenge we helped solve
AI Automation
Representative builds and named studies show practical AI automation systems.
Built in-house at CloudNSite.
The systems we run on ourselves before shipping them to clients. Cold email pipeline, self-learning ad ops, agent observability, agentic RAG.
AI implementation results
Our Track Record
Built in-house at CloudNSite
The systems we run on ourselves, documented end-to-end.
Four deep architecture write-ups of the AI infrastructure CloudNSite built for itself before shipping it to clients. Every claim links to the system that produces it.
A 14-agent cold email pipeline using three warmed sending accounts.
Research, personalization, deliverability, and self-improvement loops. Every approved message must clear account-level safety and daily-send limits.
Read the architectureSelf-learning ad opsAn AI marketing agent that runs the loop, not the campaign.
Hypothesis, execute, observe, learn, repeat. The loop runs every 24 hours inside hard guardrails and journals every decision.
Read the architectureAgent observabilityAI agent observability with every tool call on the record.
Six fields per call, retained for replay. Seven memory layers, each with a job. A guard fires on every memory write.
Read the architectureAgentic RAGAgentic RAG that respects who is allowed to see what.
Hybrid search and knowledge graph across 40+ source connectors. Permission-aware retrieval with an airgap option.
Read the architectureAI use case examples
Sample AI implementation, end-to-end.
This representative build shows the path from discovery and workflow design to a controlled production system.
Reduce Manual Review in Medical Records Processing
Representative regional health plan scenario with 50,000+ monthly claims and 25 adjusters who review medical records for coverage decisions.
A build like this typically runs for four months from discovery through production readiness.
Read full case studyAdjusters verify a structured extract instead of reading the full record first.
Source citations and confidence flags direct attention to uncertain fields.
Exception queues keep coverage decisions under human control.
Private deployment keeps regulated data inside the approved environment.
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