01Enterprise AI
Governed multi-tenant knowledge intelligence
Enterprise Knowledge Base Multi-Agents
AWS Bedrock multi-agent knowledge platform with multi-source ingestion, OpenSearch retrieval, governance, and FinOps telemetry.
Designing intelligence for the real world
I architect AI systems that earn trust in production.
Agentic AI, intelligent platforms, and engineering systems built for reliability, scale, security, and measurable business value.
Production intelligence system
Agent coreORCHESTRATE
01ModelsRoute & evaluate
02ContextRetrieve & ground
03ToolsAct with control
04GuardrailsObserve & govern
System healthy · All signals nominal
01 / Selected work
Systems, not demos.
Production-minded projects spanning enterprise agents, edge AI, civic intelligence, healthcare, and high-throughput platforms.
02Agentic AI
Governed enterprise actions
Enterprise MCP Gateway
A governed integration layer that turns enterprise APIs into safe, versioned tools for production AI agents.
03Edge AI
80% less bandwidth
CGC Sentinel
Edge computer vision for drone operations, moving decisions closer to the camera and reducing network pressure.
04Civic Intelligence
Explainable operational insight
StreetSenseAI
An open-data intelligence system for understanding roadworks, disruption, and operational risk across UK streets.
05AdTech Systems
Designed for sub-5ms execution
AgentBid
An open architecture for real-time bidding operations, combining a high-throughput data plane with explainable AI assistance.
06Healthcare AI
One story, role-aware views
Nuvro Care Intelligence
A multilingual care-intelligence platform that turns a fragmented autism journey into a coherent, role-aware case narrative.
07Women’s Health
Clarity before protocol
Mauri
A phenotype-led guidance and recovery-tracking experience for women navigating PCOS, endometriosis, and hormonal health.
02 / Technical case studies
Decisions under pressure.
Architecture is most useful when the system is ambiguous, constrained, or already failing. These are the decisions behind the diagrams.
CS–01
The Tuesday latency spike that revealed an agent feedback loop
A production AI assistant looked healthy at the infrastructure layer, yet response times and token consumption climbed sharply every Tuesday.
01Signal
Tracing showed that a planning agent was treating its own generated output as fresh evidence, expanding context and repeating work across the loop.
02Architecture move
I introduced bounded orchestration, model routing, semantic caching, token budgets, and task-level observability rather than adding more compute.
03Outcome
40% lower AI API cost, a stable latency profile, and a system the team could reason about during incidents.
03 / Engineering handbook
The operating principles behind the work.
A living field guide for architects, engineering leaders, and teams building AI systems that must survive contact with production.
Field note / 01
Most projects fail from human misalignment before they fail from technology.
- ↳Write decisions, not just requirements
- ↳Make ownership visible at system boundaries
- ↳Prefer small, reversible bets under uncertainty
04 / Open source
Ideas made inspectable.
Tools and reference architectures built in public—because strong technical thinking should be testable, reusable, and open to challenge.
MCPPackage
01 / 04
MCP OpenAPI Doctor
Convert OpenAPI specifications into reliable MCP tools with validation, security guidance, and predictable contracts.
SSARepository
02 / 04
StreetSenseAI
Open-source civic intelligence built around UK street works data, explainability, and operational usefulness.
ADXBlueprint
03 / 04
AdTech Intelligence Stack
A Rust, Kafka, and ClickHouse blueprint for transparent attribution, privacy-aware identity, and real-time ad operations.
AIAIn progress
04 / 04
AI Architect Handbook
Practical patterns for agent systems, evaluation, observability, platform design, incidents, and technical leadership.
05
About / Architecture with ownership
I stay close enough to the code to keep the architecture honest.
I am an AI architect and engineering leader with 15+ years across software, cloud platforms, data systems, edge computing, healthcare, civic technology, and agentic AI.
My work connects executive intent to production reality: clarifying the problem, drawing the system boundary, making trade-offs explicit, and helping teams deliver safely.
Agentic AIAI platformsMLOpsCloud & edgeTechnical leadershipProduct architecture
Build the right system
Have an AI problem that needs architecture, not another demo?
Available for AI architecture, agentic platform, and technical leadership conversations—India and remote worldwide.
shad.edims@gmail.com