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/ PROJECTS — 03

Architecture case studies.

Production-grade AI systems documented in the style of an engineering design review — problem, architecture, tradeoffs, capabilities and lessons learned.

PRJ-01
2026

Hermes

Real-Time Voice Intelligence Platform

A low-latency orchestration platform that transforms live speech into reliable AI conversations through streaming speech recognition, agentic reasoning, retrieval, and neural speech synthesis.

Source↗Docs →
01 — Problem

Building conversational voice AI requires far more than speech recognition. Audio streaming, reasoning, retrieval, tool execution and speech synthesis must all operate together under strict latency constraints while maintaining conversational context.

02 — Architecture
Microphone
Streaming Speech Recognition
Conversation Memory
Planner Agent
Tool Execution + RAG
LLM Reasoning
Streaming Speech Synthesis
Speaker
03 — Tradeoffs

Prioritized modularity and extensibility over a tightly coupled pipeline. Independent services introduce orchestration overhead but enable each subsystem to evolve without affecting the entire platform.

04 — Capabilities
Streaming Speech RecognitionAgent OrchestrationConversation MemoryRetrieval-Augmented GenerationStreaming Text-to-SpeechEvent-Driven Processing
05 — System Stack
PythonFastAPILangGraphRedisQdrantOpenAIWhisperDocker
06 — Lessons Learned

Real-time conversational AI is fundamentally an orchestration challenge. Keeping speech, reasoning, retrieval and synthesis loosely coupled produces systems that are easier to evolve, observe and scale.

PRJ-02
2026

Athena

Enterprise Retrieval Intelligence Platform

A production-oriented retrieval platform designed for trustworthy enterprise knowledge access through optimized search, reranking, vector search and permission-aware retrieval.

Source↗Docs →
01 — Problem

Enterprise search requires more than semantic similarity. Knowledge systems must retrieve accurate information while respecting permissions, reducing hallucinations and scaling across continuously growing document collections.

02 — Architecture
Documents
Chunking Pipeline
Embedding Generation
Vector Database
Hybrid Retrieval
Cross-Encoder Reranking
Access Control
LLM Response
03 — Tradeoffs

Hybrid retrieval introduces additional infrastructure complexity but significantly improves retrieval quality compared with embedding-only search.

04 — Capabilities
Hybrid RetrievalQuery OptimizationCross-Encoder RerankingVector SearchAccess-Aware RetrievalScalable Knowledge Architecture
05 — System Stack
PythonFastAPIQdrantPostgreSQLDockerOpenAIRedis
06 — Lessons Learned

Retrieval quality depends far more on disciplined retrieval pipelines than increasingly larger language models. Well-curated context consistently produces better reasoning.

PRJ-03
2026

Aegis

Trust & Decision Intelligence System

An enterprise AI governance platform focused on explainable reasoning, calibrated confidence estimation and policy-aware decision validation.

Source↗Docs →
01 — Problem

High-impact AI systems require transparent reasoning before decisions can be trusted. Predictions alone are insufficient without explanation, confidence estimation and governance.

02 — Architecture
Input
Evidence Collection
Reasoning Engine
Confidence Calibration
Policy Validation
Decision Report
Human Review
03 — Tradeoffs

Governance layers increase inference latency but substantially improve transparency, auditability and human trust.

04 — Capabilities
Explainable ReasoningConfidence CalibrationDecision GovernanceRisk AssessmentHuman-in-the-Loop ReviewAudit Logging
05 — System Stack
PythonFastAPIPostgreSQLRedisDockerOpenAILangGraph
06 — Lessons Learned

Enterprise AI succeeds when humans understand why a recommendation was made rather than simply receiving a prediction. Trust is designed through architecture, not generated by models.