POC to Production
Research prototypes, applied AI labs, proof-of-concept systems, and production-inspired experiments built to evaluate business value, risk, and scalability
Discuss your POC to ProdHow We Move from POC to Production
1. Prototype
Test the idea quickly
Start with the smallest useful system: sample data, clean interfaces, evaluation criteria, and a narrow workflow that proves whether the concept is valuable.
2. MVP & Evaluation
Measure accuracy and risk
Evaluate retrieval quality, model behavior, hallucination risk, latency, cost, explainability, security, privacy, and business fit before scaling.
3. Harden
Move toward production
CI/CD, logging & monitoring, agent observability, guardrails, role-based access, cloud deployment patterns, documentation, and measurable KPIs.
Applied AI POC to Prod Gallery
A living portfolio of exploratory systems built to test AI architectures, retrieval quality, agent orchestration, model evaluation, and business value.
Agentic Research Assistant MVP
An AWS-hosted AI research assistant for secure document upload, grounded chat, retrieval, and evaluation using modern agent workflows.
Built with Cognito, LangGraph, Bedrock, FAISS, S3, and CloudWatch. Explore the live MVP to see production-inspired RAG in action.
Cybersecurity Vulnerability Intelligence Agent
A research architecture for analyzing assets, software versions, CVEs, SBOM data, OSINT, and policy rules to produce explainable risk scores and security recommendations.
Focus areas: cybersecurity AI, CMDB enrichment, CVE matching, vulnerability prioritization, deterministic scoring, knowledge graphs, guardrails, audit logs, SIEM integration.
GraphRAG Expert Discovery System
A prototype for turning historical expert records into searchable profiles with semantic retrieval, relationship intelligence, SQL linkage, entity extraction, and knowledge graph traversal.
Focus areas: GraphRAG, expert discovery, vector search, SME profiles, embeddings, knowledge graphs, entity resolution, hybrid retrieval, recommendation systems.
Healthcare & Medical Analytics
Exploratory AI systems for risk prediction, clinical operations, medical imaging context, narrative extraction, and explainable analytics.
Claims & Incident Intelligence
Experimental models for incident-to-claim linkage, claim severity prediction, high-dollar claim risk, NLP reason codes, and operational decision support.
Drone, Sensor & Spatial Analytics
Applied experiments involving telemetry, spatial reasoning, sensor streams, geometry, rates, optimization, and decision intelligence.
Research Themes
Retrieval Quality
Precision, recall, groundedness, source coverage, and answer faithfulness
Experiments compare chunking strategies, metadata filters, embedding models, hybrid search, reranking, query planning, and evaluation rubrics for reliable RAG systems.
Agent Orchestration
State machines, tool routing, deterministic nodes, and human review
Agent prototypes explore how LangGraph-style workflows can route tasks across retrieval, tools, scoring logic, validation, summarization, and deployment-safe outputs.
Evaluation & Observability
Tracing, regression testing, monitoring, and performance measurement
Experimental systems include evaluation hooks for latency, cost, token usage, answer quality, hallucination checks, retrieval accuracy, and workflow-level reliability.