RAG & GenAI search
Question-answering over your documents with grounded answers and citations. Ingestion, chunking, hybrid retrieval and prompt design tuned to your corpus.
Applied AI consulting · Washington, DC area
Generative AI that survives contact with production.
We design, evaluate and ship LLM, RAG and multi-agent systems for regulated, high-stakes environments — and we measure them, so you know they work before your customers do.
01 — Services
From first prototype to enterprise adoption.
Multi-agent & agentic systems
Orchestrated agents that plan, use tools and check each other — built on Azure AI Foundry, LangChain and the Model Context Protocol (MCP).
LLM evaluation & benchmarking
Automated evaluation harnesses, synthetic benchmark generation and daily regression testing — so launch and migration decisions rest on evidence.
Fine-tuning & deployment
Selecting, fine-tuning and serving open-source and commercial models, balancing quality, cost, latency and interpretability.
AI for cybersecurity
Log parsing, anomaly detection and risk scoring that cut false positives and investigation time for security and support teams.
AI strategy & advisory
Finding the high-value problems, sizing feasibility and building the executive business case — then mentoring your team to own what we build.
02 — Selected work
Outcomes, not demos.
AI chat and semantic search across patient charts
Led strategy, architecture and Agile delivery of a chat assistant that lets providers query patient records in plain language, built on vector and graph retrieval.
[ADD OUTCOME — e.g. time saved per chart review]
Cited question-answering over large PDF libraries
Set the product vision and directed delivery of a GenAI search tool combining LLMs, RAG and citation generation, with document extraction pipelines in PyTorch, LangChain and LlamaIndex.
[ADD OUTCOME — e.g. search relevance gain]
Log anomaly detection and host risk scoring
ML and deep-learning models for log analysis, plus a patented automated preprocessing method that removed most manual data preparation.
30%
fewer false positives
10×
faster investigations
Evidence for a virtual-assistant migration
Built an automated RAG evaluation platform that replaced manual spot-checks and quantified the quality gap between a legacy and a re-platformed assistant.
26K
grounded Q&A pairs tested
11 pt
quality loss uncovered
03 — Approach
Measure first. Then scale.
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Step 1
Discover
Pin down the problem worth solving and what success looks like in numbers.
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Step 2
Prototype
A working proof of concept on your data in weeks, ready to show stakeholders.
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Step 3
Evaluate
Automated test suites and benchmarks that turn "seems good" into a trusted quality signal.
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Step 4
Scale
Production hardening, MLOps and hand-off, with your team mentored to own it.
Platforms and tools we work in
Azure AI Foundry
Databricks
AWS
LangChain
LlamaIndex
Model Context Protocol
PyTorch
MLflow
Vector databases
Graph databases
Open-source LLMs
04 — Founder
PJ Sheini, Ph.D.
Founder & Chief Data Scientist
PJ has spent more than a decade turning emerging AI into systems people rely on — leading conversational AI, multi-agent and evaluation programs for large enterprises, and presenting their strategic value to executive leadership.
His research spans cross-lingual word embeddings (the subject of his Ph.D. dissertation), self-supervised learning for genomic search, and cloud security. He holds a U.S. patent for automated log preprocessing, has published in venues including IEEE and The American Journal of Human Genetics, and has taught graduate and undergraduate courses in data analytics, cloud computing and security.
- Ph.D. Computer Science (NLP)
- M.S. Computer Science (Cloud Security)
- U.S. Patent 12,056,090 B1
- Agile / SAFe delivery leadership
05 — Contact
Have an AI initiative that needs to work?
Tell us what you're building. We'll reply within two business days with an honest view of feasibility and next steps.