Applied AI for Technical & Regulated Industries
If your teams can't find answers in your own data, that's the problem we solve.
We build production-grade AI systems — retrieval, agents, and workflow automation — grounded in your private data and operational reality. Engineered for environments where accuracy, traceability, and data control aren't optional.
Built by a PhD chemical engineer and former Silicon Valley CEO with operating experience in energy, industrials, and regulated environments.
Most enterprise knowledge is locked in documents, databases, and email threads that no search engine can meaningfully connect. The cost is measured in hours per analyst, per day.
RAG is not the right solution for every problem. We'll tell you that upfront — and tell you what is. The goal is a working system, not a sold engagement.
What is RAG — Retrieval-Augmented Generation
Retrieval-Augmented Generation grounds a language model in your documents, databases, and institutional knowledge — in real time. The result is accurate, traceable, hallucination-resistant responses drawn from sources you own and control.
We build the full pipeline: ingestion, chunking strategy, embedding, vector storage, retrieval, reranking, and generation — tuned for your latency and accuracy requirements.
Services
End-to-end AI systems — from architecture through production deployment.
Ingest PDFs, contracts, manuals, reports, and internal wikis into a queryable knowledge base. Natural language Q&A with citation-level traceability back to the source.
Text-to-SQL pipelines and semantic layers over relational databases, data warehouses, and APIs. No waiting for the next reporting cycle.
Multi-step systems that retrieve, reason, and act — coordinating across knowledge sources, APIs, and tools to complete real tasks, not just answer questions. For workflows too complex for a single retrieval pass.
For ops, finance, legal, and engineering teams. Slack bots, web apps, or API services — with role-based access control and SSO integration from day one.
RAG system audits, retrieval benchmarking, hallucination testing, and reranker tuning. We measure against agreed criteria and report honestly on what we find.
Full on-premises RAG stacks with local LLMs (Llama, Mistral), local embeddings, and self-hosted vector stores. HIPAA, SOC 2, and export-controlled environments.
AI-driven automation for document-heavy and compliance-bound processes — classification, extraction, routing, and monitoring. Replace manual review queues with systems that flag exceptions and keep humans in the loop where it counts.
Source connectors, document parsing, chunking strategy, embedding pipelines, and vector-store design — built for your data's structure, scale, and refresh cadence.
Use Cases
RAG systems deliver ROI wherever institutional knowledge is siloed, hard to search, or locked in documents.
Ground LLMs in P&IDs, engineering specs, maintenance logs, and safety procedures. Reduce expert search time and surface the right answer for field and office teams alike. Institutional knowledge that walks out the door when experienced staff retire — captured and queryable.
Query evolving state and federal regulatory requirements, producer documentation, and compliance records in plain language. Eliminate manual cross-referencing across disconnected systems. Built for organizations operating under SB 1383, LCFS, and similar frameworks.
Query hundreds of contracts, regulations, and internal policies in plain language. Identify clause conflicts, extract obligations, and surface relevant precedents in seconds.
Ingest earnings transcripts, analyst reports, deal memos, and financial models. Ask complex questions across structured and unstructured data in a unified interface.
Case Studies
The global biochar research community had accumulated over 17 years of irreplaceable knowledge across five email list subgroups. The archive was effectively inaccessible: keyword search couldn't synthesize answers across hundreds of threads, PDF attachments, microscope images, and field reports.
We built a production multimodal RAG system that ingests the full 4.5 GB MBOX archive — email threads, PDFs, and images — into a unified 238,988-vector FAISS index. A custom two-pass retrieval strategy ensures image and PDF results surface alongside text matches. The system handles multilingual queries and runs at $21/month on GCP.
View live system → biocharai.orgAccess password: coolplanet
California's SB 1383 requires jurisdictions to document organic waste disposition with validated invoices and third-party lab data. Manual reconciliation across multiple producers is labor-intensive and audit-vulnerable — each report requires cross-referencing data from four disconnected systems.
We built an automation pipeline that connects Sage ERP, SharePoint, DocuSign, and lab analytics systems into a single workflow. Invoices are extracted, jurisdiction-validated, and matched to analytical files using producer-keyed date-range logic. DSP agreements are auto-routed for signature where required. Output: audit-ready, jurisdiction-specific reporting folders — zero manual cross-referencing.
Process
Scoped engagements from discovery to production. No shelfware.
Audit data sources, query patterns, access control requirements, latency targets, and infrastructure constraints. Deliver an architecture recommendation and fixed-price SOW.
Ingestion, chunking, embedding, vector store setup, retrieval logic, reranker selection, and prompt engineering — built against your actual data, not synthetic benchmarks.
Measure retrieval precision, answer faithfulness, and latency against agreed success criteria. Iterate until targets are met — or explain exactly why they can't be.
Production deployment, monitoring setup, and complete documentation. Optional retainer for ongoing tuning as your data evolves. You own everything built.
Technology
We work with your existing infrastructure — or recommend the right components for your requirements. No vendor lock-in.
About
I build AI systems for the industries I've spent a career operating in — energy, industrials, regulated environments, and enterprise finance. That means I understand the data environments and organizational constraints that determine whether a system actually gets used.
A PhD in chemical engineering and an MBA in finance from Chicago Booth gives me an unusual vantage point: I can sit in the architecture review and the board meeting. The institutional knowledge problem RAG solves isn't abstract to me — I've managed the people whose expertise walks out the door, and the document repositories nobody can search.
The systems I build are production-grade because I'm not interested in shelfware. Fixed-price SOWs, documented handoffs, you own everything built.
AI Projects
Regional energy consumption is highly cyclical and seasonally structured — standard forecasting approaches miss the autocorrelation patterns that drive accurate demand prediction.
Built an end-to-end ARIMA forecasting pipeline on hourly power utility data spanning multiple years. Applied ADF stationarity testing and ACF/PACF analysis for parameter selection. Fit models at weekly and monthly resolutions, identifying seasonal cyclicality including summer demand peaks driven by cooling load.
Address-specific coupon eligibility rules tied to municipal vs. unincorporated jurisdiction boundaries — manual lookup slow and error-prone at scale.
FastAPI service embedded directly in an e-commerce checkout flow — validating customer eligibility against jurisdiction boundary data in real time at the point of purchase. Replaced a peak-staffing manual lookup operation with a single API call returning results in milliseconds. Deployed on Google Cloud Run.
First-of-kind SAF pathway with hundreds of input variables — feedstock pricing, carbon intensity, five stacked policy credits, and co-location economics — too complex to navigate manually across the scenario space.
Used Cursor AI to simulate hundreds of scenario combinations across feedstock costs, §45Z policy outcomes, carbon intensity pathways, and co-location value drivers — identifying optimal capital structure and sensitivity boundaries across a 20-year horizon.
Tell us about your data and your problem. We'll tell you if RAG is the right solution — and what it would take to build it.
Not sure if RAG is the right fit? Tell us the problem and we'll give you an honest answer — including if the answer is no.
We're not the right fit for proof-of-concept projects or teams that need a large vendor with enterprise SLAs. We're the right fit for organizations that want something built correctly, once, and handed over.