Applied GenAI & ML consulting · Austin, TX

Frontier AI for capital and industry.

Production-grade GenAI and machine learning for institutional finance and advanced manufacturing — documents, sensor streams and operational data turned into answers leaders can act on.

15+ yrs
Architecting AI & data platforms across regulated industries
~$5M
Quarterly savings from a single predictive-quality deployment
4 wks → 10 min
Root-cause analysis time at a semiconductor manufacturer
Mine → Car
End-to-end battery data models, electrolyte to vehicle
Two arenas, one discipline

The same rigor that quants demand of a model, plants demand of a line.

Financial decisions audited by regulators. Production lines measured in microns. The discipline is the same: ground every answer in source data, validate relentlessly, ship what an expert will trust.

Financial Intelligence

Answers from documents no one has time to read.

Cited answers from filings, CIMs and holdings data — for pension funds and institutional investors.

  • Chat with filings & financial data
  • Natural-language queries over holdings
  • Due Diligence Intelligence — QSRs, IIRC memos, chat over the data room
Explore financial →
Industrial Intelligence

Catch the defect before the line does.

Predictive quality, machine health and chat-with-your-plant for battery and automotive manufacturing.

  • Contamination detection (vision on SEM)
  • Battery-life & yield prediction
  • VitalFusion Edge — one plant model over i3X; production loss priced, countermeasures simulated
Explore manufacturing →
Capabilities

From proof-of-concept to production, on your cloud.

Not a notebook and a slide deck. Data plumbing, model serving, evaluation and the MLOps that keeps it running.

GenAI applications

Retrieval-augmented and agentic systems — LlamaIndex, LangChain, AutoGen — with evaluation harnesses, not vibes.

Predictive ML & vision AI

Gradient-boosted models and vision transformers for quality, failure and yield.

Cloud & data platforms

Databricks, Snowflake, Azure AI Foundry, SageMaker, Kubernetes — lakehouse and streaming at scale.

Streaming & IIoT

Kafka, Spark, Flink, MQTT, OPC UA — batch pipelines made real-time across the ISA-95 stack.

MLOps & GenAI ops

MLflow, serving, fine-tuning, caching, CI/CD and RBAC — the backbone that makes a demo dependable.

Strategy & roadmaps

Vendor-neutral: use-case selection, build-vs-buy, and a staged roadmap your team can execute.

How we engage

A path that survives contact with production.

01

Frame the decision

Start from the decision someone must make — an allocation, a line stoppage, a sign-off — and work back to the data and model that would change it.

02

Prove it on your data

A focused proof-of-concept on your real documents and streams, inside your environment, scored against ground truth.

03

Harden for production

Serving, monitoring, access control and streaming plumbing that hold up as load and data drift.

04

Transfer ownership

Documentation, mentoring and hiring support so your team owns and extends the system.

"Integrated electron-microscope and real-time sensor data to predict how foreign-material contamination shortens battery life — turning a four-week root-cause investigation into minutes, and saving roughly $5M a quarter."

— Engagement outcome, leading battery manufacturer (anonymized)
Global Battery Manufacturer $200B+ Public Pension Fund Tier-1 Automotive OEM Semiconductor Fab Class-I Railroad
Let's build

Have a decision worth getting right?

A 30-minute working session on a real use case — the data, the model, the path to production. No pitch deck.