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Jordan Hudgens

Current hands-on work

Production AI, grounded in delivery.

I build the systems behind enterprise AI workflows: closed-circuit LLMs, orchestration, encoding and context, retrieval, self-hosted inference and operational integrations. At Encapsa, the platform spans 300+ live deployments across seven verticals.

Co-founder & CTO; eight-person team.

The AI foundation starts with a custom encoding/decoding layer covering 100% of platform data for AI-based lookups. The governed Context Engine extends that foundation. Current work spans closed-circuit LLMs, multiple models, retrieval, NLP-to-SQL and end-to-end workflows for products, internal training and usage tooling.

The 100% figure describes data coverage, not lookup accuracy. Architecture work includes Rust/Axum, Ruby on Rails, Node, React, Temporal, RunPod and Fly.io; a listed technology is not a claim that every planned integration runs in every live deployment.

Encapsa capability map

A map of approved platform capabilities, not the topology of every customer deployment.

Integration
  • ERP, CRM and POS connectors
  • MCP tooling
Orchestration
  • Multi-model routing
  • Agents and tool use
  • Workflow automation
Retrieval
  • RAG and PageIndex
  • Milvus
  • BGE-M3 and Qwen3 embeddings
Data and context
  • Custom encoding/decoding for AI-based lookup across 100% of platform data
  • Governed Context Engine extending that foundation
Inference
  • Self-hosted vLLM
  • Closed-circuit/on-prem options
Workflow engineering
  • End-to-end product, internal-training and usage workflows
  • Temporal-related durable-execution work
Delivery
  • Shared, single-tenant, and on-prem deployments

Commercial insurance · selected team delivery

Commercial underwriting, without the three-day wait

3 days → 15 minutes

Reported submission-to-decision time

Context
Commercial underwriting with a three-day submission-to-decision workflow.
Work
Automated commercial underwriting using ACORD submission ingest, loss-run extraction and risk-memo generation.
Reported outcome
Submission-to-decision: three days to 15 minutes; 300+ staff hours recaptured per month; no added headcount.

Energy services · selected team delivery

Field reporting connected to operational data

Context
Ticketing and reporting applications for an energy-services business.
Work
Replaced ticketing/reporting applications with NLP-to-SQL and workflow automation for an energy-services business.
Reported outcome
Tens of thousands of dollars saved annually.

Education · selected team delivery

A board presentation from enrollment data

Context
Preparing a board presentation from enrollment data.
Work
Generated a board deck using SQL enrollment data and deep research.
Reported outcome
Weeks of preparation saved.

Hospitality · selected team delivery

Training generated from existing documents

Context
New-hire training based on legacy documentation and a prior LMS.
Work
Generated new-hire training from legacy documentation.
Reported outcome
$10K+ per year saved compared with the prior LMS.

At the keyboard

The current practice.

Jordan uses Claude Code CLI, Codex CLI, custom Goose harness, and Mistral daily. He teaches customer teams during every deployment, conducts ongoing training outside deployments, and builds internal training and usage tooling at Encapsa.

Daily coding agents

  • Claude Code CLI
  • Codex CLI
  • custom Goose harness
  • Mistral

As Co-founder & CTO at Encapsa AI, Inc., Jordan leads eight full-time team members: three AI/ML engineers, two full-stack engineers, one UX/UI designer, and two customer-success staff.

Independent consulting

Enterprise delivery before the current AI stack.

Client engagements alongside operating roles, not all direct employers.

Quip

Quip, the oral-care subscription brand, 2015–2017: built the platform from scratch; it scaled to multi-million-dollar revenue.

Eventbrite

Eventbrite, 2014–2015: admin dashboards and Redis-based caching that improved query performance by 90%+.

Chevron

Chevron via Trackr, 2013–2014: field-ticketing systems.

Oxy

Oxy via Wilson Systems/Trackr, 2011–2014: field-ticketing systems; the quality-analysis engine increased ticketing quality by 75%.

Yahoo

Yahoo, Fall 2007–Spring 2008: six-month video-player/CDN integration contract.

See the complete consulting track

OpenSite organization

Code in the open.

Selected OpenSite projects. Organization attribution, not a claim of sole authorship.

opensite-ui

React components for web and AI-driven site-building workflows.

View repository

opensite-skills

Reusable guidance and skills for AI coding agents.

View repository

page-speed-hooks

Typed React hooks for common application behavior.

View repository

Public speaking

From working code to the stage.

Recording published · Confreaks

RubyHACK 2018: Machine Learning Microservices in Rails by Jordan Hudgens

A practical approach to implementing machine learning algorithms into Rails applications.

Archival conference talk. The date shown is the recording publication date.

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Watch on YouTube
Jordan presenting Machine Learning Microservices in Rails at RubyHACK 2018
RubyHACK 2018 · Machine Learning Microservices in Rails
Jordan addressing an auditorium of attendees at the Utah Code for Success keynote
Code for Success · Utah keynote

An archival conversation

On development and teaching.

March 2017 · Archival podcast

My Ruby Story MRS-008: Jordan Hudgens.

A conversation about the path into development and technical education.

The same systems thinking shapes the teaching work. See how the instructor-development program was built.

Teaching record

Teaching at scale. Building in production.

Curricula used by 250,000+ learners across platforms and schools.

300+ live enterprise deployments across seven verticals.

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