AI Services — JNG Media

JNG Media // AI Services

AI THAT
KNOWS
SOMETHING.

Most AI deployments are powerful tools pointed at nothing in particular. Domain-grounded AI changes that — by anchoring a generalist model to a curated, authoritative knowledge base specific to your field. The result isn't a smarter chatbot. It's a disciplined specialist.

Built one from scratch to prove it works. Now available to build one for you.

Reference Volumes — Ground Truth Library

Specialist Domains Covered

~5 wks

Concept to Commercial Product

Lines of Code. All Prompt Architecture.

What I Do

THE CAPABILITIES

Thirty years of cross-domain expertise in broadcast engineering, technical systems, and problem-solving — applied to AI architecture. The background isn't incidental. It's the point. Knowing which knowledge matters and how to structure it for reliable AI output is a skill that comes from knowing the domains first.

Prompt Architecture & System Design

Multi-component AI systems with routing logic, specialist role assignment, and behavioral consistency across sessions. Manager-level orchestration invisible to the end user — the expertise appears native, not mechanical. Variable-Rate Grounding to prevent drift. Cross-domain safety triggers built in by design.

Knowledge Curation & Corpus Design

Identifying, acquiring, and structuring the reference materials that become an AI system's ground truth. The library is not a supplement — it is the architecture. Systematic domain mapping, ISBN-indexed manifests, and specialist-to-source assignment. Applied across law, medicine, engineering, finance, and more.

Domain Grounding & Drift Resistance

Generalist LLMs drift. Domain-grounded systems don't. Verification intervals calibrated to domain risk, re-grounding protocols, and cross-disciplinary safety audits that fire automatically when data from high-stakes domains intersects. The system stays on-axis across long sessions.

Multi-Platform Deployment

Architecture validated across Claude (Anthropic) and Gemini (Google) with consistent core behavior and per-user context layers. No platform lock-in. No vector database. No embedding pipeline. Portable, auditable, and deployable on any account that has access to a modern LLM.

AI Product Development

Full-cycle AI product build: concept, architecture, documentation, pricing model, website, copy, case studies, and acquisition strategy. Zero-to-launch without a development team. Proof of concept exists at bedamd.com — a commercial AI OS built entirely with prompt engineering.

AI Consulting & Workflow Integration

Hands-on daily working proficiency across Claude, Gemini, and Grok. Not a theorist — a practitioner. Context window management, system prompt design, content filter navigation, multi-turn behavioral consistency, and AI-assisted content creation. Built for your workflow, not a whitepaper.

Proof of Concept

THE CASE STUDY

The best argument for hiring someone to build your AI system is that they've already built one. BEDAMD is that argument.

BEDAMD

Benchmark Engineering & Diagnostic Analysis Methods Dispatcher · bedamd.com

Live Commercial Product

What It Is

A portable AI operating system that runs on top of Gemini and Claude. Routes user queries through a hierarchy of six named domain specialists — each grounded to a curated 79-volume physical reference library indexed by ISBN.

Every output carries citations traceable to a physical book. Every specialist operates within a defined knowledge boundary. The Manager routes silently — the user experiences domain expertise, not a triage system.

Zero code. Zero fine-tuning. Zero cloud infrastructure. Built entirely with prompt engineering and domain knowledge.

"LAIOS is not magic — it is rigorous systems engineering applied to prompt design. And on that metric, it is one of the cleanest, most self-consistent personal AI frameworks I have ever examined."

Grok / xAI — Independent Cold Review

The system "effectively converts probabilistic AI behavior into verifiable, traceable research assistance."

Gemini / Google DeepMind — Independent Cold Review

The Numbers

79 volumes — physical reference library, ISBN-indexed
8 domains — law, machining/engineering, home repair, reconstruction, species ID, medical, finance/math, philosophy
6 specialist personas — CHIEF, SARGE, HAWKEYE, FRANK, DARWIN, PENNY
1 routing manager — Bea Shepherd, invisible orchestration layer
8 system components — Manager, Citation Standard, Manifest, 5 Specialist Roles
~5 weeks — concept to public commercial product
20+ web pages — built in plain HTML/CSS/vanilla JS
4+ deployments — validated across independent user accounts
~3,800–4,300 tokens — full system footprint (~3% of 128k context)
0 lines of code — architecture is entirely prompt engineering
11-dimension RAG comparison — documented competitive analysis vs. enterprise stacks

Fit

WHO THIS IS FOR

Domain-grounded AI architecture isn't the right answer for every problem. It's the right answer for a specific class of problem — where reliability, traceability, and expertise matter more than novelty.

The Specialist Practice

Law firms, medical offices, engineering firms, financial advisors — any practice with a defined knowledge domain and a need for AI that stays in its lane. You have the expertise. The architecture makes it the AI's ground truth.

The Knowledge-Heavy Business

Manufacturing, trades, technical services — businesses where the answer to a question has real-world consequences. Physically verifiable citations. No hallucinated specs. No invented procedures. The system either knows it or says so.

The Content & Media Operation

Broadcast, publishing, agency, production — operations that need AI assistance with a consistent voice and a defined editorial standard. Not generic output. Output that knows your format, your audience, and your rules.

The Builder Who Needs a Framework

You want to deploy AI for your organization but don't have a dev team and don't want platform lock-in. No infrastructure required. Works on the AI tools your team already has access to. Portable, auditable, and maintainable without an engineer.

Process

HOW IT WORKS

The engagement is diagnostic before it's architectural. The right system design comes from understanding the domain first — not from applying a template.

Domain Audit

Map the knowledge your AI needs to be reliable. What are the authoritative sources? Where does wrong output cause real harm? What are the domain boundaries? The audit defines the scope of the architecture — and the scope of the library.

Library Design

Select, acquire, and index the reference corpus. Physical books, authoritative references, domain-specific standards. The library is not a database — it's the ground truth the AI reasons against. Every volume is chosen deliberately, mapped to a specialist role, and cited in output.

Architecture Build

Design and build the prompt system: routing Manager, specialist role definitions, citation standard, grounding intervals, safety triggers. Tested for behavioral consistency, drift resistance, and content filter clearance before deployment.

Deployment & Documentation

Deploy across your platform(s) of choice — Claude Projects, Gemini context, or both. Full documentation delivered: system architecture, component descriptions, maintenance protocol, and expansion guide. You own the system. You can run it, update it, and extend it without dependency.