ericpendleton.com
Rev. Oct 2026 · Boston

G. Eric
Pendleton

Communications and AI enablement. Human judgment, governing AI.

Eric Pendleton, smiling, in a grey T-shirt against a white wall.

Training, quality systems and communications in pharma, healthcare and finance taught me to make complicated work clear, controlled and able to survive an auditor.

Now I bring that discipline to AI. I start with the problem, not the technology. I define the product, direct the AI that builds it, test it in real use, and keep at it until it behaves.

Contact LinkedIn ↗ Work samples About
Working systems

Two things I directed AI to build. Both are live. Go poke at them.

The Enterprise-D Flight Deck at red alert: shields up as a Borg Cube fires a green beam at the ship, with helm, tactical and long-range sensor panels around the view.

Take the conn.

Enterprise-D Flight Deck

A starship flight and tactical simulator. Scan anomalies, rescue derelicts, beam people around, blow things up. Fly the ship yourself, or let the crew do it for you, Captain Lazy.

37 days oldBuild 110Browser
Try it ↗
Origin story

Born September 10, 2026 · 37 days old

Why

I wanted to play an old Star Trek game. It doesn't exist anymore. The Windows version survives, but those things never run well on a Mac, and the juice wasn't worth the squeeze.

So I tried the online Star Trek games. I'm not a big gamer. The few I found were restrictive, stingy with free resources, and confusing. I just wanted to admire the Enterprise. Fly around. Fly it myself, or put it on autopilot and let the crew do the work.

On September 10, 2026, I decided to build my own.

How

The game became the experiment. Could someone who doesn't write code direct AI to build something truly complex, then test it, debug it from its own flight logs, and keep piling on features without losing control?

I set the direction and wrote the specs. I played every build and read the telemetry. ChatGPT and Codex wrote the code. Claude and Gemini reviewed each build independently before release. Saucer separation, fleet behavior, docking, mobile controls and far too many edge cases came out of that loop.

Turns out, yes. Build 110, day 37.

Source and build records on GitHub ↗

Unofficial, non-commercial fan project. Not affiliated with or endorsed by Paramount or CBS Studios.

LiveCue during its demo: a live transcript of a financing discussion with terms highlighted, and explanations of a term sheet, a nonrefundable deposit and a non-binding date in the side panel.

It's OK to zone out.

LiveCue

Live transcription for meetings that quietly explains the terms, acronyms and names flying past, and suggests a smart question to ask. No frantic Googling. No pretending you know what pari passu means.

v1.7.0-dev.22Web appAudio not stored

Works in Chrome or Edge, on a laptop or a phone (not Safari yet). On a phone, add it to your home screen and it opens like an app.

Try it ↗
How it was built

In complicated business meetings, financing, legal and tech terms flew by faster than anyone could look them up. So I built the help I wanted.

I defined the product and the interface, directed the AI that built it, tested it in real meetings, and had each release reviewed independently before it went out. The demo runs without a key. For live use, you bring your own Gemini key, which is never stored on the server.

Source on GitHub ↗

The Gaia development dashboard: a management update headline, a what-changed panel, a status bar and the first of three workstream cards, with figures and names blurred.

Gaia Development Dashboard Private

A complicated project shouldn't require a complicated explanation.

One view of financing, milestones, partners and dependencies for a live infrastructure venture. AI reads the week's documents, calls and correspondence and drafts the update; I check every figure against its source before it goes on the board. Built for facts that won't sit still. Figures and names blurred here. Access on request.

The Hub In progress

Because “where the hell did I leave that?” is not a workflow.

I work across ChatGPT, Claude, Gemini, Gmail and Drive. Each one remembers its own conversations. None of them remembers the others'. So I built a boring source of truth with a smart layer on top: plain, static tools, a Drive folder, a sheet, a protocol document, that every AI reads from and writes to under the same rules. The static tools became the dynamic undergirding.

The payoff is that each project is about that project's goals, not about hunting down where the last decision went. The infrastructure is built. The write-up is slow. Going from frantic to organized is good for your blood pressure.

Work samples

Two PDFs. Read them here or take them with you.

First page of Selected Work Samples

Selected Work Samples

14 pages · PDF · 2 MB · August 2026

Eight samples from twenty years inside corporate L&D, plus the early AI record. A sole-authored white paper on competency-based training. A train-the-trainer course built and facilitated for a global biopharma client. A ten-step needs-analysis instrument. A reusable course template. A published article on AI text analytics. Onboarding guides for an enterprise NLP platform. Documentation at scale.

View ↗ Download
First page of the finance writing samples

Finance Emails and Blog Articles

4 pages · PDF · 0.8 MB · 2023

Four client pieces written for Pendleton Financing, my commercial-lending brokerage: why lenders want an LLC as the borrower, adaptive reuse, getting started in long-term rentals, and 1031 exchanges. Plain English for people about to sign something.

View ↗ Download
Also published

5 Steps to Unlocking the Value in Unstructured Big Data ↗ Quirk's Media, 2017

How I work with AI

AI made me dramatically faster. It also gave me more places to lose the thread.

So every project runs on four rules borrowed from regulated work, where nothing is finished until someone else has checked it and the evidence is on file.

Spec before build

Nothing gets built until the problem, the audience and what "done" means are written down.

Independent review

A different model reviews the work. No AI grades its own homework.

Evidence before release

Tested in real use, checked against logs and sources. A claim without proof doesn't ship.

One current version

One record of the truth, updated in place. Not a pile of drafts that disagree with each other.

AI can generate learning content very fast. It cannot certify that anyone can now do the job.

Selected work samples, 2026

A tool will not fix a broken process any more than training fixes a hiring mistake.

Selected work samples, 2026

Generating information is easy. Keeping track of what actually happens, what matters, and what needs attention is not.

The Hub project sheet, 2026

In a 500-page knowledge base, a quiet fabrication survives review because nobody rereads page 340.

Selected work samples, 2026

The most common failure of AI training: a demo, a burst of enthusiasm, and no behavior change.

Selected work samples, 2026
About

Where the discipline comes from

I make complicated work clear enough to act on and controlled enough to survive an audit. That started in corporate training and quality systems at Takeda, Alexion, Shire, Citizens Bank and others, where nothing is finished until someone else has checked it and the evidence is on file.

Now I apply the same discipline to AI. At Lunchbox Labs, an animation studio where I'm a partner and executive producer, I run a human-governed, multi-model AI review workflow for contracts, operating agreements, financial records and investor materials. At Gaia Eco Developments, a regenerative-infrastructure venture where I'm an equity partner, I write the investor narratives, executive briefs and decision materials. The two projects above came out of the same habit: define the product, direct the AI, test it in real use, keep at it until it behaves.

Before that: six years in France, first teaching, then corporate communications and company-wide English training at bioMérieux in Lyon. Fluent French. Conversational Spanish. Boston, remote-first.

Currently
Partner and Executive Producer, Lunchbox Labs · Equity Partner, Gaia Eco Developments
Education
M.S., Business Management, Emmanuel College · M.A., French Language and Literature, Washington University in St. Louis, Teaching Fellowship · B.A., French Language and Literature, Colby College, Psychology minor
Contact

Available for fractional, contract and project work.

LinkedIn ↗ Boston · US Eastern · remote-first
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