Selected work ↗ Start a conversation The long story
$11M+
Closed in SaaS & services across SMB and mid-market
3,000+
Sales meetings run — discovery, demo, close, expand
28%
Team-high close rate within 2 months at SimpleTalk.ai
49/50
CCAT at Gauntlet AI (~2% admit program)
Credentials
Stanford
AI Grad Cert
CS221 · CS230
Vanderbilt
BA Philosophy
CS Minor · 3.7 GPA
Gauntlet AI
~2% admit
CCAT 49/50
Trilogy
AI Engineer
Production delivery

Two tracks, one operator.

The shape of the bet

Most AI teams hire an engineer who can build, or a closer who can sell. I'm both. That collapses the handoff between the person who ships and the person who the customer trusts.

The engineer.
production AI · rag · agents · evals
  • Trilogy. Own end-to-end delivery of production AI features — requirements through rollout, with latency / reliability / cost as first-class KPIs.
  • Grounded systems. Knowledge-base cleaning, indexing, RAG with citations & fallbacks — measurably improved answer quality and time-to-answer.
  • Eval harnesses. Golden sets, regression thresholds, guardrails. Turns vibes into release criteria.
  • Gauntlet AI. ~2% admit program — shipped full-stack AI apps in accelerated sprints across Slack, Zendesk, and TikTok surfaces.
The closer.
enterprise sales · discovery · expansion
  • Solar Exclusive. Closed $11M+ across SMB and mid-market. #1 in closing %, revenue per booking, and retention — three years running.
  • Scaled a top client to $250K/month — second-largest in company history — through consultative selling, QBRs, and custom dashboards.
  • SimpleTalk.ai. Highest close rate on the team (28%) within two months of onboarding. Led a pricing feedback loop that drove 20%+ MoM revenue growth.
  • 10× upsells. Grew multiple accounts 10× by diagnosing workflow gaps and tightening feedback between customer and delivery.
&

Selected work.

Shipping, 2025–

Small, real things. Code is on GitHub; case studies are anonymized.

Case study · TAIGR
— · Healthcare opsPython · AI

Medical claims reconciliation, automated

End-to-end automation of a claims-reconciliation workflow for a medical practice. Replaced a manual review queue with a deterministic pipeline plus model-in-the-loop checks — cut manual review time by 85%.

PythonAutomationHealthcareEval gates
Case study · TAIGR
— · Construction insuranceRAG · Ingestion

Maintenance-report ingestion pipeline

AI-enabled ingestion + structuring of unstructured maintenance reports for a construction-insurance firm. Drove a step-change in throughput — ~$1M of annual OpEx removed.

RAGOCRPipelinesInsurance
↗
01 · Knowledge pipelinePython

X-port-obs — bookmark-to-Obsidian sync

Pulls bookmarked posts from custom folders through the v2 API and writes them as clean Markdown notes inside an Obsidian vault. Handles OAuth 2.0 refresh, image downloads & metadata sync, thread expansion, unfiled backfills, scheduled exports via cron, RAG query pipeline via QMD.

PythonRAGOAuthObsidianX API
↗
02 · Social platformTypeScript

eventOS — a social OS for live events

Temporary, high-trust social network that organizers deploy for a single event. Geo-fenced quests, AI-verified check-ins, and ephemeral content that vanishes when the event ends. React Native + Expo on Supabase.

React NativeExpoSupabaseGeolocation
↗
03 · Writing toolTypeScript

Wordwise — Daily pages with AI

A journaling app that turns stream-of-consciousness writing into shareable insight. Two AI personas — Anima (intuitive) and Animus (strategic) — react as you write, then mine recurring patterns into tweet-ready threads.

Next.jsSupabaseOpenAIJournaling
↗
04 · Obsidian pluginTypeScript

FlowGenius — AI-generated background images based on what you're writing

Obsidian plugin that reads the active note, asks GPT for a scene prompt, and generates atmospheric backgrounds via Stable Diffusion XL. Ken Burns animation, per-vault persistence, and opacity tuning so it stays out of the way.

ObsidianOpenAIReplicateSDXL

Where I'm headed.

Role target

Remote-first AI roles at companies shipping frontier product. Variants of the same story — pick the one that's yours.

AI Solutions Engineer

Technical discovery, demos, POC design. Translate buyer problems into shippable architecture.

Forward Deployed Engineer

Build + deploy production AI inside customer environments. Carry the POC through to the rollout call.

Solutions Architect

Target-state architecture, implementation planning, trusted advisor from evaluation to production.

AI Engineer

Own production AI features end-to-end — RAG, agents, evals, model routing. Latency, reliability, cost as first-class KPIs.

Sales Engineer

Technical co-pilot to AEs. Engineer-grade demos, POCs that survive procurement, deal-saving deep dives with buyer technical teams.

GTM Engineer

Revenue systems, agentic GTM workflows, APIs and pipelines that convert signal into pipeline.

Things I actually use.

Stack

Not an exhaustive keyword farm. These are the ones I'd bring to a whiteboard.

AI & LLM Systems
  • Model routing
  • Agent harnesses
  • Context Engineering
  • Evals
  • MCP & tool calling
  • RAG
  • Guardrails
  • Fine-tuning
  • Browser automation
Models & Tools
  • Codex
  • Claude Code
  • Cursor
  • OpenCode
  • LM Studio
  • OpenClaw/Hermes Agent
  • Gemini
Build & Ship
  • Python
  • TypeScript
  • Next.js
  • React
  • Node
  • Supabase
  • Redis
  • Docker
  • SQL
  • REST / Webhooks
  • AWS
  • GCP
  • Firebase
Pre-Sales & GTM
  • Discovery
  • POC design
  • Demos
  • Workshops
  • QBRs
  • MEDDICC
  • Stakeholder alignment
  • Salesforce
  • HubSpot
  • Clay
Credentials
  • Stanford AI Grad Cert
  • Vanderbilt BA — Philosophy
  • CS Minor
  • Gauntlet AI · 2% admit
  • CCAT 49/50
Operating Principles
  • ship > demo
  • evals > vibes
  • latency is a feature
  • customers > abstractions
  • receipts > adjectives
Get in touch

Let's talk

Best fits right now: Forward Deployed Engineer, AI Solutions Engineer, Solutions Architect, GTM Engineer. Remote-first. If your team has messy customer-facing AI work and you need someone who can build it and explain it to the CEO — hi.

LinkedIn DM @timothytgregg → Book a 20-min call Calendly → GitHub @tg-gt →
Or drop a line
About · the long version

I didn't take the shortest path here — and that's the point.

I started out writing code at Earth Day Network (2017) and UpChannel (2016) — shipping React Native for phone manufacturers, launching a Google Home experience that drove donations, contributing to a site redesign that served 6M+ pageviews.

Then I did something people in software don't often do on purpose: I went and spent four years in the room with customers. $11M+ closed. 3,000+ discovery calls. I scaled a single account from zero to $250K/month. I learned — painfully, usefully — how enterprise buyers actually decide, and how demos die.

When the AI wave broke, I didn't switch careers. I combined them. I got into Gauntlet AI (~2% admit), shipped full-stack AI products in accelerated sprints, and joined Trilogy to own production AI delivery — RAG, agents, evals, the operational boring parts that make models useful.

Today I'm the person on your team who can architect the RAG pipeline and call the CTO back on Friday to save the renewal.

Philosophy degree from Vanderbilt. Currently working through a Stanford AI graduate certificate. I read too many changelogs and not enough novels.