Teddy Alston
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AI systems builder Operator Engineer

Forward-Deployed AI Engineer

I turn messy business operations into bounded, auditable AI systems—from prototype through production.

7+
months in production
5,800+
logged agent events
50+
days without a sync gap
500+
regulated-service clients

Flagship system · Production

Trident Protocol

Four agents across two hosts with shared memory, scoped tools, independent monitoring, and earned autonomy.

Read the production case study
Trident Protocol architecture showing the operator, Telegram gateway, four agents, shared memory, scoped tools, and independent Fenrir monitoring.
Four agents · two hosts · shared memory · independent watchdog

Governed workflow

Argos Vendor Command

Multi-agent vendor-risk workflow with a seven-state machine, dual-specialist review, weighted policy, and human override.

  • Explicit transition gates
  • Nine-domain risk policy
  • Versioned evidence record
Explore case study

Shipped product

TraceReady

Browser-side traceability cleanup that turns messy farm files into deterministic buyer-ready evidence packs.

  • CSV, KML, and GeoJSON
  • Raw data stays browser-side
  • Structured audit-ready exports
View product Repository

How I deploy

From ambiguity to an operated system.

Forward deployment is part discovery, part engineering, and part operating judgment. The work is not finished when the demo runs.

  1. 01

    Discover

    Map the real workflow, decision rights, constraints, and failure cost.

  2. 02

    Prototype

    Build the smallest useful path and make assumptions observable.

  3. 03

    Deploy

    Ship with scoped authority, human gates, recovery paths, and documentation.

  4. 04

    Operate

    Measure behavior, root-cause incidents, and increase autonomy only on evidence.

Operator context

Engineering shaped by real consequences.

I have spent 12+ years building and operating businesses where errors affect customers, compliance, cash flow, and trust.

That background changes how I build AI systems: permissions are explicit, regulated data stays outside the agent plane, important decisions leave evidence, and every autonomous loop has a stop condition.

  • Python
  • TypeScript
  • Node.js
  • REST APIs
  • Docker
  • Tailscale
  • LLM routing
  • MCP tooling

Open to the right deployment problem

Need an engineer who can operate in the mess?

I am targeting forward-deployed engineering, AI deployment, agent-infrastructure, and enterprise tooling roles.

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