Operator-Led Businesses

AI Systems, Forward-Deployed

I work with founders, operators, and owner-led businesses to map the real workflow, find the bottlenecks, and deploy AI systems that survive contact with operations: messy data, brittle tools, human approvals, edge cases included.

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Four ways to work together ↓

Is this the problem?

diagnostic
A tangled knot of black and red thread unspooling into a single straight red line
01

You know AI can help, but your operations are a tangled mess. Which workflow do you untangle first?

Scattered torn-paper icons for a spreadsheet, chat, folder, and note, loosely stitched together by a red thread
02

Your operation runs on spreadsheets, SaaS tools, inboxes, Slack, and tribal knowledge. None of it is AI-ready.

An arrow shattering into fragments at the moment it crosses a ground line, marked by a red impact point
03

You've tried the tools and watched the demos, but they shatter on impact with your daily operations.

A torii gate with a single red dot resting at the threshold
04

You cannot let AI near customers, money, or production without gated controls and human approval.

The Deployment Loop

framework
The Deployment Loop Six phases on a timeline from Week 0 to Day 14+: Discovery, Scoping, Embedded Sprint, Evals, Handoff, Expansion. Each phase produces a named artifact. Sprint, Evals, and Handoff happen embedded on site, and Expansion's Next-Sprint Roadmap starts the next Discovery. EMBEDDED · ON SITE the roadmap starts the next Discovery Discovery WEEK 0 WORKFLOW BOTTLENECK MAP Scoping WEEK 0.5 IMPLEMENTATION CONTRACT Embedded Sprint WEEKS 1-2 PROTOTYPE + GOLDEN DATASET Evals DAYS 8-10 EVAL SCORECARD Handoff DAYS 11-12 RUNBOOK + DASHBOARD Expansion DAY 14+ NEXT-SPRINT ROADMAP
  1. Discovery Week 0
    Workflow Bottleneck Map
  2. Scoping Week 0.5
    Implementation Contract
  3. Embedded Sprint Weeks 1-2 · on site
    Prototype + Golden Dataset
  4. Evals Days 8-10 · on site
    Eval Scorecard
  5. Handoff Days 11-12 · on site
    Runbook + Dashboard
  6. Expansion Day 14+
    Next-Sprint Roadmap

↺ the roadmap starts the next Discovery

Every deployment ships with State-machine agentsPermission-aware retrievalGolden-dataset evalsHuman approval gatesFull tracingNo new silos

Why Ben

proof

Operating reality

Runs a five-property short-term rental operation end to end on software he built.

Hardware scale

Staff TPM at Meta, ex-Apple: a decade shipping consumer hardware through factories and launch.

Agentic infrastructure

Builds and runs agent systems daily, from commerce infrastructure to a home inference fleet.

Proof under pressure

Three first-place hackathon wins in 2026, judged on shipped systems, not slides.

WAYS TO WORK TOGETHER

Forward Deployment Options

Start with a call, diagnose the system remotely, bring Ben onsite to map the real workflow, or move into a deployment sprint once the problem is clear.

The Operator Call

$399

30 minutes

  • Private call with Ben
  • One decision, workflow, or system bottleneck
  • Best for founders and operators who need a fast tactical read
  • Leave with the clearest next move
Book the Call

Payment due before the call

Remote Systems Deep Dive

$2,500

90 minute working session

  • Pre-read of docs, workflow, repo, architecture, or data schema
  • Diagnosis of failure and leverage points
  • Recommended AI, agent, or automation build path
  • Written follow-up with next steps
Request a Deep Dive

Fit check before booking

Enterprise Onsite Assessment

Starts at $7,500

1-2 onsite days

  • Stakeholder interviews and workflow shadowing
  • Systems and data inventory
  • Revenue, cost, or throughput value map
  • Pilot recommendation and SOW outline
Request Onsite Assessment

Travel and multi-stakeholder workshops scoped separately

Forward Deployment Sprint

Starts at $15,000

1+ week embedded deployment

  • Workflow mapping, agent architecture, and implementation guidance
  • Async review plus working sessions
  • Human approval gates, data checks, and operating cadence
  • Onsite, remote, or hybrid depending on the workflow
Apply for a Sprint

Enterprise pilots and production rollout scoped after assessment

Not sure which option fits? Start with the Operator Call or . Enterprise onsite diagnostics and production pilots are scoped around the workflow, data access, stakeholders, and the success metric.

Deployments start with ground truth.

FAQ

fit and scope
Is this consulting or implementation?

Both, selectively. The work starts with diagnosis. The goal is a build path, implementation guidance, or the first working system your team can run.

Do you write code?

Yes. Ben writes production software and prototypes, but the engagement is scoped around leverage. Sometimes the highest-value output is architecture, workflow mapping, prompts, schemas, or a reviewed implementation plan.

What kinds of teams are a fit?

Founders and operators with a real workflow, messy data, and urgency. The best fit is a team that already feels the operational pain and wants a working system, not abstract AI advice.

What is not a fit?

Generic AI strategy, pitch-deck polish, vendor shopping, or speculative agent ideas without a real operating surface. This is paid, selective, and practical.

Bring Ben In

Start with the operating system, not the AI demo.

If the system matters and the current path feels fuzzy, start with the Operator Call. If the workflow is already complex, request a Deep Dive.

Book the Operator Call Request a Deep Dive