Open the Playbook
Most "AI consultants" sell decks. I ship the actual infrastructure —
the open-source agent kernels, the digital twin factories, the
operational intelligence loops — and then deploy them into your
organization. The same playbook I use is on GitHub. You can read it,
fork it, and run it yourself — or hire me to install it for your
team.
20+ years of hands-on VP Eng/CTO/Architect experience, applied to one
specific job: replacing the operational layer of your engineering org
with an agentic operating system. Faster cycles, higher rate of
improvement, AI employees handling the cadences that humans burn out
on. With only 3-4 clients per year, you get white-glove attention,
including on-site collaboration for 6+ month engagements (travel
covered by you).
Curious to see me in action? Check out my YouTube channel
youtube.com/@Control-The-Outcome
where I dive into engineering team optimization, share actionable
strategies, and discuss challenges. This will give you a feel
for how I think, what you can expect, and how I apply systems over
goals thinking to deliver, get it straight from the source!
Digital twins of human roles are deployable today, not in
2027. The Digital Twin Factory framework gives you a
repeatable way to clone the operating cadence of a high
performer and run it 24/7.
The only metric that matters in an agentic org. The
Operational Intelligence Lab tracks how fast your system
gets better at getting better — week over week, twin over
twin, loop over loop.
Using ChatGPT is not the same as running an operating system
of AI employees. I close the gap — wiring the kernel, the
twins, and the improvement loops into a single system your
team actually runs on.
When growth exposed delivery bottlenecks and turnover
stalled throughput, I diagnosed constraints, clarified
decision rights, and realigned the team from the ground
up:
The results were slashed cycle times by 40%, reduced turnover to under 10%, $250K revenue per FTE, and a company positioned for scalable growth without inflating headcount.
I'm a member of CTOx.com, a premier network of 220+ vetted
fractional-CTO operators. When an agentic-OS engagement
needs a specific deep-domain perspective — security
architecture for an AI-augmented pipeline, scaling
infrastructure under twin-driven load, regulated-industry
deployment patterns — I tap the network directly.
This is how every Solid Cage engagement gets the depth
of a 220-person bench behind a single point of
accountability: me. You hire one operator. You get a network
of operators feeding the work.
Combined with my 20+ years of hands-on VP Eng / CTO /
Architect work and the open-source agentic OS stack I
maintain (agent-kernel, agent-factory, digital-twin-factory,
operational-intelligence-lab), this is the most
capital-efficient way to install a real agentic operating
system in your engineering org without hiring a 30-person
internal AI platform team.
Duration: 30 days
Price:
$5,000
Deliverables:
Best For: Founders and CTOs who keep hearing "we should do AI" and want a defensible answer about where to start.
Duration: 60 days
Price: $30,000
Deliverables:
Best For: Teams that have done the assessment and want one undeniable production win before going all-in.
Duration: 12 months
Price:
$100,000
Deliverables:
Kickstart with a 1-month pilot at $5,000 — a focused preview
of hands-on agentic OS work. 10 hours of dedicated senior
leadership (process baseline, first twin scoped, kernel
architecture sketch), 1 weekly 1-hour review.
Deliverable
is a tailored agentic OS roadmap with early KPIs (twins
shipped, Rate of Improvement targets, ROI per twin). No
lock-in.
Best For: Engineering organizations committed to running on an agentic operating system, not just sprinkling AI on top of existing process.
Objective: Score readiness across the 6 dimensions, baseline today's operating cadence, and pick the first 1-2 workflows worth deploying as digital twins.
Assess current
engineering processes, including cycle time, turnover, and
tech stack, to establish a baseline (e.g., cycle time = 10
days, turnover = 20%).
Evaluate client
needs, such as PMF status, revenue goals, and retention
challenges, by interviewing client success, sales, and
marketing teams.
Identify high-impact
AI adoption opportunities, such as replacing manual QA with
AI agents or automated code reviews, prioritize an "AI first
before human hire" approach to enhance efficiency without
expanding headcount.
Define metrics,
core values, and OKRs, ensuring a single source of truth for
tracking progress.
This phase establishes baselines for all KPIs (e.g., Cycle
Time, Turnover Rate), enabling measurable progress in Phase
2.
Objective: Ship the first
production digital twin, then scale into a full agentic operating
system —
twin factory, agent kernel, governance, and
Operational Intelligence loops measuring Rate of Improvement on every
twin.
Action: Conduct a
thorough assessment of current engineering processes,
including cycle time, turnover rates, and tech stack, to
establish baselines. Identify high-impact AI adoption
opportunities, such as replacing manual QA with AI agents to
improve efficiency without expanding headcount.
Impact:
Uncovers bottlenecks and unlocks targeted AI integrations
that automate repetitive tasks, freeing engineers for
high-value innovation.
KPI: Cuts Cycle
Time by 20-25% (10 to 7-8 days) by Month 3; boosts Rate of
Innovation with faster ideation cycles.
Action: Redesign your
system to align with PMF goals—mapping outcomes like 99.9%
uptime and 2x speed to budget and skills—while embedding AI
across ingestion, compute, and CICD with self-healing
features like auto-scaling.
Impact: Builds a
lean, scalable architecture that minimizes downtime and lets
your team focus on feature development over maintenance.
KPI: Reduces
Cycle Time by an additional 10% (7-8 to 6-7 days) by Month
4; increases Revenue per FTE as output rises.
Action: Streamline
workflows with pre-configured dev environments (e.g.,
Docker), a 20% tech debt refactor, 1-day CICD cycles via
GitHub Actions, and AI-driven docs/tests (50%+ automated),
while defining "Done" to ensure quality.
Impact: Enables
engineers to ship faster with less stress, shifting their
focus from maintenance to innovation.
KPI:
Achieves a 30-50% Cycle Time reduction (10 to 5-7 days) by
Month 6; increases Rate of Innovation to 3 features/quarter;
lowers Turnover Rate to 15% (from 20%).
Action: Leverage the
Idea Evaluator (People/Process/Tech, 80/20, Revenue/Risk) to
score and test product ideas with AI insights (e.g., Grok
ranks concepts), conducting 5-10 sales calls with client
success to build a "hell yes" case study through 30-60-90
sprints.
Impact:
Identifies true customer demand, eliminating guesswork and
delivering products that resonate.
KPI: Boosts
Client Retention Rate to 75-80% (from 70%) by Month 9;
increases ARR by 10-20% ($2M to $2.2M-$2.4M); sustains Rate
of Innovation at 3-4 features per quarter.
Action: Implement EOS
(metrics, Level 10 meetings, OKRs) and Topgrading to
hire/coach 1-2 A-players, while sharing my YouTube training
content (e.g., "Systems Over Goals") and gamifying AI use
with a "Prompt Leaderboard" to enhance engagement.
Impact: Aligns
your team to a unified vision, boosting talent retention and
fostering a strong sense of ownership.
KPI: Reduces
Turnover Rate below 10% by Month 12; raises Revenue per FTE
to $200k-$250k; achieves a Rate of Innovation of 4-6
features per quarter.
Action: Implement
PMF-driven CICD with weekly releases tied to client
feedback, prioritizing the top 20% of ideas (Idea Evaluator)
for revenue impact, and integrating engineering with client
success for real-time iteration.
Impact: Delivers
products customers truly want, driving business growth and
enhancing loyalty.
KPI: Increases
ARR by 20-40% ($2M to $2.4M-$2.8M) by Month 12; lifts Client
Retention Rate to 80-85%; achieves Revenue per FTE of
$250k+.
Daily staff check, shared fleet memory, and a yes before anything ships.
Architecture reviews, hiring plans, and a workflow built around pull requests.
I run engineering in small pods. Every engineer has their own bots, and the bots do the repetitive work so people can do the work that needs judgment. The bots talk in a shared channel. Work comes in from the bug tracker, the feature tracker and production errors, and the bots turn it into draft pull requests across the whole product. Every PR is written for the people reviewing it, in plain language with screenshots, charts and diagrams. Standups are the open pull requests. Nothing merges, deploys, sends, or spends until a person says yes.
What it changed: junior engineers working at senior level in weeks. Visible velocity and throughput gains with smaller teams. Less time coordinating, more time building and delivering. An order of magnitude impact on engineering resources.
How to start:
