AI operations & governance — for teams that ship.

Who this is for

• Founder-led companies between 5 and 200 people that have already deployed AI in fragments and are now feeling the consequences — inconsistent output, brand-tone drift, IP exposure, customer-trust complaints, or a regulator-shaped concern.
• Public-facing operators — creators, executives, family-office principals — whose name and reputation are in every AI-generated output the team ships, and who need the governance to match the exposure.
• Operators in regulated-adjacent categories — legal, health-adjacent, financial-adjacent, EdTech, ancestry, identity, child audiences — where AI mistakes are not aesthetic, they are compliance failures.
• Teams that have a small budget and a fast clock — no twelve-month transformation timelines, no $400k engagements, no "let's do another readiness assessment."

The AI operating stack we design

An AI operating system is more than a chat tool subscription. We design it as five connected layers:

Layer	What lives here	Common failure mode
1 · Workflow	Content research, repurposing, deal outreach, customer support, analytics copilots, customer-onboarding triage	Twenty teams using twenty tools with zero shared discipline
2 · Model + vendor	Which model for which job, which vendor, which fallback, which evaluation	Defaulting to one model because someone bought a seat once
3 · Human-in-the-loop	Where humans approve, where they audit after the fact, where they never see the output at all	"Just review everything" — which means nobody reviews anything
4 · Governance overlay	IP rights, accuracy expectations, privacy boundaries, brand-tone enforcement, disclosure language, citation discipline	Governance memos that nobody reads, or none at all
5 · Audit + record	What was generated, by whom, with what input, with what review — retained for the operating window the business actually needs	No audit trail when something goes wrong

The governance overlay (the thing other consultants skip)

It is no longer enough to "deploy AI." For public-facing operators, the governance overlay is the part that protects the brand, the reputation, and the legal posture. We design it explicitly:

• Workflow governance — which workflows can run unattended, which require a one-eye review, which require sign-off.
• IP and rights — what content the AI is allowed to train on or generate from; what is licensed; what is owned by the brand vs. by the platform.
• Brand-safety controls — voice guidelines, prohibited claims, disclosure boilerplate, brand-tone tests.
• Privacy boundaries — what user data goes into a prompt, what does not, what is logged, what is purged.
• Explainability — when a decision is AI-influenced, how it is documented so a human can defend it later.
• Human-in-the-loop escalation — the explicit rules for when AI hands a matter to a person, and which person.

Patterns we ship

• Content research → repurposing workflow for creator-led businesses with brand-safety governance and a single-pass review gate.
• Deal and outreach automation for founder-led sales with personalization controls, disclosure language, and CRM hand-off rules.
• Customer-support augmentation with explicit fallback escalation and audit log of every AI-assisted response.
• Analytics copilot stack with prompt templates, named queries, and dataset-access gating.
• Document workflows — intake, summarization, redaction, scoring (see the AEGIS engine).
• Compliance-adjacent intake — UPL-style boundary design adapted for the client's category (we have built this for legal access, ancestry, messaging, and consultation commerce in the agentic2x portfolio).

Deliverables

• AI operating-stack diagnostic — five-layer view of where your team is and where the gaps are.
• Workflow design document — for the specific workflows in scope, including model choice, fallback, evaluation, human-in-the-loop, and audit.
• Governance overlay pack — the templates, memos, prompt boilerplate, and disclosure language your team actually uses.
• Vendor selection memo — model + vendor recommendation with unit economics, dependency risk, and exit options.
• Operating cadence install — weekly review, monthly governance check, quarterly audit.
• CW Leaders Studio configuration — the firm's proprietary desktop OS, configured for the team's AI cadence; see Studio.

Pricing & timeline

Format	Timeline	Fee
Get Grilled by the Founder (AI-strategy decision pressure-test)	30 minutes · private	$99
AI operating-stack diagnostic	2 weeks	From $6,500 fixed
Workflow + governance design sprint	4–6 weeks	From $22,000 fixed
AI ops retainer (monthly governance + cadence)	Rolling monthly	From $5,500 / month

Why us

Dustin L. Clemons is the founder and CEO of Agentic Agentic Enterprises, a portfolio of four shipped AI platforms — a live legal-AI access-to-justice platform serving self-represented litigants with four AI counselor modes, integrated payments, and recorded private telephony; a browser-first private temporary-messaging product engineered around hard-coded data-minimization limits; an iPhone-native ancestry-intelligence product with explicit non-claims guardrails; and a culturally specific AI consultation-commerce platform with tiered intake and asynchronous generation. Each platform is a working answer to a different governance problem — and the answers are what we bring to clients.

And — because Clemons Wright is led by a pro se litigant — the governance is not theoretical. It is built with the understanding that everything an AI workflow touches could end up in a deposition.