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Make sophisticated work your starting point.

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Your AI should bring the right tools and know‑how to the job.

01 / Start hereTell it what you want
Company context arranged above a connected evidence graph
The AI starts with the requested job, uses the team's prior work and relevant information, brings the right tools, and leaves the result and process open to review.
  1. 02 / What came beforeStart from how your team did it before
  2. 03 / What matters nowFind what matters and where it lives
  3. 04 / The workBring the right tools and do the work
  4. 05 / Your reviewReview the result and how it got there

Here's what that looks like on a real job.

Human signal

What the team brought back

“Everyone was talking about controlling their AI spend and figuring out how the org could all get on the same page.”

Context plan

Build the job around the signal

  • OrientLoad company instructions and the content specialist.
  • SelectProduct direction, positioning, customer language, and prior work.
  • PlanResearch first. Stop before the argument is set.

Only selected context enters this run.

Calls + feedback

Listen for the real tension

  • Duplicated work across the team
  • Tool sprawl at the handoff
  • Who owns the decision
Owned context

Position it against strategy and competition

  • Company strategy + AI operating model
  • Product roadmap + vision
  • Positioning + competitive landscape
Research tools

Check demand and competitor coverage

  • Source the cost-control conversation
  • Compare operating-model coverage
  • Check search demand if useful
Synthesis

Find the argument inside the signal

  • ConnectThe signal to customer and company evidence.
  • DiscardCompanies just need fewer AI tools.
  • ProposeIndividual speed without a shared operating model creates cost and accountability gaps.
Compounding intelligence

Save the research

Claims · sources · dates · status

Owned repo or approved data store

Human review

Review the narrative + outline

Proposed narrative

AI made the individual faster. The hard part was making judgment survive the handoff.

  • 01Where AI spend gets duplicated
  • 02Why individual speed creates coordination debt
  • 03The operating model that puts the team on one page

Open with the conference conversation.Lead with the operating problem. Use the conference as evidence.

Specialist execution

Turn the decision into work

  • 01Produce: argument, draft, source ledger
  • 02Challenge: skeptical buyer + cross-model review
  • 03Polish: edit, compress, anti-slop, voice
  • +Optional SEO + keyword pass when the route calls for it
Final artifact

Speed vs. accountability

AI made the individual faster. The hard part was making judgment survive the handoff.

Draft comparison

AI compares its draft to the human finish

AI draft · June 1The AI ROI panic went mainstream this month.
Human edit · June 1Coordination is bottlenecked between the people responsible for the work.
Saved lesson

Lead with your own causal model. Use the news as evidence.

Specialist memory27 saved rules
Editorial training+1 before/after example
Content principles34 active · 1 proposed
Reusable artifactFinal outline + edit
  1. 04Every word must earn its place.
  2. 05Trust the reader. Don't over-explain.
  3. 12Cause and effect over abstract framing.
See the content production skill

Editorial Training Data · Example 32b

Signal

A human spots the question worth pursuing.

“Everyone was talking about controlling their AI spend and figuring out how the org could all get on the same page.”

Research + context

The AI selects the context and tools this job needs.

It loads company instructions and the content specialist; pulls strategy, roadmap, positioning, customer calls, competitor coverage, search demand, and sources; then saves the research without pulling the rest of the repository into the run.

Human review

Review the narrative and outline.

Research turns the signal into a proposed argument. A person changes the framing, confirms the outline, and decides what the draft should say before production begins.

Draft + learn

Finish the work. Keep the useful correction.

Specialists produce, challenge, and polish. The system compares the AI draft with the final human version, then proposes the lesson for specialist memory, editorial training, content principles, and the next run.

You have an entire system behind your AI.

One job sits above four connected layers in a company-owned system.

  1. Tools and access expose only the ports the job needs: retrieve, research, enrich, create, update, and publish.
  2. The business ontology defines what each thing is, how it relates, and what can happen across company, people, roles, ICP, product, market, work, campaigns, and decisions.
  3. The owned knowledge graph connects company, people, product, market, and prior work.
  4. Business systems and evidence contain the records, conversations, signals, and work the company already has.

A tuned skill, research agents, tools, and a human decision remain connected to those layers. Approved lessons return to the ontology and research can be saved into the knowledge graph.

Same system. Unlike work:Account briefCampaignSector reportSaved research

The work gets done. The foundation gets stronger.

Completed work improves what comes next in three ways.

  1. Consciously curated: your team makes the call, including the decision, the evidence behind it, and where it applies.
  2. Automated and low effort: the system carries it forward, watches for the trigger, and brings the decision back when related work begins.
  3. Every relevant run: account plans, campaigns, product briefs, and reviews stop working from conflicting assumptions.

In the worked example, a new capability says “Audit logs are live.” The system proposes where that changes the business rather than applying changes silently.

  • Market: enterprise healthcare was blocked on auditability. Buyer requirements and product strategy support updates to ICP, positioning, and the enterprise page.
  • Pipeline: accounts that already asked are identified from CRM losses and active pipeline. Named accounts can be reopened while a broad campaign remains held.
  • Customers: renewal and expansion candidates are identified from support requests and renewal notes. Risk notes can be updated and upsell candidates flagged.

The approved rule is written back for later work: enterprise healthcare is in scope and auditability is covered, linked to affected accounts and customers.

Own your motion.

Build on a foundation that is portable, company-owned, and not trapped inside one model.

Stays with your company

ContextMethodsMemoryWork
Compatible AICapability connects here
People + agentsStart here

You do not have to architect this alone.

We'll look at what's working, where AI can help, and what I'd prioritize next.

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