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Marketing and growth
Content that is measured to the lead, not to the word count.
A content engine picks topics from search and AI-answer demand, drafts under a written standard, fails its own drafts on unverified claims, publishes at a paced cadence, and measures every article to the qualified leads it produced. Volume is capped. The goal is
Who it is for
Built for the team that does this by hand.
B2B and SaaS companies whose blog is a cost centre nobody can tie to
Firms that need to be cited by ChatGPT, Claude and Google AI answers, not
Marketing teams of one who cannot write, fact-check and measure forty articles a year
What it does
Content engines, by the job.
Plans from demand
Search Console, analytics, AI-citation monitoring and the CRM feed one scoreboard. The engine writes what buyers are asking, and refreshes what is decaying before it writes
Drafts under a standard
Positioning chosen at plan time, a direct answer in the first hundred words, one named source per claim, and a house register enforced by gates that fail the draft rather than
Fails on fabrication
A claim with no source, a number with no window, a machine-sounding sentence: any of these stops the piece. A live fabrication found later preempts the queue and is
Measures to the lead
Every article carries an inline form with first-touch attribution into the CRM, so the engine knows which pages produced contacts and which produced traffic that
How it is built
The build, in order.
Every system here is scoped as one bounded workflow with a written success metric and a kill threshold agreed before the build. The steps below are the ones specific to
Write the standard
Positioning method, fact floor, register, sources allowed, claims forbidden. The engine inherits it as gates, and a gate failure is fixed in the copy, never in
Connect the measurement
Search Console, analytics with paid traffic separated from organic, AI-citation monitoring, and the CRM, so the engine can only take credit for what
Run at a capped cadence
At most one merged article a day, refresh before new, and a monthly model bake-off so the writer seat is the best available rather than the one chosen
Let the overseer steer
An in-engine overseer holds the written goal, grades its own predictions, and moves the levers the loops read. Lead pressure never becomes a
In production at Strataigize
We run this ourselves.
The Insights section of this site is run by this engine. It ships at most one article a day, refreshes before it writes new, separates organic traffic from paid so it takes credit only for what it earned, and reports to an overseer whose goal is written down: high-quality, long-term clients, not
Governance
How it checks its own work.
- A person can stop the engine with one switch, and the engine cannot turn itself back on
- Every claim traces to a named source, and client evidence is used at most once per article and never in the headline
- Spend on models and data is metered per call against caps you set
The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say
Price
Published, not gated.
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month. Every AI build starts with a $7,500 AI Opportunity Audit over two weeks that is credited toward the build if you proceed, and a pilot is one bounded workflow on your real data over 30 to 90 days with a written success metric and a
The full price list is on the pricing page, and the cost drivers, with the questions that expose a padded quote, are in how much AI agents cost in 2026.
Questions
Content engines, answered.
Does Google penalize AI content?
How is this different from a writing tool or a content agency?
Can it write for AI search as well as Google?
Related
Where this connects.
Generative engine optimization · Search engine optimization · B2B
The other systems in the catalogue: lead qualification agents, outbound sales agents, customer support agents, document processing agents, internal knowledge assistants, ad optimization systems, client reporting automation, computer vision systems, mcp servers and agent-callable surfaces, always-on operations agents.
Teams we've
Clients include 3M, Jiffy Lube, Goodwill, Western University, Hino Motors, Lundin Mining, Extendicare, Calian, Wabtec, ARGO, and
Kleo
Goodwill
GameGo
Hino Motors
Sad Panda Studios
StoryNest
Blush Blush
Stoic Forge
Beyond Score
MyPetHealth
TaskHive
Sincere Notes
Voices in my Head
Black Top Cabs
BeNow Trader
Crystalyze
24/7 AI Health
Aero Imagery
RepairFind
Prepr
Guiding Light
Freestyle Dance Studio
First Step Mobility
Noa Coach
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000
Real ranges, published. Ad spend stays in your
Where this fits: see the Strataigize Growth System, the ladder every engagement
Talk to the build team about content engines
Name the workflow you want it to run and we reply within 24 hours with whether it is a fit, the bounded scope we would propose, and the
Start with one workflow.
A free 30-minute consult with the senior team names the workflow this system should run first and what it should cost, whether you build it with us
Canadian and looking at funding? How the BDC LIFT program works. Public sector? How we build for government.
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