AI content automation that runs in production, with checks that catch it when it is wrong
AI automation here means systems that do a defined job unattended and report when they fail: content pipelines with verification steps, monitoring for AI citations and competitor changes, data collection engines, and agents connected to business workflows. They run on your infrastructure or ours, with cost and quality controls built in.
Sounds familiar?
- Your team spends hours on content production that follows the same steps every time
- You need to track competitor prices or catalogue changes across many sites
- You want AI citations and search visibility monitored automatically
- You tried an AI tool and the output needed so much checking that it saved no time
What we fix and deliver
- Multi-stage content pipelines where each step does one job: research, draft, restructure, metadata, structured data, deduplication against existing content, audit, publish
- Verification inside the chain: hallucination and puffery detection with automatic retries
- Monitoring for citations, crawler behaviour and entity drift, reported as trends
- Data engines for price tracking and catalogue monitoring across competitor sites
- Conversational and voice agents with defined handoff to people
- Operator tools such as brief generators that your team runs without a developer
How it runs
Initial build at a fixed price, then optional monthly operation and maintenance. Model and API costs are passed through at cost. How pricing works.
- Job definitionWhat the system does, what it must never do, and how failure is detected.
- PrototypeOne working path end to end, on real inputs.
- HardeningTests, retries, cost limits and logging.
- OperateRun on your infrastructure or ours, with monitoring and a handover.
Related work
Own and client infrastructure
The production systems behind the work
Running our own infrastructure is the only honest way to advise on other people's. These systems run unattended, and several power client deliverables.
Content archive, 138 articles
A 138-article archive restructured for AI answers, measured before and after
A blog with half a million lifetime views was well written but invisible to retrieval: few question-led sections, Word paste debris and stale claims. Every article was restructured and scored on countable signals.
Ecommerce group, three domains
A three-domain garden retail group, up 120% year on year after seven months
Three ecommerce domains competing with each other, a duplication defect across 466 products and broken ecommerce tracking. Seven months later, Search Console showed 120% year-on-year growth across the group.
Start with what is actually wrong
Not sure what is wrong yet?
Run a free check on Website AI Score to see what blocks search engines and AI assistants on your page. Send the report with your brief and the proposal starts from real findings.
Frequently asked
Which models do you use?
The model is chosen per step on cost and accuracy. Pipelines usually combine a cheaper model for structured steps with a stronger one for writing and checking.
Where do the systems run?
Most run on Cloudflare Workers with KV and D1 storage. They can also be deployed to your own cloud account.
How do you stop the system from inventing facts?
By limiting what it may state to verified inputs, checking outputs against those inputs, and stopping when required data is missing instead of filling the gap.
Further reading
Send the problem. Get a scoped proposal.
Within 24 hours you get a fixed scope, a delivery window and a price, or a short note saying we are not the right agency for it.