A clear scope
What AIO means here
AI Optimisation, or AIO, is ambiguous marketing shorthand. Here it means technical and operational readiness for AI-assisted discovery. AIO also commonly means Google AI Overviews; this service name does not imply that the two meanings are identical.
AIO owns technical readiness and access controls. LLMO owns factual brand consistency; technical SEO handles the wider crawl and indexing programme. We reuse shared checks and scope each fix once, without inventing a second technical stack.
The buyer problem
Where the work makes a difference
Important specifications may disappear behind a failed request, a hosting rule may block discovery, or crawler permissions may have been changed without distinguishing search from training. These are concrete access and consistency issues that need a reproducible diagnosis.
AI Optimization makes the technical and operational side of AI-assisted discovery easier to understand. Review how important pages can be accessed, what information is available to supported search systems, and whether product facts stay consistent across your website and data sources. Turn the findings into an implementation backlog your team can act on.
Google AI Overviews & AI Mode
For Google AI Overviews and AI Mode, we review current Search eligibility, content access and documented inclusion controls. There is no special AI schema to install, and Google says it does not use llms.txt for Search visibility or ranking. Native generative-AI impressions can be reviewed where the property has enough data.
The specific work
What we investigate and improve
- Review representative category, product, policy and content pages for discovery and content availability.
- Audit robots, indexability, snippet settings, relevant AI-feature inclusion controls and CDN/firewall behavior.
- Distinguish search-crawler settings from model-training permissions and user-triggered fetches.
- Map public product information across HTML, structured data and merchant feeds.
- Define content ownership, freshness checks and release QA rather than adding unsupported AI files.
BrownBag’s approach
From evidence to useful improvements
Inspect access and page content
Review representative categories, products and policies in their initial and rendered states. Check relevant index, snippet, AI-feature and infrastructure controls against current platform documentation, recording defects and the limits of each inspection.
Agree controls and implement fixes
Distinguish search crawling, user-triggered retrieval and model-training settings where the provider supports separate controls. Prepare owner-approved changes with affected templates, expected behaviour and acceptance criteria. Reconcile visible product facts with structured data and eligible feeds.
Validate readiness after release
Retest content availability and intended access on the agreed sample. Record corrected defects, source freshness and remaining dependencies. Review available visibility reports over time without treating a readiness checklist as a ranking factor.
Your handover
What you receive
- Platform-by-platform readiness matrix with documented limitations.
- Crawler and content-control review for owner approval.
- Reproducible defect tickets with affected templates and acceptance criteria.
- Catalog consistency tests and a release checklist.
- Before-and-after implementation validation.
Evidence, with context
How we measure progress
- Confirmed readiness defects resolved
- Important-template content availability
- Catalog consistency and stale-data rates
- Relevant native AI visibility trends after implementation
- No fabricated readiness score presented as an engine ranking factor
Scope your next step
Bring us the problem you want to solve.
These details help us agree the deliverables, responsibilities and review points:
- Store URL, rendering architecture and infrastructure contact
- Your intended search-discovery and training-access preferences
- Representative product pages, feeds and existing crawler rules
Start with a focused scope. We confirm access, implementation ownership and any platform eligibility before the work begins.
Your questions, answered
Does AIO mean AI Overviews?
Sometimes. BrownBag uses it here for AI Optimization. Google AI Overviews is a specific Search feature covered within the service, alongside other relevant discovery systems.
Do we need llms.txt?
Google says it does not use llms.txt for Search visibility or ranking. Maintain such a file only for a specific system that documents a need, not as a universal SEO upgrade.
Do AI features require special schema?
Google documents no special AI schema. Existing eligible product markup can still support standard search and shopping features.
Will you unblock every AI bot?
No. We review the purpose of each control and present changes for the site owner's approval, taking business, privacy and infrastructure requirements into account.
What if we already have strong technical SEO?
The review may confirm that much of the foundation is already sound. The deliverable should identify actual gaps rather than invent a second technical stack.