A clear scope
What LLMO means here
Large Language Model Optimisation, or LLMO, is marketing shorthand for improving the public facts that systems may encounter about a brand. This service focuses on factual representation and source consistency. It does not fine-tune a third-party model or give BrownBag control over its memory.
LLMO owns factual brand representation and source consistency. AIO owns technical access and crawler controls. Retrieval-grounded and model-only answers are recorded separately; we cannot edit a model’s internal knowledge or guarantee correction of a third-party response.
The buyer problem
Where the work makes a difference
A brand name can be confused with another company, an old policy can contradict the current offer, or several owned pages can describe the same product differently. Those inconsistencies affect customers and make observed AI errors harder to investigate.
A useful AI answer depends on the information available to support it. This LLMO service focuses on the public facts about your brand, product ranges and policies: what is stated, where it is supported and where sources disagree. It combines entity research, editorial corrections and careful monitoring of answer accuracy.
The specific work
What we investigate and improve
- Build a public fact inventory for business identity, product families, identifiers, differentiators and supported claims.
- Find conflicting or stale statements across owned pages and legitimate external profiles.
- Create a source-of-truth content plan with named subject experts and review dates.
- Check selected assistants for factual errors using neutral, predefined prompts and a documented rubric.
- Record external factual discrepancies for your team and prioritise corrections to owned pages; external requests, profile updates and outreach are outside this service.
BrownBag’s approach
From evidence to useful improvements
Establish the source of record
Inventory important brand, product-family and policy facts. Assign a supporting page and business owner to each, then identify contradictory or ambiguous information across your controlled public sources.
Correct and connect owned information
Prepare approved corrections and clearer entity relationships on relevant pages. Resolve outdated descriptions, explain product-family differences and link to the authoritative policy or evidence without inventing credentials or unsupported claims.
Recheck factual representation
Create a repeatable accuracy rubric for the agreed scenarios. Record whether an observation used current web sources, track source changes and stale facts, and hand over a schedule for rechecking business and catalogue updates.
Your handover
What you receive
- Brand/entity fact sheet with evidence URLs and ownership.
- Discrepancy register ranked by commercial and customer risk.
- Brand, policy and product-family content briefs.
- Accuracy evaluation rubric and baseline assessment.
- Ongoing source-update and recheck schedule.
Evidence, with context
How we measure progress
- Verified factual consistency across controlled sources
- Number and severity of observed factual errors
- Correction completion rate and stale-fact age
- Sampled brand disambiguation and attribute accuracy
- Qualified referral quality where measurable
Scope your next step
Bring us the problem you want to solve.
These details help us agree the deliverables, responsibilities and review points:
- Verified business, product-family and policy facts
- A record of outdated pages or observed factual errors
- Owners who can approve corrections and maintain authoritative sources
Start with a focused scope. We confirm access, implementation ownership and any platform eligibility before the work begins.
Your questions, answered
Can you train ChatGPT or Gemini to know our brand?
This service cannot directly train or edit public third-party models. It improves public evidence and tests observable responses.
Does allowing a training crawler guarantee recommendations?
No. Crawler access and recommendation outcomes are different matters. Search access and model-training controls can also be separate.
Is LLMO the same as GEO?
The labels overlap in the market. Here LLMO focuses on factual brand representation, while GEO focuses on question opportunities and evidence-led visibility experiments.
Can you remove an inaccurate third-party article?
We can document inaccuracies and give the evidence to your team. The publisher controls its content. External correction requests, profile changes and outreach are outside this service; our implementation focuses on your owned information.
Should we create a knowledge panel?
We can improve accurate identity information where relevant, but no third-party provider can guarantee a knowledge panel or a particular entity treatment.