BeRelevant builds the system around it: verified product data, buyer questions, brand rules, and human review, connected so your team publishes useful content at catalog speed without letting a model invent the facts.
The model writes what the process gives it
The prompt has no view of the buyer, the product’s evidence, the brand’s rules, or the claims an editor will approve. Your team then spends its days correcting output that should never have reached review.
Scaling that process creates more copy and more cleanup, never more useful answers.
A working content system defines five things for every page
That is the system we build.
Start with the questions buyers need the catalog to answer
- A travel-bag buyer weighs airline size limits, laptop fit, weight, and the tradeoff between capacity and bulk
- Someone choosing a marketplace provider needs qualifications, service area, lead time, and proof of similar work
- A property search may hinge on access, transport, lease terms, pets, and nearby services
We research those decisions and map them to verified source data. Then we define which answers belong on a product page, which on a category or comparison page, and which need a deeper guide or FAQ.
The catalog becomes a connected answer system rather than a set of isolated descriptions.
Audit the content pipeline before adding AI to it
Find the work a model can handle and the decisions that still need people
This exposes the work a model can handle and the decisions that still need people.
Build generation around your products, buyers, and publishing system
Content models that define useful output
Each page type gets a clear job and structure. A product description, comparison, and buying guide should never come from the same template with a different word count.
Source data behind every claim
Approved catalog fields, product documents, expert input, and brand rules feed the generation process. Missing evidence becomes a visible data gap instead of an invitation to guess.
Prompts built for the task
Buyer context, source constraints, required fields, format rules, examples, and rejection conditions, producing structured output that fits the next step in the workflow.
Checks before an editor sees the draft
Automated tests flag missing facts, unsupported claims, duplicate language, and banned phrases. Editors review exceptions, not everything.
Integration with the tools your team already uses
The process fits into the PIM, commerce platform, CMS, or editorial system where the work happens. Nobody copies product data into a separate chat for each page.
Training and maintenance
Your team gets the content models, prompts, controls, and training to run the system without us. We also teach where generative AI helps and where it defaults to slop.
Use generative AI where it removes work, not judgment
Generation suits repeated transformations: structured facts into a first draft, approved content into a required field, consistent variants at scale.
It should never invent how a product performs in the rain or assign a seller a qualification they never supplied.
The model handles repeatable production. Source owners and editors keep control over facts, risk, and brand judgment.
See the difference on a catalog release
A seasonal launch without the rewrite marathon
A retailer launches 4,000 seasonal products. The system checks each product for the data useful copy needs, generates page components from approved fields, and routes weak records back for completion. Editors handle the exceptions instead of rewriting every page.
Uneven seller data, consistent listings
Thousands of sellers submit uneven descriptions. The system keeps seller-specific facts, normalizes the fields buyers compare, and drafts clear listings without unsupported quality claims.
Regional expansion without thin pages
A directory expands into new regions. The system combines verified location, service, and eligibility data with local buyer questions, without publishing thousands of pages that differ only by city name.
Content should improve as the catalog grows
New products expose missing fields. Buyer questions reveal weak guidance. Editors discover which drafts need too much work.
We feed those signals back into the source requirements, content models, prompts, and checks.
Each release makes the production system sharper for the next one.
Build a content pipeline your catalog can trust
Show us how a product becomes a page today. We will identify where buyer research, source data, generation, and human review need to enter the process, and build the system your team runs from there.