Give people a reason to recommend your product.

Start with the decision someone is trying to make.

“A communication platform for every team” leaves the reader to work out the problem, the fit, and the effort. A more useful explanation names a situation: a founder has a release note, a company page, a personal profile, and very little time to write two thoughtful updates.

Describe the input, the change you make to it, the result, and the judgment still needed. Then make the boundaries visible. A recommendation can only be useful when the product fits the person’s constraints. That is the standard we are applying to Tellshift’s workflow pages and examples.

Being retrieved, cited, and recommended are different outcomes.

A page can be found without being used. It can support an answer without the product being recommended. A recommendation can mention a product without sending a measurable click. Treat these as separate steps in discovery, followed by the question that matters commercially: did the visitor get something useful?

Google describes AI search as building on its Search systems, including related queries and retrieval. Its guidance prioritizes original, useful material and says Google Search ignores llms.txt. That makes clear product evidence and good pages a stronger starting investment than a file presented as a ranking shortcut.

Google Search Central · Generative AI guidance ↗

Railway connects awareness to a practical first action.

In her first-person Railway account, Sarah Krasnik Bedell describes community conversations as an awareness source, with documentation and tools helping people implement. This is a useful strategy to study, not a causal model that every product can copy.

Her separate 2025 account reports 30% more Google Search traffic to integration pages after several structural and content changes. It does not isolate an AI recommendation lift. The transferable idea is specific pages for specific jobs, connected to a usable starting point.

For a communication product, that starting point could be an approved project note and a worked example of two different voices. The reader should finish with a useful draft or a better decision, even before buying anything.

Sarah Krasnik Bedell · Railway discovery strategy ↗

Sarah Krasnik Bedell · Integration-page search changes ↗

Make your evidence easy to inspect.

Show what the input actually looks like. Include the source beside the finished artifact. Explain why the wording changes between a company and a person. Label fictional examples, proposed features, and measured customer outcomes differently. If a connection is not available, say how to do the task manually.

A useful guide should have prerequisites, concrete steps, an example, a reusable template, limits, and a next action. A comparison should help someone make a choice, including when your product is a poor fit. Do not create a page for every wording of the same question; maintain a smaller library that answers distinct decisions well.

Outside your site, earn descriptions people can stand behind: a participant explaining how they used the product, a partner publishing an agreed workflow, or a customer documenting an outcome with permission. Disclosed, specific accounts are more useful to a buyer than anonymous praise you cannot verify.

Remove friction without hiding an important decision.

Let someone inspect an example and try a small task before asking them to connect a workspace, invite teammates, or book a call. Ask for the context needed by the next decision. Save the larger configuration for when it becomes useful.

That does not make every product ready for public signup. Tellshift’s application currently requires an invitation. Its public worksheet is available without an account and uses editable templates, not live AI generation. Private deployment, including on-premise requirements, starts with an assessment. These distinctions let someone choose the right path.

Measure visibility and usefulness separately.

Check whether public pages can be crawled, then measure discovery and what happens after a visit. OpenAI distinguishes its search crawler from its training crawler; permission for one is not a promise of inclusion or a recommendation.

Use available platform reports for their stated purpose. Google’s current Generative AI report measures impressions. Bing’s AI Performance reports citations and grounding queries, with explicit limits on interpreting citations as rank or importance.

For your own learning, keep a small, stable set of customer questions. Record the assistant, date, question, context, product mentions, cited sources, and factual errors. Repeat the observations; do not call one screenshot a market share result. Pair that evidence with completed worksheets, useful drafts, access requests, and retained users. Those are different measurements with different limits.

OpenAI · Search crawlers ↗

Google Search Console · Generative AI performance report ↗

Bing Webmaster Blog · AI Performance ↗

A worksheet to take away

Write an explanation someone can check.

A product explanation worth recommending

For: [specific person and situation]
Their problem: [something they recognize]
They already have: [one realistic input]
We help them produce: [a concrete, inspectable output]
They still decide: [the judgment or permission required]
A worked example: [source → result → boundary]
Available today: [verified capabilities and access]
A poor fit when: [an honest constraint]
The smallest first step: [a useful action before a call]
Evidence to collect next: [a real outcome, with permission]

This method is our interpretation of the cited sources. It does not guarantee rankings, citations, or AI recommendations. Tellshift’s capabilities are described separately on its product pages.