This guide is for AI implementation and applied-LLM consultancies whose public descriptions are vague or inconsistent, so search results and AI answers default to generic labels. It covers how short summaries get formed and how to make yours specific, consistent and checkable.
Compression is the problem
Search results and AI answers summarize firms in a sentence. The summary is built from what the firm says about itself and what other sources say about it. If your pages say “AI solutions for the enterprise”, the compressed version is “AI consultancy”, or whichever category is most common around your name.
Your field also moves quickly. Last year’s vocabulary can already be out of date on pages and profiles that nobody has revisited.
Make the offer specific
- Use cases. A page for each application you build repeatedly: support assistants, document processing, forecasting, search over internal knowledge.
- Method. How you scope, prototype, evaluate and deploy, including the evaluation criteria and test sets you use.
- Data handling. Where data goes, what is retained and how access is controlled, in plain terms.
- Limits. What you will not build, or when you recommend not using AI at all.
- People. Named engineers and researchers with background a reader can verify.
Make it consistent
- Choose a short description of the firm and use it on your site, LinkedIn page, directories and partner listings.
- Retire pages and listings that describe past positioning.
- Use the same names for your offers everywhere, so sources agree.
- Check what AI engines say on a fixed prompt set, and record the sources they cite.
What to change first
- Rewrite the homepage and about page with one sentence naming what you build, for whom and how you measure success.
- Add use-case pages for the three applications you deliver most often.
- Publish a short evaluation-and-governance page that a technical or legal reviewer can read.
- Review partner and directory listings for outdated descriptions.
Quick diagnostic map
Starting hypotheses to test against your own prompt results.
| Symptom | What to check | Likely fix |
|---|---|---|
| AI answers call you a “chatbot company” or “AI agency” | The exact wording and the sources it cites | Add specific use-case pages and align profiles |
| Different sources describe different offers | Site, profiles and partner listings side by side | Choose one description and update every source |
| Technical buyers ask for evaluation details on every call | Call notes and repeated questions | Publish the method and evaluation page |
Limits of this guide
Engines differ and change, and no one can guarantee a particular description.
Do not publish performance figures, model claims or client names you cannot support.
Common questions
Why do AI answers call my consultancy a chatbot vendor?
Short summaries are built from what your pages and other sources say. If those are vague or inconsistent, the summary defaults to the most common category. Specific, consistent descriptions give an answer something accurate to use.
What should an AI consultancy publish about its method?
How it scopes, prototypes, evaluates and deploys, what evaluation criteria and test data it uses, how data is handled, and what it will not build.
How often should an AI firm review its descriptions?
Regularly, because the field’s vocabulary changes quickly. A quarterly check of the site, profiles and AI answers on a fixed prompt set is a reasonable starting point.
Checking how AI engines describe a firm, and which sources they rely on, is part of AI search optimization.
Kryston publications
Analysis for IT services and software development firms on search, AI visibility and buyer trust.
