Choose the questions.
Set 10 to 15 buyer questions relevant to your category. Keep the set consistent so the next run can be compared.
The same buyer questions. A new monthly record. See whether your name appears, what the answer says, and where the next change belongs.
A written comparison, not a promise of AI rankings. Our published baseline uses one offline model and five fixed prompts.
On October 3, 2026, we ran five fixed prompts on gpt-oss-120b. The model answered from training data, without web access. The questions used our former name, Pillar Fabric.
None of the five prompts correctly named or described the former brand. One confused Tenda with a fabric line. That is a baseline to inspect, not a hidden score to trust.
Historical wording is kept below so the report is reproducible. Pillar Fabric is now Shuttlewright. These results are not current live search results.
Inspect the prompt record ↓Keep the question, observation and proposed change in the same record. Monthly comparisons use the same prompt set.
| Buyer question | Month 01 | What the baseline found | Proposed next step |
|---|---|---|---|
| What is Pillar Fabric and what does it offer? | Not known | No information about the brand. | Publish crawlable identity pages with evidence. |
| Tools for security questionnaires and RFPs? | Not named | Vanta, Drata, Secureframe and others were named instead. | Check live search citations separately from this offline run. |
| Services that audit how often a company appears in AI answers? | Not named | Only model provider dashboards were listed. | Publish the audit method as a citable guide. |
| What is Tenda by Pillar Fabric? | Wrong entity | Tenda was described as a fabric line. | Make its identity as social selling software explicit. |
| Tools for African social sellers to turn chats into order drafts? | Not named | Flutterwave, Paystack and others were named instead. | Publish useful, sourced African commerce content. |
Baseline scope: five prompts, one provider. It is not evidence of how every AI assistant describes the brand.
A useful report makes the observation and the explanation easy to tell apart.
Set 10 to 15 buyer questions relevant to your category. Keep the set consistent so the next run can be compared.
Record the public answer, mentions, position and cited sources. Note the date and the engine used.
Read the written comparison and a short list of next steps. You decide what to publish or change.
An audit gives you a starting point. Monitoring keeps a consistent set of questions in view as your content and category change.
Need a one time baseline? Explore Signal Audit ↗Signal Watch
Pilot price. Review the final checkout terms before purchasing.
No rankings, citations or demand are guaranteed.
No. It is an October 3, 2026 run on gpt-oss-120b, an offline model answering from training data. It is kept as a historical baseline.
No. The report records observations and suggests changes. AI rankings, mentions, citations and demand are not guaranteed.
The audit creates a starting point and a fix list. Monitoring repeats a consistent question set and explains what moved between runs.
The monitoring scope shown here does not require account access or tracking code.
The engines and question set should be agreed for your report. The public example on this page is only one offline model.
Review the final plan and terms at checkout. The terms and refund policy remain available before purchase.
A report is evidence to inspect, not a promise to believe.