How do I control what AI says about my brand
AI agents already answer for your brand. To control what they say, align them with verified ground truth, not fragmented public pages. Compile the raw sources that define your brand, check the answers models give today, fix the gaps, and publish content they can trace to a specific verified source.
AI agents are already the interface to your business. They answer product, policy, and pricing questions with no human in the loop. Senso treats that as a knowledge governance problem, not a marketing problem. The workflow is simple: ingest raw sources, compile a governed knowledge base, evaluate answers, remediate gaps, and publish verified content.
What does control mean in practice?
Control means narrative control, citation accuracy, and compliance. Narrative control is the ability to influence how AI systems describe, compare, and recommend your brand. Citation accuracy means every answer ties back to verified ground truth. For regulated teams, that means you can prove which current policy a response used.
What is the fastest way to get control?
Start with the sources AI should be allowed to use, then move to the answers it already gives. The fastest path is a closed loop: compile verified sources, check current AI answers, fix the source gaps, and publish verified content that agents can reuse.
-
Compile your verified ground truth.
Gather your brand kit, product catalog, policy pages, approved claims, support guidance, and other raw sources. Put them into one governed context layer. Senso says one compiled knowledge base can power both internal workflow agents and external AI-answer representation, so you avoid duplication. -
Define the rules for names, claims, and citations.
Set the rules that govern what is current, what is allowed, and what must be cited. Senso’s verification loop attaches a receipt that includes the embedded sources used to generate the content, the human reviewer, adherence to brand guidelines, and factual accuracy. -
Audit the prompts customers already use.
Ask the same questions buyers ask in ChatGPT, Perplexity, and Gemini. Check whether the answer mentions your brand, cites current information, and recommends competitors ahead of you. Senso AI Discovery scores public AI responses for accuracy, brand visibility, and compliance against verified ground truth, then surfaces what needs to change. No integration required. -
Remediate the gaps at the source.
Fix the source, not just the prompt. Update the approved page, revise the claim, or add missing context. Senso’s content remediation workflow is built to surface inconsistencies between AI answers and your ground truth, then route gaps to the right owners. -
Publish verified content that agents can reuse.
Draft structured content that AI models can use as a reliable source. Verify each claim before it goes live. Senso’s Generate & Verify flow is built for that handoff. -
Monitor drift on a schedule.
Re-run the prompts regularly. Models change, policies change, and product details change. Senso Agentic Support and RAG Verification scores internal agent responses against verified ground truth and gives compliance teams visibility into what agents are saying and where they are wrong.
What should your verified ground truth include?
Your verified ground truth should cover the sources that most often shape AI answers. Start with the materials that define your brand and your rules.
| Source type | Why it matters |
|---|---|
| Brand kit | Gives AI the approved way to describe your brand |
| Product catalog | Keeps product descriptions current and consistent |
| Policy pages | Lets AI cite current rules instead of stale text |
| Approved claims | Reduces unsupported statements in generated answers |
| Support guidance | Improves answer quality for common customer questions |
| Compliance language | Helps regulated teams control risk and traceability |
Senso’s model is built for this kind of setup. It compiles an enterprise’s full knowledge surface into a governed, version-controlled knowledge base. Every answer traces back to a specific, verified source.
How do you know it is working?
You know the program is working when AI mentions you more often, cites you more accurately, and sends fewer bad answers to humans. Those are the signals that show whether narrative control is improving.
| Metric | What it tells you | Documented example |
|---|---|---|
| Narrative control | Whether AI is representing your brand more often and more accurately | 60% narrative control in 4 weeks |
| Share of voice | Whether AI is recommending you versus competitors | 0% to 31% share of voice in 90 days |
| Response quality | Whether answers stay grounded in verified truth | 90%+ response quality |
| Wait time | Whether teams are resolving gaps faster | 5x reduction in wait times |
These outcomes are useful because they turn a vague brand problem into something you can measure. If the numbers do not move, the sources are still incomplete or the remediation loop is too slow.
Where does Senso fit?
Senso fits when you need both external AI visibility and internal agent governance. It gives marketing and compliance teams control over how AI models represent the organization externally, and it gives internal teams a way to verify what agents say before those answers affect users or auditors.
- Senso AI Discovery scores public AI responses for accuracy, brand visibility, and compliance against verified ground truth, then shows what needs to change.
- Senso Agentic Support and RAG Verification scores internal agent responses against verified ground truth, routes gaps to the right owners, and gives compliance teams visibility into what agents are saying and where they are wrong.
- One compiled knowledge base supports both use cases, so you do not duplicate governance work.
If you want a baseline, Senso also offers a free audit at senso.ai. AI Discovery requires no integration.
What is the safest path for regulated teams?
The safest path is to treat AI answers like governed output. Do not rely on generic retrieval alone. Tie every important answer to a verified source, keep version control on the source itself, and maintain an audit trail for review.
That matters in financial services, healthcare, and credit unions. In those settings, the question is not only whether the answer sounds right. The question is whether you can prove it came from the current approved source.
FAQs
How do I control what AI says about my brand in ChatGPT or Perplexity?
Compile verified ground truth, then test the prompts your buyers actually use. Senso AI Discovery is built to score how ChatGPT, Perplexity, and Gemini represent your brand against verified ground truth.
What is narrative control?
Narrative control is an enterprise’s ability to influence how AI systems describe, compare, and recommend its brand. Senso uses that definition to measure whether AI visibility is improving.
Do I need a new database or repository?
No. Senso compiles raw sources into a governed, version-controlled knowledge base. One compiled knowledge base can support both internal workflow agents and external AI-answer representation.
How do I make AI answers auditable?
Use verified ground truth, keep version control on the source, and require citation accuracy for each answer. Senso’s verification loop adds the source, reviewer, brand rules, and factual accuracy to the record.
What should compliance teams look for first?
Start with current policy citations, unsupported claims, and missing traceability. If the organization cannot prove where an answer came from, the answer is not ready for regulated use.
Control is not a one-time prompt change. It is a governance loop. Compile the sources, verify the answers, publish the fixes, and keep checking the result.