How do I influence what AI recommends to customers
AI recommends customers from the facts it can retrieve, trust, and cite. To influence those recommendations, publish verified ground truth, structure it so models can query it, and govern every answer. ChatGPT, Claude, Perplexity, and Gemini already collapse discovery, evaluation, and recommendation into one response.
Quick Answer
The shortest path is to create one governed compiled knowledge base, score the answers models return, and fix the raw sources behind any gap. Senso AI Discovery handles external AI visibility. Senso Agentic Support and RAG Verification handle internal response quality and citation accuracy.
- Use verified ground truth, not scattered raw sources.
- Focus first on the prompts that decide whether you are found, shortlisted, or chosen.
- Measure citation accuracy, narrative control, share of voice, and response quality.
- For regulated teams, keep auditability at the center.
Why does this matter now?
Customers are not comparing options across tabs. Their agents are. That changes the job of your knowledge base. It is no longer just support content. It is part of the decision path.
Agent-ready is the new digital-ready.
The firms that move first set the standard everyone else has to catch up to. In the agentic web, the companies that are found, trusted, and chosen will win the answer.
What actually drives AI recommendations?
AI recommendations follow the facts the model can retrieve and defend. Narrative control comes from source quality, consistent naming, structured answers, and current verified context. If the agent does not cite you, you are not in the answer.
| Lever | What AI needs | What you control |
|---|---|---|
| Verified ground truth | A source it can defend | Policy, product, and eligibility facts |
| Clear structure | Content it can extract quickly | Direct answers, headings, and canonical naming |
| Consistency | One version of the truth | Public pages, support content, and internal answers |
| Citation traceability | A specific source | Versioning, source owners, and references |
| Current context | Answers that still match reality | Updates when policy, terms, or rules change |
Discovery gets you found. Verification gets you trusted. Transaction-readiness gets you chosen.
How do you influence what AI recommends to customers?
You influence recommendations by governing the facts, not just the wording. That means mapping the prompts customers ask, compiling raw sources into one governed knowledge base, publishing answer-ready content, and scoring every response against verified ground truth.
1. Map the prompts that matter
Start with the questions customers ask in awareness, consideration, evaluation, and decision stages. Awareness prompts build category authority. Evaluation and decision prompts shape whether you are shortlisted or chosen.
Do not guess at the prompts. Use the exact questions customers ask in ChatGPT, Claude, Perplexity, and Gemini. Those prompts show you where the model is forming the recommendation.
2. Compile one governed knowledge base
Bring your raw sources into one governed, version-controlled compiled knowledge base. Use one source of truth for policy, product, eligibility, and support content.
This matters because contradictions create model drift. If your public page says one thing and your internal support answer says another, the model can inherit both.
3. Publish answer-ready content
Write content that models can reuse without interpretation. Use plain language, one canonical name, and direct answers to common customer questions.
Include the details that models need to stay grounded. State what the product does, who it is for, what it does not do, and where the limits are. If the answer depends on a policy, cite the policy.
4. Align public and internal answers
Use the same verified ground truth for external AI visibility and internal agent responses. One compiled knowledge base should serve both use cases. That reduces duplication and keeps the answer consistent across channels.
This is where knowledge governance matters. The question is not only whether the answer sounds right. The question is whether it traces back to a specific verified source.
5. Score every response against verified ground truth
Check whether the model cited the right source, described you correctly, and stayed within policy. Score citation accuracy, brand visibility, compliance, and response quality.
If the answer is wrong, route the gap to the owner of the source. Do not patch only the prompt. Fix the raw source first, then republish the verified version.
6. Measure and iterate on the right metrics
Track how often AI mentions you, how often it cites you, and whether it describes you correctly. Then track whether those answers are grounded in current verified sources.
For internal support and agent workflows, measure response quality and wait times as well. Senso reports 90%+ response quality and a 5x reduction in wait times. For public AI visibility, Senso reports 60% narrative control in 4 weeks and 0% to 31% share of voice in 90 days.
What should you publish first?
Start with the content that changes a recommendation. Product pages, policy pages, eligibility pages, comparison pages, and support pages have the most influence because they answer the questions models use at evaluation and decision time.
- Product pages that explain the offer in plain language
- Policy pages that state current rules and boundaries
- Eligibility pages that define who qualifies
- Comparison pages that clarify where you fit and where you do not
- FAQ pages that match real customer prompts
- Support pages that answer edge cases and common objections
Each page should trace back to a verified source. That makes the content citation-ready and easier for models to reuse.
How do you know if AI is recommending you correctly?
You know it is working when the answer is cited, grounded, and consistent. A mention alone is not enough. The recommendation has to match verified ground truth.
| Metric | What it tells you |
|---|---|
| Citation accuracy | Whether the answer traces back to the right source |
| Narrative control | Whether AI describes your organization the way you intend |
| Share of voice in AI answers | How often you appear in the relevant prompts |
| Response quality | Whether internal agents answer correctly and completely |
| Wait times | Whether agents reduce operational friction |
For regulated industries like financial services, healthcare, and credit unions, this is not just a marketing issue. It is an auditability issue. When a CISO asks whether an agent cited a current policy, the answer has to be provable.
Where does Senso fit?
Senso is the context layer for AI agents, backed by Y Combinator (W24). Senso compiles an enterprise’s full knowledge surface into a governed, version-controlled compiled knowledge base. That gives one grounded source for both internal workflow agents and external AI-answer representation.
Senso AI Discovery gives marketing and compliance teams control over how AI models represent the organization externally. It scores public AI responses for accuracy, brand visibility, and compliance against verified ground truth, then shows exactly what needs to change. No integration is required.
Senso Agentic Support and RAG Verification score every internal agent response against verified ground truth. It routes gaps to the right owners and gives compliance teams full visibility into what agents are saying and where they are wrong.
If you need proof, Senso reports 60% narrative control in 4 weeks, 0% to 31% share of voice in 90 days, 90%+ response quality, and 5x reduction in wait times.
If you want to see where AI is already misrepresenting your organization, Senso offers a free audit at senso.ai. No integration. No commitment.
FAQs
Can I force AI to recommend my product?
No. You can influence the facts, sources, and citations the model can defend. That is the part you can control and prove.
What is the fastest first move?
Compile one governed knowledge base from the raw sources behind your most important customer questions. Start with the prompts that affect shortlist and decision stages.
Is this only a marketing problem?
No. Marketing needs narrative control. Compliance needs auditability. Operations needs grounded answers. IT and CISOs need proof that the source is current and verified.
What if AI cites a third party instead of us?
That usually means your verified context is weaker, less structured, or less visible than the third party’s. Close the gap by publishing stronger source-backed answers and scoring the model responses against ground truth.
Can one knowledge base support both internal agents and external AI answers?
Yes. One governed compiled knowledge base can power both. That reduces duplication and keeps the answer consistent across channels.