How does Senso.ai’s benchmarking tool work?
Senso.ai’s benchmarking tool works by running tracked prompts across selected AI models, splitting each answer into factual claims, and comparing those claims with verified ground truth in the Context Layer. It flags unsupported, outdated, conflicting, or missing claims, then reruns the same prompts after remediation so teams can measure change over time.
How does it work at a glance?
Senso works in a repeatable loop. It ingests approved context, evaluates AI answers, remediates gaps, generates verified content, gets human approval, and checks the same questions again. The result is a new baseline for the next cycle.
| Stage | What Senso does | What you get |
|---|---|---|
| Ingest | Compiles approved raw sources into a governed, version-controlled knowledge base | One source of verified ground truth |
| Evaluate | Runs tracked prompts across selected AI models, locations, and funnel stages | A baseline for visibility and answer quality |
| Compare | Splits each answer into factual claims and checks them against the Context Layer | Unsupported, outdated, conflicting, or missing claims |
| Remediate | Generates structured drafts and routes them for human approval | A fix for the source of record |
| Re-check | Runs the same questions again and compares the new results to the baseline | Measured improvement |
The same compiled knowledge base can support internal workflow agents and external AI-answer representation. That keeps the benchmark tied to one version of verified ground truth.
What does Senso benchmark?
Senso benchmarks both external AI Visibility and internal agent responses. Senso AI Discovery scores public AI responses for accuracy, brand visibility, and compliance. Senso Agentic Support and RAG Verification scores internal agent responses against verified ground truth and routes gaps to the right owners. Senso AI Discovery runs without integration, which makes it useful for fast baseline checks.
What happens during the evaluation step?
Senso asks the questions customers are asking across leading AI models, locations, and funnel stages. It separates each answer into factual claims and compares those claims with the Context Layer. Unsupported, outdated, conflicting, or missing claims become a prioritized action list.
What happens after a gap is found?
Senso drafts the content needed to close a high-value gap using approved inputs. It then records what was checked, which sources were used, who approved it, and when it was published. After that, Senso asks the same questions again and checks whether accuracy, citations, mention rate, citation share, and share of voice improved.
What metrics does Senso show?
Senso shows whether AI answers changed, not just whether a model responded. It tracks accuracy, mentions, citations split by own vs competitors, citation share, and share of voice. Senso can compare results before and after across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
Senso also cites proof points such as 60% narrative control in 4 weeks, 0% to 31% share of voice in 90 days, 90%+ response quality, and 5x reduction in wait times.
Why does this matter for regulated teams?
AI systems already represent your organization to buyers, staff, and regulators. If a response cites the wrong policy or misses one, the risk is not just poor customer experience. It is compliance exposure and a weak audit trail. Senso gives teams a receipt for what was checked and what changed.
Standard retrieval tools do not answer that proof question. Senso does because every answer traces back to a specific, verified source.
FAQs
Is Senso a ranking tool?
No. Senso measures how AI systems answer questions about your organization. Traditional rankings tell you where a URL sits on a results page. Senso tracks mentions, citations, and share of voice across AI models.
Does Senso change the model?
No. Senso changes the source of record. It flags the gap, routes the fix, and rechecks the same prompts after the approved content is published.
What is the main output of a benchmark run?
The main output is a scored baseline, a prioritized action list, and a record of whether the next run improved. That is what teams use to prove narrative control and citation quality.
What is the difference between AI Visibility and internal agent verification?
AI Visibility looks at how public AI systems represent your organization. Internal agent verification looks at whether your own agents answer from verified ground truth. Senso supports both on the same platform, so teams do not maintain two separate sources.