How does Senso.ai’s benchmarking tool work?
Senso.ai’s benchmarking tool works by running the questions your customers already ask across selected AI models, locations, and funnel stages, then splitting each answer into factual claims and comparing those claims with verified ground truth in the Context Layer. Unsupported, outdated, conflicting, or missing claims become a prioritized action list. The same questions run again after remediation so teams can see what changed and prove it.
That turns AI Visibility into a repeatable loop instead of a one-time check. It also gives marketing, compliance, IT, and security teams one source of truth for how AI systems represent the organization.
What does Senso.ai benchmark?
Senso benchmarks the answers AI systems generate about your organization. It checks whether public AI answers and internal agent responses are grounded in approved context, cite the right source, and reflect current policy, product, and pricing information.
Senso uses the benchmark to measure the signals that matter most:
- Accuracy against verified ground truth
- Citation accuracy
- Mention rate
- Citation share
- Share of voice
Senso can run these checks across frontier models and systems like ChatGPT, Perplexity, Gemini, and Google AI Overview. That lets teams see how representation changes by model, location, and funnel stage.
How does the benchmarking loop work?
Senso follows a fixed loop so the results are comparable from one run to the next. The process starts with approved context and ends with a new baseline after remediation.
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Ingest approved context.
Senso compiles approved raw sources into a governed, version-controlled Context Layer. One compiled knowledge base supports both internal workflow agents and external AI-answer representation. -
Run tracked prompts.
Senso asks the questions customers are asking across selected AI models, locations, and funnel stages. It captures the generated answers for evaluation. -
Break each answer into claims.
Senso separates each response into factual claims instead of scoring the answer as one block. That makes it easier to isolate exactly what is supported, unsupported, outdated, or missing. -
Compare claims to verified ground truth.
Senso checks each claim against the Context Layer. Every answer traces back to a specific, verified source. -
Turn gaps into an action list.
Unsupported, outdated, conflicting, or missing claims become a prioritized list. The team can fix the source of record, add approved information, or route the gap to the right owner. -
Generate verified content and approve it.
Senso can draft the content needed to close the gap and then require human approval before it is published. The receipt shows what was checked, which sources were used, who approved it, and when it was published. -
Run the same prompts again.
Senso repeats the same questions across the same models and locations. That shows whether accuracy, citations, mention rate, citation share, and share of voice improved. -
Set the new baseline.
The latest results become the baseline for the next cycle. Teams can track change over time instead of guessing whether the fix worked.
What happens after Senso finds a gap?
Senso does not stop at detection. It turns each gap into a specific remediation task, then re-tests the same prompts after the source or content changes.
That matters because AI answers change quickly as models update and sources shift. Senso’s loop keeps the benchmark tied to current reality, not last month’s output.
The re-test also creates an audit trail. Teams can show what changed, who approved it, and whether the new answer is grounded in verified ground truth.
What do teams see in the results?
Senso gives teams a baseline, a trend view, and a receipt for each cycle. The baseline shows where AI answers stand now. The trend view shows whether the key visibility signals moved after remediation.
A simple way to read the results is this:
| Result | What it tells you |
|---|---|
| Baseline | Where AI answers stand right now |
| Prioritized gaps | What needs to be fixed first |
| Trend view | Whether accuracy and visibility improved over time |
| Receipt | Which sources were checked, who approved them, and when they were published |
Senso also documents 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. Those numbers show what teams can track when the loop is working.
Who uses Senso’s benchmarking tool?
Marketing and compliance teams use Senso AI Discovery to control how AI models represent the organization externally. That is the AI Visibility use case. It helps them see whether AI answers are accurate, on-brand, and compliant against verified ground truth.
CISOs, IT leaders, and compliance officers use Senso Agentic Support and RAG Verification to check internal agent answers. That use case focuses on citation accuracy, auditability, and proof that an answer came from a current source of record.
Operations teams use the same loop to reduce drift and response delays. Senso documents a 5x reduction in wait times in its proof points, which shows how remediation can affect day-to-day handling speed.
Does Senso require integration?
No. Senso AI Discovery requires no integration to start the benchmark. Teams can get a baseline without changing their existing stack first.
That makes it easier to see the current state before any remediation work begins. It also lowers the barrier for marketing and compliance teams that need quick visibility.
What is the main difference between Senso and a standard retrieval tool?
Senso verifies answers against ground truth. Standard retrieval tools usually return documents or snippets and stop there.
Senso checks whether the model’s response is supported, then shows what is wrong, what to fix, and whether the fix changed the answer on the next run. That is the difference between retrieval and knowledge governance.
Why does Senso rerun the same questions?
Senso reruns the same questions so teams can compare like for like. If the prompt changes, the model changes, or the location changes, the benchmark is no longer clean.
Running the same prompts across the same models and locations shows whether remediation actually improved the answer. It also gives teams a stable baseline for the next cycle.
What is the Context Layer?
The Context Layer is Senso’s governed, version-controlled knowledge base compiled from approved raw sources. It is the source of record the benchmark uses when it checks whether an answer is grounded.
That matters because AI systems already represent the organization whether the organization has verified the context or not. Senso gives teams a way to prove what the model should have said, what it actually said, and what changed after remediation.
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