Search every source at once
One name queries every web and social source you have enabled, with results merged and de-duplicated.
Adverse media
Find the negative news about your customers, and let AI do the sorting.
Search the open web and social media for negative coverage of a person or company. Your own AI provider judges every result, noise is cleared automatically, and analysts review only what needs a human decision.
Every result read for you
Your own AI provider reads each result and checks whether it is really about the subject and what kind of conduct it describes.
Less to review
Irrelevant results are filtered out and the rest arrive already checked and sorted, so analysts have far less to read.
An informed decision, faster
Analysts get the evidence and a clear recommendation, and make the final call. A share of automatic checks is spot-checked by a person.
How it works
From a name to a recorded decision.
From a name to a recorded decision, in five steps that keep a person in charge of what counts.
- 01
Search
Enter a subject name, for a person or a company. Every enabled web and social source is searched together.
- 02
Read
Your AI provider reads each result and gives it a subject-match confidence, a category-relevance score and an adverse-topic verdict.
- 03
Sort
Results below your floors are discarded. The rest are cleared, tagged with their verdict, or held in an exception queue for a person.
- 04
Decide
An analyst reviews what is left, with the evidence beside it, and confirms or rejects. Any automatic result can be overridden with a written reason.
- 05
Record
Link the search to a customer so the findings sit on their record and count toward their risk classification.
Search
One search, every source.
Search a subject once, and link the search to a customer when you want the findings on their record.
Type a subject name once and OneView queries every web and social source you have enabled at the same time, merging and de-duplicating the results. Search open web and social coverage for negative news about a person or a company, and optionally link the search to a customer.
A search can run on its own for a quick check, or be linked to a customer so the findings sit on that customer's record. The Results page lists recent searches across every customer, or narrowed to one, with their status and a View result action.
AI review
The heavy reading is done before an analyst starts.
OneView reads, checks and organises every source for you, so your analysts have far less to review and can make an informed decision faster.
Your own AI provider reads each result so your analysts do not have to. It checks whether the result is really about the subject, and what kind of conduct it describes, such as financial crime, corruption or bribery, sanctions evasion, cybercrime or corporate fraud. Each result arrives with its source, its adverse-topic verdict and a confidence score.
Results that clearly have nothing adverse are cleared and tucked away, and clear, high-confidence findings are tagged with their verdict, so each one reaches the analyst already checked. Anything uncertain stays in an exception queue for a person, and an analyst can override any of it.
What reaches an analyst comes with the evidence beside it and a subject profile summarising who the results appear to be about, noting that it comes from search results and is not independently verified. The analyst confirms or rejects, so the final decision stays with a person, and is made faster.
Sources
Choose where OneView looks.
Connect the web and social sources you want searched. Every enabled one is used on every search.
Connect the web search providers you use, such as Google Programmable Search Engine, Brave Search API, SerpApi, Tavily and Exa. Every enabled source is queried on every search, and the results are merged and de-duplicated. There is no preferred or failover source, so any number can be enabled at once.
Social search extends web search to X / Twitter, Reddit, TikTok, Facebook, Instagram and LinkedIn, each with its own switch. A native API connection is available for X and Reddit for better coverage.
Controls and governance
Tune the automation, and keep it accountable.
You decide how aggressive the automation is, and a person keeps checking what it decides.
A candidate below the subject-match or category-relevance floor is discarded before an analyst ever sees it, which is the main control against false positives when a common name would otherwise surface many irrelevant articles. Separate auto-accept and auto-tag floors decide what is decided automatically, and raising them toward 100 returns you toward all-manual review.
Automatic decisions stay accountable. A share of auto-accepted and auto-tagged candidates is drawn into a sampling review queue for a human spot-check, and a reviewer agrees or overrides with a written rationale. A review frequency, a Confirm review action and a review history keep the cadence visible, with an annual validation recorded in AI governance.
Start a conversation
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See how a single sweep across connected sources, AI-assisted judgment, and manual review come together in one adverse media workflow.