Panira is an AI diagnostic run with human experts. It shows a board what the outside world can already see about its company, then what only its own data can show, and what to do about both.
Seventeen lenses on your company, from the outside and then from the inside. Outside-In from the public record in hours, Inside-Out from your own data, and every finding graded before it is matched to the specialist or the agent that fixes it.
Two negotiations the business would want to run apart arrive together, with the lender watching the lease outcome. The board has a year. The record suggests it is not being used.
3.1m members generate first-party data with no retail-media revenue line against it. The asset sits on the balance sheet as a cost, not a P&L.
Seven lenses reached the same point from different evidence: the mid-market weekly shop is the one both ends are taking.
The hires a company makes are public. The one it has not made is the finding.
The data to defend the mid-market basket, and to build a media business on it, is already inside Larkfield, unused. The lease and facility window decides whether there is time to do either.
Two negotiations the business would want to run apart arrive together, with the lender watching the lease outcome. The board has a year. The record suggests it is not being used.
Give us your company web address and the engine analyses everything the public record says about you. What comes back is the two or three things a rival, a buyer or an investor would find if they looked properly, graded for how much they matter and how quickly you can test them, rather than a summary of your accounts.
Every finding in the example comes from the kind of public record we use: filings, hiring, reviews, press. Your summary carries your own findings, from your own record.
Outside-In, intervention, Inside-Out, intervention. One engine, two data sets, and the work that follows each. Click a step to see it work.
Seventeen lenses analyse the company from the public record, before anyone has spoken to it.
The right specialist or agent for each finding, from the marketplace.
The same engine on your own data, tested with the people who know the business.
Validated findings matched to interventions, with the engine still observing.
The same evidence, put in front of seventeen agents. The specialists each work alone, so none can influence the others. An agent provocateur attacks what they found. Prometheus, the seventeenth agent, integrates it into one picture, with our experts working through it.
Each outside hypothesis meets the internal data only your people hold. Some are confirmed, some reframed, some dropped. Click a finding to test it.
Outside-In findings are hypotheses with the evidence attached. The first interventions are the ones you can start before anyone inside has confirmed them: the four-week work the rest of the plan waits on. Three from the Larkfield example.
Every finding routes to a specialist or an agent, drawn from a rated marketplace. You pick the team, and the engine keeps watching.
Inside-Out confirms, reframes or drops each outside finding against your own data. The work that follows is smaller, more precise and owned by the people who will do it. The same three, after the inside pass.
Then the engine keeps observing, and the next Outside-In starts from a sharper point.
Most firms leave a report. We leave an engine. You own it.
Enter your role, a work email and your company web address. We run Outside In on your company and send you a summary report within 24 hours, with a link to buy the full report if you want the whole picture.
