How it works
From "the agent picked someone else" to "the agent picked you"
Six stages, one loop. Each stage produces evidence you can inspect — no opaque scores, no automatic publishing.
1 · Evidence scan
Paste a store URL, product page, sitemap, or CSV. Bismion extracts what agents actually read: product facts, JSON-LD schema, pricing, shipping and return policies, FAQ content, and trust signals. Every finding is stored as evidence with a confidence score — no black-box grades.
2 · Buyer-agent simulation
A fixed panel of buyer-intent queries runs through a real AI agent against your catalog and competitor context. For each query the agent names its pick, what it matched, and what it could not verify. One ad-hoc query is an anecdote; a panel is a metric.
3 · Agent win rate
Win rate is the share of panel queries where the agent picks you. It sits beside your visibility score as the headline number — and unlike mention counts, it moves only when the agent's actual decision changes.
4 · Losing factors → fixes
Every lost query comes with named losing factors: the missing certification, the unattached return policy, the schema gap. Bismion generates the fix for each one — JSON-LD, FAQ blocks, product copy — targeted at the exact reason you lost.
5 · Approval gate
Nothing publishes on its own. Fixes queue for your review; you approve or reject each one. Approved fixes feed the next simulation as merchant-approved facts, so you preview the verdict flip before anything touches your live store.
6 · Re-run and prove it
Re-run the panel after approving fixes. The before/after report shows the baseline verdict, the fixes applied, the new verdict, and the win-rate delta — the artifact you show your team, your boss, or your client.