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Sai researches each account before it scores it — so every number comes with the evidence behind it.
You describe a good account once. Sai does the other steps.
Point Sai at your list and describe what makes an account worth working.
Sai opens every account and checks what the company is actually doing now.
Sai writes a score, one line of reasoning, and the source link behind it.
Open the sheet, spot-check a few sources, and give your reps the top rows.
A score is only useful if a rep believes it. Sai shows its work, and rescores every week as accounts change.
Most scoring reweights fields already sitting in your CRM. Sai opens each account first and scores what the company is doing now, not what someone typed into a form last year.
One line of reasoning and a source link sit next to every number. When Sai can't verify a signal, it marks the row unverified instead of guessing.
Sai works in a real browser inside your own logged-in sessions, so it can read and update the CRM, portals and internal tools that have no public API.
Accounts change: they hire, raise, pivot. Sai rescores your whole list on a schedule, unattended, and flags what moved — and every repetition makes it cheaper and more reliable.
AI-powered fit scoring for an ICP lead list is the process of having an AI agent evaluate every account on your target list against your ideal customer profile, then rank them so reps work the best-fit accounts first. Most tools approach this as filtering: you set attributes, and records that match come back. That produces a list of companies that look like your ICP on paper. It does not tell you which ones are showing buying behavior right now.
Sai is an autonomous AI assistant from Simular that operates a real browser, so it scores the way an analyst would. It opens the company site, reads the careers page, checks recent funding, looks at leadership activity on LinkedIn, and applies your weighting rules to what it actually finds. Each score arrives with the signal behind it and a source link, which means the list is auditable rather than a black box.
Sai takes a different starting point. Instead of reweighting what is already in the record, it opens each account, reads the website and public profiles, and checks the things a rep would check: whether headcount is growing, whether they are hiring for the roles that signal your problem, whether they recently raised, what the product actually does now. The score is written from that research, with one line of reasoning and a source link attached. A rep can disagree with a score in ten seconds instead of quietly ignoring the column.
Scoring is only half the job — the score has to land where your team works. Many CRMs, industry portals and internal deal systems expose no public API, or gate it behind a tier nobody wants to buy. Sai avoids that problem entirely by operating a real browser inside your own logged-in sessions. If a person can click it, Sai can run it, which means it can read your account list and write scores back into the same system your reps already use.
An account that scored a 4 last quarter may be an 8 today, because it raised, started hiring, or shipped something adjacent to your product. A list scored once is a snapshot that quietly rots. Sai saves the scoring instruction as a recurring workflow, rescores the full list on your cadence without supervision, and flags every account whose score moved so your team sees the change rather than the number. The hundredth run costs less and breaks less than the first.
Free your hands from the computer.
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