Intelligence,
not alerts.
A list of matches is not intelligence. Averrow runs a 42-agent AI mesh that reads every signal the platform collects — email posture, social findings, domain infrastructure, threat feeds — and reasons about what they mean together, in language a human can act on the same day.
An alert tells you what happened. Intelligence tells you what it means.
Most brand-protection tools stop at detection: a phishing domain appears, a row lands in a table, an email fires. That's data, not intelligence — and it leaves the interpretation work to whoever on your team has time to read the row.
Averrow's agent mesh does the interpretation first. Every detection is evaluated against everything else the platform already knows about that brand — its email posture, its social exposure, its domain history — before it ever reaches a person. What reaches your team is a scored, explained, prioritized picture, not a raw feed.
- New domain match: acme-support.co
- New domain match: acme-billing.net
- Social handle flagged: @acme_help_desk
- DMARC record changed
Two lookalike domains registered within 48 hours of each other, paired with a new impersonation account and a DKIM gap that makes spoofed mail from your domain harder to catch — treated as one compound, high-severity situation, not four unrelated rows.
Six agents that reason, not just detect
The 42-agent mesh includes specialized processors for individual feeds, enrichment steps, and platform maintenance. These six are the ones whose output you'll see directly — the reasoning layer on top of everything the mesh collects.
Watches the open feed network around the clock — phishing databases, malware URL lists, Certificate Transparency logs, DNS intelligence — and hands every new hit to the mesh for scoring the moment it appears.
Takes what Sentinel finds and everything else the mesh already knows about a brand, and decides how much it actually matters — folding email posture, social findings, and infrastructure signals into a single composite score.
Steps back from individual threats to look at trend lines — grade changes, new campaign patterns, exposure moving up or down over time — and writes the daily intelligence briefing your team actually reads.
Resolves and plots the infrastructure behind every threat — where a domain is hosted, which provider, which region — so a pattern of attacks from the same infrastructure is visible, not just a list of isolated incidents.
Reconstructs how a threat unfolded — first seen, what changed, what else co-occurred — and writes it up in plain language a human can act on without reading raw log lines.
Applies the same correlation the mesh runs for active protection to forward-looking signal — surfacing where exposure is rising before an incident forces the question.
Four signal streams. One score.
Email posture, social findings, active threats, and domain infrastructure are collected by different parts of the platform — but they describe one thing: how exposed a brand actually is. The mesh fuses all four into a single composite Brand Exposure Score, so priority is never decided by whichever feed happened to fire most recently.
- Cross-system signal fusion across email, social, threats, and domains
- Social intelligence correlated directly into risk scoring, not siloed
- One composite Brand Exposure Score per brand
- Natural-language threat reports generated from the same fused signal
- Automated takedown-evidence generation once a threat clears triage
SQL does correlation. AI does narrative.
Most of what looks like "AI" in brand protection is really just counting and grouping — work a database engine already does in milliseconds. Averrow keeps that split explicit instead of routing everything through a model:
Counting hits, grouping by provider or region, matching a domain against a feed, checking a DKIM selector — anything with a deterministic, rule-based answer runs as fast structured queries across the full dataset. No model call, no latency, no cost per row.
Explaining why a combination of signals matters, writing a daily briefing, drafting takedown evidence in plain language — work that genuinely benefits from reasoning over structured correlation — is reserved for the agents built to do it.
The result: correlation that scales to the full dataset without AI cost attached to every row, and narrative that's only generated when there's something worth explaining.
What a fused narrative looks like
The example below is illustrative — a representative sample of the kind of narrative Blackbox generates once ASTRA has scored a situation as worth explaining, not a live customer report.
The rest of the mesh's inputs
How Sentinel and the feed network find phishing, lookalikes, and malicious infrastructure.
Read more →SPF, DKIM, DMARC, and MX graded A+ through F — and how the grade feeds the exposure score.
Read more →Impersonation and handle-squatting detection across six platforms, with AI confidence scoring.
Read more →See what the mesh finds for your brand.
Run a free exposure scan in under five minutes, or book a demo to see the agent mesh reason through a real detection.