Archive Intel is a compliance software platform. If you're evaluating alternatives to Archive Intel, here's how Comma Compliance compares across architecture, channel coverage, and exam readiness.
Archive Intel is a BYOD-focused archiving solution built around selective capture and contact-based whitelisting. Comma Compliance is an end-to-end solution: capture, archive, supervision, policy matching, and exam-ready case management across 30+ channels, at a flat per-user price with no per-channel fees.
Feature
Comma Compliance
Archive INtel
Architecture
End-to-end — capture, archive, supervision, policy matching, and exam-ready case management, with open source transparency.
AI-powered platform with two distinct products — AI Communications Archiving for capture/supervision and AI Marketing Review for content compliance
Built-in archive
Yes — included in platform
Yes
WORM storage
Yes — written at point of capture
Implied through audit-ready archiving, but not stated as WORM
WhatsApp capture
Captures both WhatsApp Business and personal WhatsApp
Yes.
Transparency
WhatsApp and Signal capture code published openly on GitHub — no NDA, no request required
Proprietary capture methodology not publicly disclosed. Transparent pricing.
Channels supported
30+ channels where conversatiosn happen: iMessage, WhatsApp, Signal, SMS, Voice, Microsoft 365, Teams, Exchange, OneDrive, Gmail, Google Workspace, Slack, Zoom, Webex, Bloomberg Chat, Salesforce, Telegram, and more.
iMessage, Android SMS, WhatsApp, Gmail, Outlook, chat (Slack, Teams, Zoom, Bloomberg), web/HTML, and LinkedIn, YouTube, X/Twitter, Meta
Pricing model
Flat monthly pricing, all platforms included, no per-connector fees, free unlimited exports
Starts at $99/month; text archiving add-on at $35/user/month; per-connector and per-user pricing. No onboarding fees or export fees.
Free trial
Yes
Not publicly offered
Personal vs. business separation
Automatic contact-based filtering — personal contacts can be excluded automatically
Yes. Personal converations stay private, only business comms captured.
Policy processing
Yes — built in
Yes
Custom policy matching
Yes
AI-based message filtering. Structured policy matching not prominently documented.
AI compliance monitoring
Real-time policy scanning; human validation before escalation; no client data used for training without consent
AI-driven supervision
Data ownership
Client retains full ownership; never sold or shared outside authorized sub-processors
Client retains full ownership; never sold or shared outside authorized sub-processors
Infrastructure
AWS and Azure, multi-AZ clustering
Infrastructure not publicly detailed
Archive Intel does not publicly document support for Signal capture. For firms whose compliance exposure includes Signal, that channel would require a separate solution.
Comma Compliance captures Signal natively alongside 30+ other channels in a single platform.
Archive Intel focuses on AI-assisted filtering to reduce review volume. It is designed to surface fewer messages rather than provide full supervisory workflows.
For firms that require structured policy matching, escalation, and case management, additional systems or workflows may be needed.
Comma Compliance includes policy matching, supervision, and exam-ready case management in one platform.
Book a Demo or learn more here.
Due Diligence
01
Where exactly is the message first captured — at the point of delivery, or after a backup or sync cycle?
02
What conditions must be true for a message to be captured? What happens if any of those conditions aren't met?
03
If a user edits or deletes a message before capture occurs, what version gets archived?
04
Can you show documentation — architecture diagrams, code, or independent audit — of how your capture actually works?
05
Where are encryption keys stored, and who controls them?
06
Are all channels included in the base price, or are there per-connector fees?
07
Are there export or egress fees?
08
Does your case management workflow support regulatory examination prep?
09
Can cases be opened directly from flagged message threads?
10
Is any client data used to train your models? Under what conditions?
11
Can we adjust, refine, or contribute feedback to my policy models? (e.g. different languages, customer-complaint responses)
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