Unabyss

Unabyss is a portable context layer between the applications where work already lives and the AI clients a user wants to use. Instead of rebuilding separate memories in Claude, ChatGPT, Cursor and other assistants, users connect source systems once; Unabyss imports and structures that context, keeps it current and exposes selected slices through the Model Context Protocol (MCP).¹²

The current product supports more than 25 source-app integrations and more than ten AI/MCP clients. Sources include Gmail, Drive, Calendar, Slack, Notion, GitHub, GitLab, Linear, Jira, HubSpot, Pipedrive and meeting tools; client surfaces include Claude, Cursor, Claude Code, ChatGPT, Grok, VS Code, OpenCode, Codex and Gemini CLI.²

Its core advantage is architectural neutrality. Context is not inherently trapped inside one model vendor and can follow the user across several AI clients. This is most valuable for people who actively switch models or agents; it is less compelling for users who live almost entirely inside one vendor ecosystem.

Unabyss says imported material is structured and tagged by topic, sensitivity and source rather than passed as one flat dump, and clients retrieve relevant context over MCP.¹ That matters because more context is not automatically better: a useful memory layer must retrieve the right facts, preserve provenance and avoid flooding the model with irrelevant information.

The critical unresolved product questions are therefore import completeness, freshness, duplicate/conflicting-fact handling, retrieval precision, sensitivity tagging and MCP reliability—not generic LLM intelligence.

Unabyss explicitly states that once context is delivered through MCP to a third-party AI client, the receiving service’s own terms and privacy rules govern that downstream processing.³⁴

This is the central operational caveat. Fine-grained controls inside Unabyss do not automatically impose the same retention, deletion or training policies on Claude, ChatGPT, Cursor or another destination. Teams need to approve both the context source and the AI client receiving it.

The Privacy Policy says Team administrators do not automatically gain access to members’ private Context Data, AI Output, memories or integrations merely because they administer the workspace.⁵

Current public pricing is transparent. Pro is shown at $13/month when billed annually, versus a $15 monthly reference, and supports up to three connected MCP agents and 20 connected accounts. Max is $79/month billed annually versus an $89 monthly reference and removes those account/agent limits. Team is $39 per seat per month billed annually versus a $49 monthly reference and has a three-seat minimum.¹

The current plans include a seven-day trial, and the Terms also describe a Free Plan plus a legacy pay-as-you-go credit model for some older users.⁴

Pro is inexpensive for a heavy multi-AI user if it meaningfully reduces repeated setup and manual retrieval. Max is a much larger jump and mainly makes sense for people who actually hit the Pro limits. Team pricing is reasonable per seat but creates a meaningful minimum spend for very small organizations.

The homepage and Terms also provide useful cost predictability: Pro/Max are presented without surprise overages, and subscription overages are not charged unless expressly accepted in advance.¹⁴

Unabyss published an August 20, 2026 benchmark comparing three Claude Code setups across 20 tasks and 60 runs: connected MCP tools alone, connected tools plus a hand-written CLAUDE.md file, and connected tools plus Unabyss.⁶

The company reports 36% lower cost, 38% faster elapsed time, 60% fewer follow-up questions and accepted-output quality rising from 7.7 to 8.9 out of 10 in the Unabyss arm.⁶ The documented test design is more useful than a generic productivity claim, but the test remains vendor-run and needs external replication.

The direction of the result is plausible: if an agent starts with information it would otherwise have to discover through tool calls and follow-up questions, lower latency and token/tool cost are reasonable. The magnitude is not independently established.

The Privacy Policy identifies OneType Prosta Spółka Akcyjna in Warsaw, Poland as controller and describes the data accessible through connected services.⁵ Gmail, Calendar and Drive can contribute messages, events and document content under the relevant scopes. Unabyss says Google user data is not sold, used for advertising or used to train AI models.⁵

The policy names OpenAI, Anthropic and Google as AI providers that may process Context Data and conversation messages for generation or semantic search, and ElevenLabs for voice transcription. It states that Context Data is not used to train or improve those external models.⁵

Subprocessor disclosure is comparatively strong and includes infrastructure, communications, analytics and integration providers.⁵ The policy also explains EEA storage elements, possible non-EEA processing and transfer mechanisms such as Standard Contractual Clauses.

Retention rules are concrete: Context Data, context files, AI Output and conversation logs remain for the life of the account unless deleted earlier; account closure triggers deletion and backup copies age out within up to 90 days. Copies already sent to external AI destinations are not automatically deleted from those destinations.⁵

Unabyss says data is encrypted in transit and at rest, OAuth/API-key connections are user initiated, passwords are not stored and source-app permissions are read-only by design.³ Read-only source access materially reduces the blast radius of a connector error because Unabyss cannot use those permissions to send mail, edit a document or delete a source record.

The company also describes need-to-know production access, backups, Cloudflare edge infrastructure and a vulnerability-reporting channel.³

SOC 2 Type II is still in progress rather than completed.³ That does not invalidate the technical controls, but it matters for enterprise procurement.

Product Hunt shows strong launch traction, including Product of the Day placements, but currently no user-review population.⁷ The Chrome extension also has no ratings.⁸ Launch attention therefore supports product awareness, not reliability or satisfaction.

The Chrome extension is still relevant as corroboration of the capture workflow: it can save web pages/resources, attach text or voice notes and turn them into structured context.⁸

Unabyss is best suited to people who genuinely use multiple AI tools and have useful context scattered across many work systems. Developers moving among Claude Code, Cursor, Codex and other coding agents are an obvious fit; founders and operators who split research, writing and operations across multiple assistants can benefit for the same reason.

It is also attractive to technically sophisticated users who want context portability without building and maintaining their own MCP ingestion and retrieval stack.

It is a weaker fit for users who mostly use one assistant, organizations that require completed SOC 2 Type II before procurement, or people unwilling to centralize sensitive professional context in a third-party cloud service.

Before a team rollout, buyers should review the DPA/security materials, define prohibited data classes, approve downstream AI clients, test deletion/export and run a controlled retrieval-quality test on representative material.