Iris.ai
Enterprise R&D document processing. Researcher Workspace lets users accumulate a corpus + apply self-written context filters; extracted data from experiments across hundreds of papers persists in a structured database. Regulated-industry focus (pharma, materials).
At a glance
- Type
- Researcher Workspace + persistent extraction
- Tier
- T2
- Created
- 2015 (co-founded 2015 by Anita Schjøll Abildgaard; Oslo Norway)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- searched not found
- Funding
- Series A $8.3M (Silverline Capital) May 2024; total $21.6M
Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- cross-session
- update
- extraction
- unit
- document
- governance
- inspectable
- conflict
- append
When to use
Optimised for: research-workflow integration + provenance + claim grounding
Anti-fit: not for non-research / non-academic use cases
Pros & cons
Pros
Long-running scientific knowledge discovery; explicit problem-statement-to-papers semantic mapping.
Cons
Smaller user base than Elicit; product positioning has evolved multiple times.
Claims & capabilities
Enterprise R&D positioning. Norway-based.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- Managed-only
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- no MCP support advertised — vertical product, no MCP server / client integration documented
- A2A
- no A2A protocol support advertised — vertical product, no A2A integration documented
- OpenTelemetry
- no OpenTelemetry integration advertised — vendor logs/observability not publicly documented
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