Interloom

https://interloom.com/

Back-office operations platform where agents and domain experts triage and resolve cases; each resolved case feeds a context graph, and a 'MemoryRank' component reuses prior successful cases to route new work.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 42 cited facts, 6 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T1
Created
2026-03 third-party fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Funding
$16M total Seed · 2026-03 third-party fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
commercial vendor fix
Deployment locus
cloud vendor fix
Platform vs product
both vendor fix
Compliance
none-published vendor fix

Memory taxonomy

storage
proprietary
retrieval
extraction-pull
persistence
long-term
update
extraction
unit
skill
governance
auditable
conflict
none

When to use

Optimised for: enterprise tacit-knowledge capture + governance

Anti-fit: not for consumer / individual use

Pros & cons

Pros

Memory-as-context-orchestration positioning — focuses on what to inject when, not just what to store.

Cons

Newer entrant with limited track record; orchestration framing may overlap with what LangGraph and similar tools already address.

Claims & capabilities

$16.5M seed (March 2026, DN Capital). Customers: Zurich Insurance, JLL, Fiege, Commerzbank, Volkswagen. Processing millions of enterprise cases.

Technical surface

API surface
searched not found
Backend storage
searched not found
Deployment
Managed multi-tenant SaaS (AWS); private single-tenant VPC (Azure); private VPC with bring-your-own-model
Embedding model
searched not found
Multi-tenancy
searched not found
MCP
not documented publicly
A2A
not documented publicly
OpenTelemetry
not documented publicly

Similar systems

Other dedicated memory layers in the catalog, ranked by inbound references.

  • Mem0 T1

    Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.

  • Zep & Graphiti T1

    Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.

  • Cognee T1

    "Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.

  • Hindsight (Vectorize) T1

    Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.

  • Letta / MemGPT T1

    Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.

  • Memori T2

    Structured, persistent memory layer for AI agents. Open source.

Sources (41 cited facts, 6 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-22
    • Pricing “Enterprise pricing; no setup fees; fixed-price per interaction (contact sales)” → “No seat fees (unlimited users); usage-based per agent interaction — prepaid volume packages or pay-as-you-go” source
    • Deployment “Three modes: managed multi-tenant cloud; dedicated single-tenant; BYOM (bring your own model/endpoints)” → “Managed multi-tenant SaaS (AWS); private single-tenant VPC (Azure); private VPC with bring-your-own-model” source
    #219