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Core AI · Module 04 / Language

Every word your
enterprise already owns.

Semantic search, translation, sentiment and structured extraction — turn contracts, tickets, calls and wikis into a queryable knowledge fabric.

Explore the moduleSee it in action
17
native languages
1.2M
graph nodes / tenant
22ms
retrieval latency
clickripple · language · workspace
LIVEus-east
Policy · 2026
Ticket #48219
Contract · Acme
KB · onboarding
Call · 2026-07-12
Semantic query
Why did Acme's renewal churn last quarter?
Knowledge graph · 1.2M nodes · 8.4M edges
Answer
Renewal churn driven by SLA breaches on ticket #48219 and misaligned onboarding — see contract §4.2.
Cited sources
  • Policy · 20260.92
  • Ticket #482190.90
  • Contract · Acme0.97
  • KB · onboarding0.95
  • Call · 2026-07-120.93
HomeCore AINatural Language Processing
What it does

Enterprise knowledge is not a chatbot problem. It's a graph problem — waiting to be asked the right question.

cr-embed-4 and cr-reason-1 turn every document, ticket, call and email into a typed, cited, queryable knowledge fabric. Not another search bar. An answer engine that knows what it doesn't know.

01
Semantic understanding
Meaning-first retrieval across contracts, tickets, wikis and voice with 22ms p95 latency.
02
Entity + relationship graph
Every document flows into a typed graph — organisations, contracts, SLAs, sentiment.
03
17 languages, one contract
Translation and cross-lingual retrieval are first-class — no per-locale rebuilds.
How it works

One continuous flow from signal to outcome.

Every stage is instrumented, observable and safe to swap. No timelines. No boxes to connect by hand.

  1. Stage 1
    Ingest
    connectors
    Contracts, tickets, calls, emails and wikis are streamed into the language runtime.
  2. Stage 2
    Structure
    entities + relations
    cr-reason-1 extracts typed entities and links them into a tenant-scoped graph.
  3. Stage 3
    Embed
    cr-embed-4
    Every chunk is embedded and indexed alongside its provenance and permissions.
  4. Stage 4
    Reason
    answer engine
    Queries fan out over graph + vectors; answers arrive with sentence-level citations.
  5. Stage 5
    Serve
    APIs + surfaces
    Same runtime powers search, chat, analytics and compliance workflows.
Enterprise knowledge

The answer,
with its evidence.

language · knowledge · workspace
LIVEus-east
Corpora
  • Contracts128k
  • Tickets412k
  • Calls2.1M
  • Wiki88k
  • Emails6.4M
Languages
ENDEFRESPTITNLJAZHARTRHI
Query
Show accounts at churn risk with SLA breaches in EMEA.
Results · ranked
  1. Acme Ltd · SLA breach ×3 · sentiment −0.420.94
  2. Northwind AG · pricing dispute Q30.88
  3. Contoso SA · onboarding delay 21d0.83
  4. Fabrikam Oyj · renewal downgrade signal0.79
Sentiment · 30d
Entities
  • Acme Ltdorg
  • EMEAgeo
  • SLA breachconcept
  • Q3period
  • renewalevent
Enterprise applications

Where natural language processing earns its keep.

Legal & compliance
Legal & compliance
Case · 01 / 04

Search across 128k contracts with obligations extracted.

AI capability
Clause extraction · graph · citation.
Business result
6× faster contract review · 100% audit trail.
Explore this scenario
Performance

Numbers that earn deployment.

22ms
Retrieval latency
p95 over 1.2M nodes
6×
Faster review
measured on legal workflows
94%
Citation accuracy
sentence-level attribution
17
Native languages
single embedding space
Compatibility

Meets your stack where it lives.

Bring your own models, cloud and data. Everything speaks the same runtime contract.

Language models
cr-reason-1cr-embed-4GPT-4.1Claude 4mBERT · XLM-R
Vector & search
pgvectorPineconeElasticOpenSearchcr-index (native)
Sources
SharePointConfluenceNotionZendeskSalesforceGongGmail · Outlook
Governance
ACL propagationPII redactionGDPR · CCPAResidencySigned answers
Frequently asked

Straight answers.

RAG is one primitive we use. The runtime also builds a typed entity/relationship graph and reasons over it — answers cite specific spans, not fuzzy chunks.
Every chunk inherits ACLs from its source. Queries execute under the user's identity — the model can never surface content the user shouldn't see.
Only inside your tenant, only for retrieval quality, only with your written approval. Nothing crosses the tenant boundary.
Streaming ingestion keeps the index within seconds of the source. Batch reconciliation runs nightly.
Yes. The full stack runs in your VPC or on-prem, with the same APIs as our managed cloud.
Natural Language Processing

Deploy this module in your enterprise.

Talk to an AI engineer. Ship a working proof-of-value in weeks, not quarters.

Book a working session