Core AI · Chapter 01 / Intelligence
The reasoning
operating system
for the enterprise.
Models, agents, tools and traces — one console, one contract, one runtime. Deterministic where it matters, generative where it counts.
240ms
p95 latency
99.99%
uptime SLA
17
native languages
clickripple · engine · prod
Agents
12 live- fraud-sentinel
- doc-extractor
- code-reviewer
- renewal-copilot
- policy-guard
Deployment
build100%
eval88%
canary34%
consoletrace · fraud-sentinel · req_9f42a218ms
$clickripple›engine start --tenant=acme --region=us-east
$
- 12msingesttransaction · tenant=acme
- 44msretrieve24 prior events · vector store
- 88msreasoncr-reason-1 · 3 hypotheses
- 132mstoolsql › risk_score(user_882)
- 196msverifypolicy-guard · SOC2 pass
- 218msacthold · notify reviewer
Model orchestration
cr-reason-170B MoE
62%
cr-vision-222B
24%
cr-embed-41.2B
14%
Live · last 60s
Requests / min+3.1%
12,482
Tool ok
99.4%
Denies
47
ONLINEus-east · gpu:H100 ×48
build 2026.7.03 · commit a1f4c9
Manifesto · No. 01
Enterprise intelligence isn't a collection of models. It's one operating system.
Model Catalog
A tuned family of models, not a bag of APIs.
Every model ships with the same evaluation harness, safety layer and observability contract. Swap sizes without swapping teams.
Model
Family
Params
p95
Traffic · 60m
The engine, in view
One console.
Every layer of the stack.
clickripple.engine · workspace
HEALTHYus-east
Overview
Models
Agents
Traces
Deploy
Policy
Models12
Agents34
Traces218k
Tools58
PolicyOK
Fleet48
Requests / min
12,482
+3.1%
Avg reasoning depth
4.2
steady
Tool success
99.4%
+0.2%
Reasoning throughput · 24h
tenant=acme
Active trace
- 12ms ingest
- 44ms retrieve · 24 events
- 88ms reason · 3 hypotheses
- 132ms tool · sql
- 218ms act · hold
Deployment · canary
cr-reason-1 @ v1434%
cr-vision-2 @ v968%
cr-guard-1 @ v3100%
Inspector · cr-reason-1
archMoE · 12 experts
ctx128k tokens
p95240ms
safetySOC2 · HIPAA
regionus-east
build2026.7.03
Health
engine · v2026.7.03 · a1f4c9latency 218ms · queue 4
Capabilities
Every primitive an AI team needs.
Multi-agent orchestration
Planners, workers and critics coordinated through a typed message bus.
Reasoning graphs
Deterministic control flow with generative steps. Auditable end-to-end.
Multimodal understanding
Text, tables, PDFs, screens and vision unified in one embedding space.
Fine-tuning pipeline
LoRA, DPO and RLAIF loops with drift detection built in.
Safety layer
PII redaction, jailbreak detection, policy enforcement at every hop.
Tool-use SDK
Give any agent typed access to your APIs, databases and business logic.
Manifesto · No. 02
A benchmark is a claim.
Production is the proof.
Benchmarks
Measured against the frontier.
Internal evals run nightly against public and proprietary benchmarks. Numbers you can hold us to.
Reasoning (MMLU-Pro)78.4 vs 71.2
Code (HumanEval+)84.1 vs 76.9
Multimodal (MMMU)62.8 vs 55.4
Long-context recall92.0 vs 81.3
Tool-use accuracy88.6 vs 73.1
Ecosystem
Wired into the tools your teams already run.
Data
- Snowflake
- Databricks
- BigQuery
- Postgres
- Kafka
- Elastic
Cloud
- AWS
- GCP
- Azure
- Cloudflare
- Vercel
- Fly.io
Ops
- Datadog
- Grafana
- PagerDuty
- Sentry
- OpenTelemetry
- Splunk
Business
- Salesforce
- HubSpot
- SAP
- Workday
- Slack
- Notion
Get Started
Deploy the
reasoning engine.
A 20-minute technical session. Bring an architecture diagram. Leave with a scoped deployment plan.