No data exposure
Company data and customer PII must never leak to third-party LLM vendors. On-prem / private cloud, 9 AI security guards, AES-256 encryption with per-group keys keep your data inside your perimeter.
Enterprise AX, Now
Enterprise AX how long will you keep drawing it yourself?
Stop burning budget and quarters wiring workflows and validating knowledge by hand.
Building it yourself on an open-source workflow stack means hundreds of thousands in infrastructure, ops, and security and six months gone before launch. What enterprises actually need is to run a global big-tech-grade AI product inside their own walls, immediately, without leaking data and without locking into one vendor.
What enterprises actually want
Standing up an open-source AI workflow builder, a Graph RAG stack, and an SI engagement to wire it all together is a different path than adopting an off-the-shelf Enterprise Agent OS.
No vendor lock-in
Locked into one LLM or one cloud, you lose negotiation leverage and scalability. A multi-LLM router lets you swap models per stage and run the same product across SaaS, on-prem, or private cloud.
Working from day one
Drawing workflows is not the goal: business outcomes are. With 166+ built-in tools and validated pipeline templates, your first PoC week already shows agents doing real work.
Build it yourself vs AICLUDE
Pick up just a workflow builder and you'll end up engineering the infra, security, and operations on your own.
| Item | DIY (open source + self-run) | AICLUDE Agent OS |
|---|---|---|
| Time to launch | 3–6 months: infra, auth, security from scratch | 1–4 weeks: automating work day one |
| LLM strategy | Locked to one LLM, refactor on every swap | Multi-LLM router · per-stage model choice |
| Data exposure | PII and secrets risk leaking to external LLM APIs | On-prem / private + 9 AI security guards |
| Knowledge integrity | Hand-tuned RAG, expensive validation cycle | Knowledge Hub · auto-learning · source tracking |
| Operational load | Run vector DB, auth, audit logs yourself | Unified ops console · multi-tenant · billing |
Time to launch
DIY (open source + self-run)
3–6 months: infra, auth, security from scratch
AICLUDE Agent OS
1–4 weeks: automating work day one
LLM strategy
DIY (open source + self-run)
Locked to one LLM, refactor on every swap
AICLUDE Agent OS
Multi-LLM router · per-stage model choice
Data exposure
DIY (open source + self-run)
PII and secrets risk leaking to external LLM APIs
AICLUDE Agent OS
On-prem / private + 9 AI security guards
Knowledge integrity
DIY (open source + self-run)
Hand-tuned RAG, expensive validation cycle
AICLUDE Agent OS
Knowledge Hub · auto-learning · source tracking
Operational load
DIY (open source + self-run)
Run vector DB, auth, audit logs yourself
AICLUDE Agent OS
Unified ops console · multi-tenant · billing
Why AICLUDE
Three Core Differentiators
The positioning pillars that appear on every industry page.
Hybrid Execution
Autonomous LLM execution combined with verified deterministic skills, switched automatically by context.
- Dynamic routing across LLM and skills
- Fortress Pipeline 8-stage verification
- Automatic failover on vendor outage
Secure by Design
8-stage Fortress Pipeline, 9 AI security guards, AES-256-GCM, and physical multi-tenancy.
- 9 AI guards covering OWASP LLM Top 10
- AES-256-GCM with HMAC blind index
- Physical tenant isolation
On-prem Omnichannel
Native web, app, Slack, SNS, API, MCP - plus kiosk, signage, and STTS voice dialog.
- Native kiosk and signage
- Realtime STTS over WebSocket
- Shared agent and RAG context across channels
Industry Grid
Industry Use Cases
Click any card to open the scenarios and cases for that industry.
Start your AX today
A single PoC maps autonomous-execution coverage across your organization.
Scenarios on this page describe applicability based on AICLUDE capability and do not imply any specific adoption or result.