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Meet the AI platform enterprises run on

A unified platform for deploying, operating, and optimizing AI agents at scale.

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Automate outcomes across every agent, system, and channel.

Unify orchestration, models, retrieval, integrations, and governance in a single runtime built to deploy, operate, and optimize AI at scale.

Orchestrate AI Agents into one goal-driven system

Druid Conductor coordinates Knowledge Agents, Process Agents, AI assistants, and business logic so that every moving part works as one.


Run a complete enterprise AI stack

Combine NLU, generative AI, knowledge retrieval, agentic workflows, analytics, and observability with no stitching required.


Integrate your full enterprise ecosystem without replatforming

Connect every system and data source securely and at scale with prebuilt integrations, open APIs, and event-driven triggers that go live in days without disrupting existing infrastructure.

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Enterprise-grade from the ground up

Permissions, auditability, explainability, and deployment flexibility built into the platform and not layered on after.

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Embed governance and explainability by design

Role-based permissions, audit trails, data minimization, and explainable AI that are built into the platform architecture and not added after deployment.

Monitor AI performance in production

See how agents are performing, how they are reasoning, and where improvement is needed across your enterprise.

Deploy on your terms

Run in cloud, hybrid, or fully on-premises with air-gapped Druid Becus LLM. Same codebase, same security controls, every topology. Your data stays where your policy requires it.

Scale operations without loosing control

Support high-volume, sensitive workflows at enterprise scale without compromising compliance posture, context integrity, or escalation paths.

Meet your AI Agents

AI Agents built for real enterprise work.

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AI Agent Orchestration

Drive enterprise AI execution at scale.

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Agent Builder

Build and ship enterprise AI agents. From idea to production in hours, not months.

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AI Voice

Voice AI that works like your best agent every time.

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Data Analytics & Insights

See everything your AI does. Understand why it works.

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AI Governance

AI you can explain. AI you can trust.

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Frequently asked questions

Get answers to the most common questions about Druid's AI agents and the platform's capabilities before your demo.

What components make up the Druid AI Platform stack?

The platform combines an NLU/NLP engine, generative AI layer (multi-model support), graph RAG-backed knowledge retrieval with embeddings and entity data model, integration module, the Conductor agentic AI orchestration framework, natural language, visual and pro-code Agent Builder, AI Workspaces with natural language data query and enterprise systems update, omnichannel runtime, analytics and observability dashboards, and a multi-layer security architecture, all running on a containerized Kubernetes infrastructure.

What are AI Workspaces?

Interactive, agent-assisted mini-applications that combine conversational AI with actionable data views. Users see, query, and act on live data from CRM, ITSM, ERP, and HRIS systems through visual widgets or natural language including a two-way sync pushing updates back to source systems in real time.

How does the platform handle model lifecycle management?

Teams can deploy, A/B test, and swap between Azure OpenAI, Anthropic Claude, Google models, Druid Becus, or custom fine-tuned models without rebuilding agents. Model performance is tracked through the evaluation engine with precision, recall, and confidence scoring, and the QA AI Agent validates flows against each model change.

What is the multi-layer security architecture?

Five defense layers: (1) Infrastructure: hardened Kubernetes, TLS 1.2+, AES-256, BYOK; (2) Access Control: zero-trust RBAC, SSO/AD, MFA; (3) Compliance: SOC 2 Type II, ISO 27001, GDPR, HIPAA, NHS embedded in design; (4) Privacy: PII masking, tokenization, consent management, data residency; (5) Audit: complete conversation logging, decision tracing, SIEM integration, anomaly detection.

What edge deployment capabilities exist?

Druid supports fully air-gapped edge deployments using the same containerized Kubernetes architecture. The Becus LLM, knowledge base, orchestration engine, and agent components run locally (e.g., branch locations, regulated environments, isolated networks). Edge deployments support auto-scaling, HA, and the same security controls as cloud, with conversation data stored and encrypted on customer-controlled infrastructure.

What is the Kubernetes-based deployment architecture?

Druid supports fully air-gapped edge deployments using the same containerized Kubernetes architecture. The Becus LLM, knowledge base, orchestration engine, and agent components run locally (e.g., branch locations, regulated environments, isolated networks). Edge deployments support auto-scaling, HA, and the same security controls as cloud, with conversation data stored and encrypted on customer-controlled infrastructure.

Connect what matters. Make work feel effortless.

See how proven AI agents work for you

Inside real systems, in real scenarios, with accuracy, reliability, and control. So your work feels simpler, not harder.