AI is now core to enterprise systems.
It's embedded in workflows, connected to proprietary data, and driving decisions. The enterprises building with confidence aren't betting on a single model or vendor. They're building on foundations they control.
Why Enterprise AI stalls
Pilots succeed, but production fails
Working demos break down against real data, real users, and real infrastructure constraints.
Vendor lock-in limits your options
Rigid platforms force architectural decisions you'll be living with for years.
The AI landscape changes faster than your architecture can
New models, new providers, new pricing — every shift forces a rebuild of what you already built.
Control is a business requirement
Build prototypes that become production systems
The same pipeline you test with is the one you deploy. No rebuilding, no framework migration.
Choose the best model, provider, and deployment for every use case
Match the architecture to the problem, not the other way around.
Swap components without rewriting your application
Models, vector stores, and providers are interchangeable parts, not foundational commitments.
Haystack Enterprise Platform puts you in control
Design AI systems your business can rely on
Engineer accuracy into every layer: the context, the model, and the tools to understand what's happening at every step.
Engineer the right context
Define what information, tools, and memory your AI accesses – from RAG pipelines to multi-agent systems reasoning across sources.
Match models to the work
Small models for simple tasks, frontier models for complex reasoning.
See what works, debug what doesn't
Tools to test pipelines, compare prompts and retrieval strategies, and refine before you scale.
Build agents that work in production, not just demos
Agents that reason, act, and recover reliably under real-world conditions.
Orchestrate multi-agent workflows
Define how agents use context, tools, and memory to complete complex tasks, with explicit reasoning patterns, tool invocation logic, and escalation paths.
Standardize across teams
Build and operate AI applications on a shared platform with reusable components and templates.
Ship with confidence
Version pipelines, prompts, and models so teams can reproduce behavior, compare changes, and roll back when something breaks.
Own your stack
Your infrastructure, your models, your foundation – built to stay portable with the freedom to change.
Choose where your AI runs
Deploy on managed cloud, self-hosted, or hybrid – with serverless execution that scales automatically.
Stay model and provider agnostic
Swap between open-source and closed models without code changes.
Build on an open-source foundation
No proprietary abstractions, no black boxes, no long-term dependency.
Govern everything your AI does
Full visibility from input to action, with the controls to enforce policy at runtime.
Trace every decision
Track every query, answer, action, and context source in a unified run history.
Enforce policy across every pipeline
Add guardrails – content filtering, PII detection, output validation – and enforce role-based access across users and teams.
Monitor performance and cost
See latency, quality, and resource usage, then optimize your pipelines accordingly.
Enterprise-grade security and compliance
Built for financial services, healthcare, and public sector organizations with the strictest security and compliance requirements.
Compliance
SOC 2 Type II certified
ISO 27001 certified
GDPR compliant
HIPAA compliant
Platform controls
Role-based access control (RBAC)
Audit logs and full traceability
Guardrails and policy enforcement
Data boundaries and pipeline isolation
Single sign-on (SSO) support
Our Partner Ecosytem Driving Sovereign AI
We work with technology and delivery partners around the world to help organizations adopt, deploy, and scale AI they control.
Get sovereign AI into production with the engineers who build it.
Forward Deployed Engineers
Our AI engineers work directly with your team to design, build, and deploy production agents on your infrastructure. From context engineering and agent architecture to performance optimization and team enablement, they help you move faster without handing the hard parts to a black box.
They don’t just advise. They build.
Strategy and architecture
Define the right use cases, map them to the right architecture, and create a path from first prototype to production deployment.
Implementation and deployment
Hands-on engineering support to build, integrate, and deploy agents and AI systems that meet your security, data, and infrastructure requirements.
Optimization and enablement
Analyze usage, improve performance, and upskill your team to build and govern AI systems independently.
Frequently asked questions
What is the difference between an AI pilot and a production AI system?
An AI pilot is a proof of concept that shows something is possible, while a production AI system runs reliably for real users at scale with the retrieval quality, evaluation, governance, and observability needed to be trusted with real work. Most enterprise AI initiatives stall in this gap because the pilot lacked grounded, verifiable answers, access controls, audit trails, or a way to monitor and improve behavior once real traffic and edge cases arrived. In regulated settings the gap is wider still, because a wrong answer can be a compliance violation rather than just a poor experience, so source citation, output logging, and runtime guardrails become prerequisites rather than nice-to-haves. Closing that gap is less about a better model than about engineering the system around it.
What is the difference between RAG and AI agents in the enterprise?
RAG grounds a model's answers in an organization’s data and documents to reduce hallucination, ideal for search and question answering. Agents go further: they reason over multiple steps and call tools to act, suited to tasks where you can't predict the exact sequence. Many enterprise systems combine both. Haystack Enterprise Platform supports RAG, agents, and hybrids as composable pipelines you can inspect and govern.
How do you govern AI across many teams?
AI governance can be achieved through role-based access control, audit logs, and data lineage applied consistently, so you can see who can build what, what changed, and why any answer was produced. Governance has to run at the system level, since policy in a document doesn't stop a developer from calling a model directly. Centralizing it also lets innovation move faster safely. Haystack Enterprise Platform builds these controls into the pipeline itself.
What is Enterprise AI
Enterprise AI is AI built for real business environments: private data, complex workflows, security requirements, governance needs, and production scale.
Unlike consumer AI tools, enterprise AI needs to work with company knowledge, follow business rules, respect access controls, and operate reliably across teams and systems. With the Haystack Enterprise Platform, organizations can build enterprise AI agents that are grounded in their own data, controlled by their own architecture, and ready to run in production.
What is Haystack Enterprise Platform?
The Haystack Enterprise Platform is a platform for building production-ready AI applications and managing them across the full lifecycle, from prototyping to large-scale production. It gives teams everything needed to work with their data, choose their models, build and evaluate pipelines, share prototypes with users, and run and monitor apps in production. It's the operational layer built on top of open-source Haystack, adding a visual Pipeline Builder, evaluation tooling, governance, observability, and deployment.
The Haystack Enterprise Platform supports modular AI architectures including agents, RAG, intelligent document processing (IDP), semantic search, text-to-SQL, and more. Organizations use the Haystack Enterprise Platform to accelerate development, lower costs, control governance, and access expert enterprise support - empowering them to innovate faster and stay ahead in the rapidly evolving AI landscape.
The Haystack Enterprise Platform was formerly known as deepset AI platform.
