Public cloud AI creates data exposure.
Sensitive documents leave the customer’s control, or require teams to rely on vendor promises, contracts, and security policies.
Haystac deploys containerized AI inside your data center, private cloud, or hybrid environment — so teams can classify documents, extract data, ask questions, and trigger workflows without sending sensitive content to public AI services.
Banks, insurers, healthcare organizations, government agencies, and BPOs want the benefits of AI. But their most valuable content is also their most sensitive content: claims, medical records, loan files, case records, contracts, policies, and regulated customer data.
Public AI services create exposure. DIY builds create delay. Waiting keeps manual work in place. That is why so many AI initiatives stall before production. The model may be impressive. The deployment model is wrong.
Sensitive documents leave the customer’s control, or require teams to rely on vendor promises, contracts, and security policies.
Building a secure AI runtime, model governance, hardened infrastructure, audit evidence, and compliance alignment from scratch can take 12–24 months before production use.
CISOs, legal teams, compliance leaders, and risk teams do not just ask whether the AI works. They ask where the data goes, who can access it, what gets logged, and how the system can be audited.
Haystac gives regulated enterprises a third path: the control of a private build, with the speed of a product. OmniSuite™ is fully containerized and designed to run inside the customer’s own environment. No external AI APIs. No SaaS dependency. No data egress. The AI operates as an internal, governed software component — not as another external risk surface.
So teams can use modern AI on sensitive enterprise content without moving that content outside the boundary they already trust.
Run on-premises, in a customer-managed private cloud, or across a hybrid architecture.
Documents, records, prompts, and outputs stay inside the customer-controlled environment.
Security, compliance, audit, and operational controls fit the way the enterprise already approves software.
Deploy a hardened AI stack without spending 12–24 months building the runtime from scratch.
Containerized AI is not just a packaging choice. It is the deployment model that lets regulated organizations use AI without expanding their risk boundary.
Haystac runs inside on-premises environments, customer-managed VPCs, sovereign cloud environments, or hybrid architectures.
Chicago, Nashville, Orion, and Polaris operate as containerized services with hardened defaults and controlled communication patterns.
The system is designed with no internet connectivity, no outbound data flow, no sensitive content logging, and in-memory processing controls.
Outputs can flow into ECM, case management, workflow, CRM, ERP, analytics, and downstream review systems through controlled integrations.
Automatically separate and route claims, applications, medical records, case files, contracts, and regulated correspondence inside your own environment.
Pull fields, tables, entities, clauses, codes, signatures, and relationships from documents without exposing the source content to external AI services.
Use retrieval-grounded AI against internal policies, procedures, records, and documents while keeping prompts and answers inside your infrastructure.
Compare documents against rules, requirements, eligibility criteria, coverage policies, or compliance standards with source-backed evidence.
Route clean cases forward, escalate exceptions, generate summaries, notify reviewers, or update downstream systems.
Keep source references, confidence signals, operational logs, and workflow records inside the customer’s governance boundary.
For many organizations, public AI is convenient. For regulated enterprises, it can be impossible. The issue is not only whether the vendor says the data is protected. The issue is whether the organization can prove where sensitive content went, how it was processed, what was logged, who could access it, and whether the output can be defended. That is a hard conversation with cloud AI. It is a simpler conversation when AI runs inside the environment the enterprise already governs.
Building a secure, compliant AI environment from scratch is not just a model deployment. It means designing the runtime, hardening the framework, managing model governance, aligning to NIST and FedRAMP expectations, building MLOps controls, preparing audit evidence, and maintaining the environment over time. That is an infrastructure program before it is an AI program. Haystac gives teams the private deployment model without forcing them to build the whole AI platform themselves.
Process loan files, KYC packets, trade documents, regulatory records, and internal procedures inside customer-controlled infrastructure.
Classify claim packets, extract facts, identify exceptions, reason over policy language, and route cases without exposing PII.
Read clinical records, prior authorizations, lab results, payer rules, and treatment documentation within controlled environments.
Process applications, permits, eligibility files, FOIA records, and compliance documents with data containment and auditability.
Run high-volume document processing for customers with strict data handling, isolation, and deployment requirements.
Let employees ask questions against policies, procedures, records, and repositories without sending prompts or documents to external AI services.
The deployment model matches how regulated enterprises already approve internal software: controlled infrastructure, defined boundaries, documented controls.
Sensitive content stays inside the customer’s environment instead of moving to external AI services.
Teams avoid the long build cycle required to create a secure AI runtime from scratch.
The organization controls where models run, what content they access, what they produce, and how results are logged.
NIST 800-53 alignment, FedRAMP-ready architecture, signed artifacts, hardened containers, and audit-ready controls reduce review burden.
Once AI runs inside the enterprise boundary, teams can safely expand from document processing to reasoning, validation, and workflow execution.
Containerized AI is the deployment foundation for OmniSuite™. Each module can operate inside the customer’s environment, connected through controlled services and APIs.
Separates and routes documents without moving sensitive intake streams outside the customer environment.
Turns documents into structured data while keeping source content, model training, and validation under customer control.
Answers questions using retrieval-grounded enterprise content without sending prompts or documents to public AI services.
Coordinates tools, systems, workflows, and human review with full auditability and control.
Classify, extract, reason, and act — without moving the work outside your environment.
Haystac runs inside the customer’s data center. Recommended for organizations with the strictest data residency, sovereignty, and compliance requirements.
Haystac runs inside a customer-managed VPC on AWS, Azure, or GCP. Dedicated environment. No shared SaaS tenancy.
Sensitive intake on-prem. Elastic reasoning in private cloud. Classification and extraction stay close to data; reasoning uses cloud GPU where appropriate.
Haystac’s containerized AI framework is designed to reduce the amount of custom security work customers need to do before production. The controls are part of the deployment architecture, not optional services added later.
No inbound or outbound access by default. Network access is explicitly granted only where required.
Containers can operate without external service dependencies, keeping data away from public AI services.
Container images are cryptographically signed and verified to reduce supply chain risk.
Non-root execution, read-only file systems, minimal base images, and reduced attack surface.
Sensitive content can be processed without persistent storage exposure.
Operational logs capture events without recording regulated document content or sensitive prompts.
Boundary protection, information flow enforcement, vulnerability management, and software integrity controls are built into the architecture.
Designed to reduce the compliance burden for government and highly regulated environments.
“Haystac does not ask regulated enterprises to choose between control and speed.”
It means Haystac’s AI services are packaged and deployed as controlled containers that run inside the customer’s own infrastructure, rather than as an external SaaS AI service.
No. Haystac is designed so documents, prompts, outputs, and model operations stay inside the customer-controlled environment.
No. Haystac supports on-premises, customer-managed private cloud, and hybrid deployment models.
Cloud AI requires sensitive content to move to an external provider or external service boundary. Haystac brings the AI stack to the content instead.
A DIY build requires teams to create secure runtime architecture, model governance, compliance alignment, hardening, MLOps, and audit evidence from scratch. Haystac provides the hardened AI platform as a deployable product.
Yes. OmniSuite™ is designed to work with ECM, case management, workflow, CRM, ERP, and other enterprise systems through controlled APIs and deployment patterns.
Yes. The architecture is designed around data containment, NIST 800-53 aligned controls, FedRAMP-ready expectations, hardened containers, and audit-ready operations.
Haystac brings document intelligence, grounded reasoning, and governed workflow automation into the infrastructure you already control.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.