Enterprise Identity
SSO · Roles · API Access
Private AI · Self-Hosted Models · Controlled Inference
Run AI inside an infrastructure boundary your business controls.
ENIGMA engineers self-hosted and privacy-controlled AI systems that keep model access, company knowledge, integrations, logging and operational policies within an architecture designed around your security, performance and ownership requirements.
R
Private Runtime
Dedicated model inference in your selected server, VPC or private cloud.
D
Governed Data Access
Identity, permissions, policies and private knowledge retrieval.
O
Operational Control
Versioning, evaluation, monitoring, capacity and audit visibility.
i
Private AI is not only a model installation. It requires a controlled runtime, identity and policy enforcement, private data connections, evaluation, monitoring, updates and a clear operating responsibility model.
Private AI Control Plane
Self-Hosted Inference & Policy Boundary
Every request is identified, governed, routed and observed inside the selected private architecture.
Model Gateway
Routing · Rate Limits · Policies
AI
Private Model
Inference active
Private RAG
Approved company knowledge
Business APIs
Controlled system actions
Monitoring
Health, logs and capacity
Data Boundary
ControlledInference
Private RuntimeOperations
Monitored
Live request path
Identity verified→
Policy applied→
Context retrieved→
Inference monitored
memorySelf-Hosted Inference
location_onData Residency Control
shield_personIdentity & Policy Layer
databasePrivate RAG & Integrations
monitor_heartMonitoring & Model Operations
Private AI by ENIGMA
What a governed private AI platform needs to control
Runtime: model services, containers, gateways, APIs, compute capacity and private endpoints.
Data access: identity, roles, private RAG, documents, databases, CRM, ERP and internal APIs.
Governance: request policies, allowed tools, evaluation, logging, access boundaries and human review.
Operations: health, latency, capacity, versions, updates, rollback, backups and controlled model replacement.
Beyond Public AI Access
Using AI privately requires control over the model, the data path and the operating environment.
The real decision is not simply public model versus open-source model. It is which architecture gives your organization the right balance of privacy, performance, cost, flexibility and operational responsibility.
public
Uncontrolled Adoption
Employees use disconnected AI tools without a defined business boundary.
closeSensitive information may enter unapproved tools or accounts.
closeModel access, prompts, outputs and usage are difficult to govern.
closeBusiness integrations and role permissions remain fragmented.
closeVendor, latency, availability and cost dependencies are not designed.
shield_lock
ENIGMA Private AI Architecture
AI is delivered through a governed platform your organization can operate.
check_circleSelected models run inside an approved infrastructure boundary.
check_circleIdentity, policy, logging and data access are centrally controlled.
check_circlePrivate RAG and APIs connect approved company systems.
check_circleEvaluation, versioning, monitoring and rollback support operations.
Why Organizations Choose Private AI
Private AI becomes valuable when control is part of the business requirement—not an optional feature.
The strongest business case usually combines sensitive information, repeatable AI workloads, integration needs and a clear requirement for ownership or deployment control.
encrypted
Sensitive Data Boundaries
Keep selected prompts, documents, records and model interactions inside an approved environment.
Outcome: Designed data control
account_tree
Enterprise Integration
Connect AI with private databases, CRM, ERP, document systems and internal workflows.
Outcome: AI inside real operations
gavel
Policy & Audit Requirements
Define who can use models, which data can be retrieved, what gets logged and when actions escalate.
Outcome: Governed usage
speed
Latency & Availability Control
Design infrastructure around workload location, response expectations and service continuity.
Outcome: Predictable operating design
tune
Model Choice & Customization
Select model size, language support, context, quantization, adapters and routing based on the use case.
Outcome: Fit-for-purpose AI
payments
Usage Economics
Evaluate infrastructure ownership against token cost, concurrency, model size and long-term workload volume.
Outcome: Transparent cost model
Private AI System Architecture
A controlled path from enterprise applications to private model inference.
Every request moves through identity, policy, data and monitoring layers before it reaches the model or triggers a business action.
devices
Enterprise Applications
Web, mobile, portals, agents, APIs and internal tools.
api
Private AI Gateway
Authentication, routing, rate limits and request policies.
shield_person
Identity & Policy
Roles, permissions, data filters, logging and approvals.
memory
Self-Hosted Model Runtime
Selected LLMs, embeddings, classifiers and model routing.
database
Private Knowledge & Systems
RAG, databases, CRM, ERP, documents and internal APIs.
monitor_heart
Evaluation & Monitoring
Quality, latency, capacity, audit records, versions and alerts.
Model Strategy
Selected by workload
Data Boundary
Defined by architecture
Human Control
Approval where required
Operations
Measured and monitored
Deployment Models
The right private AI boundary depends on your workload, infrastructure and operating responsibility.
ENIGMA evaluates model size, usage volume, latency, integrations, data classification, GPU requirements and internal capabilities before recommending a deployment pattern.
Your Data Centre or Office Infrastructure
Model runtime and selected data services deployed on infrastructure controlled by your organization.
✓Local infrastructure boundary
✓Dedicated access controls
✓Internal network integration
Cloud Isolation with Enterprise Controls
Private networks, dedicated compute and controlled service access inside your cloud environment.
✓Private network architecture
✓Identity and secret management
✓Cloud monitoring and scaling
Purpose-Built Model Server
Dedicated GPU compute sized for defined models, concurrency, context and latency requirements.
✓Known compute capacity
✓Containerized model services
✓Private API endpoint
Private Core with Approved External Services
Route workloads by sensitivity, capability, cost or availability through a governed model gateway.
✓Policy-based model routing
✓Sensitive workload isolation
✓Controlled fallback options
Private AI Use Cases
One governed private AI layer can support multiple departments and software products.
Use cases are selected by business value, data sensitivity, model capability, operational risk and integration complexity—not by adding AI everywhere.
EAEmployee AI
Internal workforce intelligence
Private internal knowledge and productivity assistant.
Help authorized teams search policies, procedures, projects, manuals and business records without exposing the same information to every user.
Typical capabilities
✓
Permission-aware knowledge retrieval
✓
Internal portal, mobile and chat integration
✓
Citations, feedback and escalation
DIDocument AI
Document intelligence
Extract, compare and analyse private business documents.
Use private models and retrieval for contracts, proposals, forms, invoices, reports, technical files and controlled document workflows.
Typical capabilities
✓
Document classification and extraction
✓
Comparison and exception identification
✓
Approval and system-update workflows
SOService Operations
Service workflow intelligence
Private customer-support and service copilots.
Ground responses in products, tickets, service history and approved policies while maintaining channel and customer access rules.
Typical capabilities
✓
Agent assist and response drafting
✓
Ticket summary and recommended next action
✓
Sensitive case routing and escalation
EKEngineering Knowledge
Technical knowledge assistance
Private coding, infrastructure and technical assistance.
Connect approved repositories, architecture documentation, deployment guides and support records inside a controlled engineering environment.
Typical capabilities
✓
Repository and documentation retrieval
✓
Code explanation and review assistance
✓
Incident and deployment knowledge support
CAControlled Automation
Governed business actions
Private AI agents connected to business actions.
Use a private model gateway to classify requests, retrieve context, propose actions and execute approved workflows through enterprise APIs.
Typical capabilities
✓
Policy-based tool and API access
✓
Human approval for sensitive actions
✓
Action history and exception monitoring
Embedded enterprise intelligence
Embed private AI capabilities into enterprise software.
Expose governed inference through internal APIs for CRM, ERP, dashboards, mobile apps, industrial systems and customer platforms.
Typical capabilities
✓
Private API and SDK integration
✓
Usage, tenant and role controls
✓
Capacity and model routing design
Model Strategy
The best private AI system is rarely the largest model.
Model selection must balance task quality, languages, context length, latency, memory, GPU availability, concurrency, licensing and operational cost.
Use-Case and Evaluation Design
Define real questions, documents, actions, quality criteria and unacceptable failure modes.
Model Selection and Benchmarking
Compare suitable models against accuracy, speed, memory, context, language and licensing requirements.
RAG, Adapters or Fine-Tuning
Use the least complex customization approach that reliably solves the business problem.
Versioning and Controlled Replacement
Maintain model versions, evaluations, rollback and migration paths as models and workloads evolve.
Model Decision Matrix01 02 03 04 05 06
Six checks before selecting a private model
Model choice should follow the workload—not the other way around.
Base Model Capability
Reasoning, language, context, tools and domain suitability.
Compute Footprint
GPU memory, quantization, concurrency and throughput.
Latency & Availability
Interactive response, batch workloads and service continuity.
Licensing & Ownership
Commercial rights, restrictions, support and distribution.
Language & Domain
Business vocabulary, multilingual use and specialist knowledge.
Operating Economics
Infrastructure, support, scaling, power and workload cost.
Security, Governance & Operations
Private infrastructure only creates value when the AI service is operated responsibly.
ENIGMA designs controls around the full service lifecycle—from user authentication and data access to model updates, capacity, logging and incident handling.
shield_person
Identity & Authorization
SSO, roles, tenants, API credentials, data filters and tool permissions.
encrypted
Network & Secret Controls
Private endpoints, firewall policy, TLS, secrets, service isolation and restricted administration.
fact_check
Evaluation & Guardrails
Test datasets, output checks, allowed tools, confidence handling and human review.
monitor_heart
Model Operations
Health, latency, capacity, versions, updates, rollback, backup and incident visibility.
Illustrative Operating Scenario
A private project intelligence assistant for engineering teams.
An authenticated engineer asks about a production issue. The private gateway verifies the role, retrieves approved project documentation and incident records, sends only the relevant context to the self-hosted model, returns a cited troubleshooting plan and records the interaction for evaluation. Restricted customer or finance data is not retrieved for that role.
Illustrative architecture pattern—not a fabricated client result.
01
Authenticated Engineer
Identity and project access verified
02
Private Retrieval
Approved architecture, logs and incident knowledge
03
Self-Hosted Model
Grounded response inside the private boundary
Enterprise Implementation
Not a one-time model install. A complete private AI operating platform.
Each engagement begins with workload and risk discovery and ends with a monitored architecture, documented operating model and controlled production rollout.
Private AI Architecture Blueprint
Use cases, data boundaries, model strategy, infrastructure, identity, integrations and responsibilities.
Model & Inference Platform
Selected models, model gateway, containers, APIs, capacity design and private endpoints.
Private Data & Application Integration
RAG, databases, CRM, ERP, documents, portals, mobile apps and workflow APIs.
Governance & Model Operations
Evaluation, access controls, monitoring, logging, versions, updates, rollback and operational runbooks.
Best-Fit Organizations
Built for organizations where privacy, integration and AI ownership are real operating requirements.
Private AI is strongest when there is a defined business workload and a reason to control the model or data path. It is not automatically the most efficient choice for every small or occasional AI use case.
Sensitive internal or customer data
Private cloud or server capability
High-volume repeatable AI workloads
CRM, ERP or database integrations
Role-based access and audit needs
Long-term model ownership strategy
Private AI Engineering Process
From workload discovery to governed private AI operations.
Use-Case & Risk Discovery
Review users, workloads, data, sensitivity, integrations, quality expectations and failure risks.
Model & Infrastructure Assessment
Benchmark suitable models and define compute, storage, network, availability and operating cost.
Security & Governance Architecture
Define identity, policy, secrets, data access, logging, approvals, evaluation and ownership.
Platform & Integration Engineering
Deploy model services, gateway, private RAG, enterprise APIs, applications and monitoring.
Evaluation & Controlled Pilot
Test task quality, access boundaries, latency, capacity, edge cases and operational runbooks.
Production & Model Operations
Launch with monitoring, support, model versions, security updates, capacity review and controlled expansion.
Enterprise FAQ
Questions organizations ask before self-hosting AI models.
The correct answer depends on data sensitivity, workload size, languages, model capability, GPU availability, integrations, support expectations and who will operate the platform.
A private AI model is deployed inside an infrastructure boundary selected and governed by the organization, such as an on-premises server, private cloud, VPC or dedicated environment. Access, data flows, logging and integrations are controlled according to the chosen architecture.
It can, when the model, retrieval layer and supporting services are fully self-hosted. Hybrid designs may still use approved external services. The final data boundary depends on the architecture, integrations and operational requirements selected for the project.
Usually not. Most organizations achieve better time, cost and risk outcomes by selecting an appropriate open or commercial model, then using private RAG, prompt controls, adapters, fine-tuning or workflow logic where required.
Yes. It can connect to approved documents, databases, CRM, ERP, internal APIs and knowledge repositories through permission-aware retrieval and controlled integration layers.
Yes, subject to model size, expected usage, latency and hardware capacity. ENIGMA can design deployments for dedicated GPU servers, private cloud instances, on-premises infrastructure or hybrid environments.
The solution can integrate with enterprise identity, roles, departments, API credentials and document-level permissions. Requests are evaluated through policy and access layers before data or model actions are allowed.
A production design can include model and API health checks, latency, capacity, request logging, evaluation datasets, version control, rollback, security updates and controlled model replacement.
Common use cases include internal knowledge assistants, document intelligence, customer-support copilots, private RAG, coding assistants, operational agents, regulated workflows and AI features embedded into enterprise software.
Private AI Architecture Discovery
Your AI strategy should define what stays private, what connects and who remains in control.
We’ll assess your use cases, data sensitivity, infrastructure, model options, private RAG requirements and operating responsibilities before recommending the right deployment architecture.