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.
Private Runtime
Dedicated model inference in your selected server, VPC or private cloud.
Governed Data Access
Identity, permissions, policies and private knowledge retrieval.
Operational Control
Versioning, evaluation, monitoring, capacity and audit visibility.
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
Enterprise Identity
SSO · Roles · API Access
Model Gateway
Routing · Rate Limits · Policies
Private Model
Private RAG
Business APIs
Monitoring
Data Boundary
Controlled
Inference
Private Runtime
Operations
Monitored
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.
Uncontrolled Adoption
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.
ENIGMA Private AI Architecture
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.
The strongest business case usually combines sensitive information, repeatable AI workloads, integration needs and a clear requirement for ownership or deployment control.
Keep selected prompts, documents, records and model interactions inside an approved environment.
Outcome: Designed data control
Connect AI with private databases, CRM, ERP, document systems and internal workflows.
Outcome: AI inside real operations
Define who can use models, which data can be retrieved, what gets logged and when actions escalate.
Outcome: Governed usage
Design infrastructure around workload location, response expectations and service continuity.
Outcome: Predictable operating design
Select model size, language support, context, quantization, adapters and routing based on the use case.
Outcome: Fit-for-purpose AI
Evaluate infrastructure ownership against token cost, concurrency, model size and long-term workload volume.
Outcome: Transparent cost model
Every request moves through identity, policy, data and monitoring layers before it reaches the model or triggers a business action.
Web, mobile, portals, agents, APIs and internal tools.
Authentication, routing, rate limits and request policies.
Roles, permissions, data filters, logging and approvals.
Selected LLMs, embeddings, classifiers and model routing.
RAG, databases, CRM, ERP, documents and internal APIs.
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
ENIGMA evaluates model size, usage volume, latency, integrations, data classification, GPU requirements and internal capabilities before recommending a deployment pattern.
Model runtime and selected data services deployed on infrastructure controlled by your organization.
check_circleLocal infrastructure boundary
check_circleDedicated access controls
check_circleInternal network integration
Private networks, dedicated compute and controlled service access inside your cloud environment.
check_circlePrivate network architecture
check_circleIdentity and secret management
check_circleCloud monitoring and scaling
Dedicated GPU compute sized for defined models, concurrency, context and latency requirements.
check_circleKnown compute capacity
check_circleContainerized model services
check_circlePrivate API endpoint
Route workloads by sensitivity, capability, cost or availability through a governed model gateway.
check_circlePolicy-based model routing
check_circleSensitive workload isolation
check_circleControlled fallback options
Use cases are selected by business value, data sensitivity, model capability, operational risk and integration complexity—not by adding AI everywhere.
Help authorized teams search policies, procedures, projects, manuals and business records without exposing the same information to every user.
Permission-aware knowledge retrieval
Internal portal, mobile and chat integration
Citations, feedback and escalation
Use private models and retrieval for contracts, proposals, forms, invoices, reports, technical files and controlled document workflows.
Document classification and extraction
Comparison and exception identification
Approval and system-update workflows
Ground responses in products, tickets, service history and approved policies while maintaining channel and customer access rules.
Agent assist and response drafting
Ticket summary and recommended next action
Sensitive case routing and escalation
Connect approved repositories, architecture documentation, deployment guides and support records inside a controlled engineering environment.
Repository and documentation retrieval
Code explanation and review assistance
Incident and deployment knowledge support
Use a private model gateway to classify requests, retrieve context, propose actions and execute approved workflows through enterprise APIs.
Policy-based tool and API access
Human approval for sensitive actions
Action history and exception monitoring
Expose governed inference through internal APIs for CRM, ERP, dashboards, mobile apps, industrial systems and customer platforms.
Private API and SDK integration
Usage, tenant and role controls
Capacity and model routing design
Model selection must balance task quality, languages, context length, latency, memory, GPU availability, concurrency, licensing and operational cost.
Define real questions, documents, actions, quality criteria and unacceptable failure modes.
Compare suitable models against accuracy, speed, memory, context, language and licensing requirements.
Use the least complex customization approach that reliably solves the business problem.
Maintain model versions, evaluations, rollback and migration paths as models and workloads evolve.
Reasoning, language, context, tools and domain suitability.
GPU memory, quantization, concurrency and throughput.
Interactive response, batch workloads and service continuity.
Commercial rights, restrictions, support and distribution.
Business vocabulary, multilingual use and specialist knowledge.
Infrastructure, support, scaling, power and workload cost.
ENIGMA designs controls around the full service lifecycle—from user authentication and data access to model updates, capacity, logging and incident handling.
SSO, roles, tenants, API credentials, data filters and tool permissions.
Private endpoints, firewall policy, TLS, secrets, service isolation and restricted administration.
Test datasets, output checks, allowed tools, confidence handling and human review.
Health, latency, capacity, versions, updates, rollback, backup and incident visibility.
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.
Authenticated Engineer
Identity and project access verified
Private Retrieval
Approved architecture, logs and incident knowledge
Self-Hosted Model
Grounded response inside the private boundary
Each engagement begins with workload and risk discovery and ends with a monitored architecture, documented operating model and controlled production rollout.
Use cases, data boundaries, model strategy, infrastructure, identity, integrations and responsibilities.
Selected models, model gateway, containers, APIs, capacity design and private endpoints.
RAG, databases, CRM, ERP, documents, portals, mobile apps and workflow APIs.
Evaluation, access controls, monitoring, logging, versions, updates, rollback and operational runbooks.
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
Review users, workloads, data, sensitivity, integrations, quality expectations and failure risks.
Benchmark suitable models and define compute, storage, network, availability and operating cost.
Define identity, policy, secrets, data access, logging, approvals, evaluation and ownership.
Deploy model services, gateway, private RAG, enterprise APIs, applications and monitoring.
Test task quality, access boundaries, latency, capacity, edge cases and operational runbooks.
Launch with monitoring, support, model versions, security updates, capacity review and controlled expansion.
The correct answer depends on data sensitivity, workload size, languages, model capability, GPU availability, integrations, support expectations and who will operate the platform.
We’ll assess your use cases, data sensitivity, infrastructure, model options, private RAG requirements and operating responsibilities before recommending the right deployment architecture.
