Controlled commercial API
Use contractual data controls, approved regions, identity, model gateways, retention settings, and monitoring when external processing is acceptable.
Private AI Implementation Consulting
Private LLM and Secure RAG Implementation
Deploy language-model and retrieval systems with controlled data paths, permission-aware access, evaluation, audit logging, enterprise integration, and an operating model your team can support.
Architecture Decision
Private means control, not one product
The right architecture depends on data sensitivity, identity, retrieval permissions, latency, capacity, vendor terms, evidence needs, operational skills, and the systems the model must reach.
Implementation Outcome
A governed service with a defined boundary
We connect the model, knowledge sources, retrieval controls, evaluation, logging, human review, integrations, support ownership, and change process into one production design.
Deployment Options
Private LLM deployment options for each control boundary
Dedicated cloud environment
Isolate model endpoints and data services inside a dedicated tenant or account with organization-controlled networking and access.
Self-hosted private cloud
Operate model inference and retrieval services in infrastructure your organization administers, with greater control and greater operating responsibility.
On-premises or isolated
Keep processing inside a local or disconnected environment when the approved boundary, mission, or data restrictions require it.
Applicability to CUI, classified, export-controlled, personal, health, financial, or other regulated data depends on the contract, data flow, system boundary, provider terms, architecture, and implemented controls. A deployment label alone does not establish compliance.
Security and Governance
Control the complete data and action path
Technical controls
Data admission, classification, minimization, retention, and deletion
Identity, least privilege, source permissions, and service authorization
Chunk, embedding, vector store, retrieval, citation, and source controls
Prompt, output, tool action, approval, and system activity logging
Operating controls
Evaluation sets, quality thresholds, red-team scenarios, and release criteria
Human review, overrides, exception queues, and escalation procedures
Performance, cost, drift, access, failure, and security monitoring
Incident response, rollback, recovery, vendor change, and exit planning
Engagement Deliverables
What an implementation engagement can produce
Use case and data boundary
Workflow definition, data classification, system context, risk tier, success measures, and authorization requirements.
Architecture and threat model
Deployment decision, trust boundaries, data flows, component choices, integrations, misuse paths, and control responsibilities.
Retrieval and evaluation design
Source onboarding, permissions, indexing, retrieval behavior, citations, test sets, metrics, and acceptance thresholds.
Working pilot or production increment
Configured infrastructure, application and pipeline code, observability, interfaces, validation, and operating documentation.
Governance and runbooks
Approved-use statement, ownership matrix, review rules, logging, incident escalation, change control, recovery, and support procedures.
Production decision package
Test evidence, known limitations, residual risks, operating cost drivers, rollout recommendation, and prioritized next work.
Cost Drivers
What changes private LLM and secure RAG cost
Build and infrastructure
Model size and usage, latency, compute capacity, environment isolation, source volume, connectors, indexing, evaluation, integration, and availability requirements shape initial cost.
Operations and assurance
Engineering ownership, security controls, monitoring, incident response, content updates, model changes, recurring evaluation, user support, audit evidence, and exit requirements shape lifetime cost.
Relevant Experience
A private model connected to an operational pipeline
GS Consulting built a locally hosted AI workflow that prepared host and operational data, applied repeatable cyber analysis instructions, produced validated structured JSON, and moved approved results into a SIEM or database. Orchestration, status, exception visibility, and human review surrounded the model so the result functioned as an operational system.
Proven Pattern
Raw data → private analysis → structured result → operational system → human review
Frequently Asked Questions
Private LLM and secure RAG implementation questions
What is a private LLM deployment?
It is a model service operated within an approved technical and contractual boundary, with defined control over data paths, access, retention, monitoring, model changes, and operations.
What makes RAG secure?
Secure RAG preserves source permissions during ingestion and retrieval, restricts indexes and embeddings, controls citations and output, logs activity, and prevents the model from becoming a path around access rules.
Do we need to host the model ourselves?
Not always. Controlled APIs and dedicated cloud services may fit some workflows. Self-hosting is justified when the additional boundary control is worth the infrastructure and operations your team must own.
How long does implementation take?
Timing depends on the workflow, data approval, integrations, deployment model, evaluation evidence, security review, and production requirements. A bounded pilot should be scoped separately from production authorization.
How is access to retrieved content enforced?
The preferred design carries user or workload identity into retrieval and filters sources using authoritative permissions before context reaches the model.
What should be monitored in production?
Monitor access, source coverage, retrieval quality, output quality, exceptions, model and prompt versions, tool actions, latency, cost, failures, incidents, human overrides, and changes to the approved boundary.
Implementation Guides
Research the architecture before you commit
Topic HubPrivate LLM and Secure RAG Guides
Navigate deployment, security, retrieval, cost, evaluation, and operating guidance.
DeploymentPrivate LLM Deployment OptionsCompare the main architecture lanes by control and operating burden.
SecurityPrivate LLM Security ControlsProtect the complete model, data, retrieval, identity, and action path.
RAG DesignSecure RAG Design PatternsMatch shared, separated, delegated, hybrid, and isolated retrieval patterns to access needs.
Customer Feedback
What Customers Have Noticed
Huge shout out to you for transforming this project from a theoretical discussion into a proof of concept and beyond in such a short timeframe.
This success would not have been possible without your outstanding contributions.
We've benefited thanks to your skillset and dedication.
Architecture Review
Define the private AI boundary before selecting the stack
Bring one workflow, its data types, user groups, required systems, and operating constraints. GS Consulting can help turn them into an implementation decision.