Option A
If you share your data
- Compliance exposure
- IP and trade-secret risk
- Data residency gaps
- Legal liability
The third path to enterprise AI
Your Data. Your Model. Your Hardware. Your Boundary.
Private AI in 90 days. A fully validated, compliance-ready on-premise AI platform running in your data center or on dedicated NVIDIA hardware.
- 01 Your Data Stays inside your estate
- 02 Your Model Fine-tuned on your data
- 03 Your Hardware Your DC, a local vendor or new NVIDIA
- 04 Your Boundary Frontier models only by policy
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100%
Data sovereignty
Zero egress to public frontier clouds for sensitive IP.
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90 days
Production target
From hardware burn-in to live corporate endpoints.
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DPDPA · EU AI Act · HIPAA
Compliance-ready design
Architecture built for these regimes, mapped during assessment.
The problem
The Frontier LLM Dilemma
Enterprises are stuck between two bad options. Sending sensitive data to public frontier models creates risk. Withholding it leaves the business behind.
Option B
If you do not share
- Zero AI benefit
- Shadow AI used by employees
- Delivery lag behind competitors
- 01Tools
Reusable building blocks: identity-aware retrieval, anti-regression gates and signed audit logs.
- 02Processes
A repeatable path from assessment to validated go-live, with sign-off at each milestone.
- 03Experts
One accountable team across hardware, platform, data, models and operations.
What we build for you
Six offerings. One full-stack private AI platform.
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01
Hardware & Acceleration Setup
Set up in your DC, procure from your approved local GPU vendor, or procure new NVIDIA GPUs and set them up.
Technical specifications
- Procurement paths: client DC, local authorized vendor, or turn-key NVIDIA DGX / SXM / PCIe
- GPU options: H100, H200, L40S, sized to workload
- Burn-in validation: DCGM diagnostics
- NCCL interconnect bandwidth tests
- NVMe IOPS benchmarks and thermal soak
- Signed burn-in report before platform work starts
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02
GPU Platform & Software Layer
Drivers, orchestration, model serving and observability on your GPUs, built for air-gapped operation.
Technical specifications
- Hardened OS baseline
- NVIDIA GPU Operator and Kubernetes orchestration
- Dynamic MIG partitioning and time-slicing
- Local Harbor air-gapped registry
- vLLM model serving
- GPU, latency and queue observability
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03
Enterprise Data Preparation
Prepare your local datasets and repositories for retrieval and model tuning, with sensitive fields handled before they reach a model.
Technical specifications
- Native connectors: SharePoint, Jira, Confluence, Git, databases, email
- PII scanning and masking with Presidio
- Permission-aware ingestion
- Scheduled re-indexing as data changes
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04
Private Domain Fine-Tuning
Fine-tune models on your own data. The tuned weights stay private to you.
Technical specifications
- QLoRA / PEFT training pipelines
- Domain dataset generation
- Adapter registry with versioning
- Regression gates and rollback
- Open-weight license review
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05
Knowledge & Dependency Graph Layer
Multi-RAG, knowledge graph and dependency graph, based on need.
Technical specifications
- Multi-RAG engine across document and code corpora
- Neo4j entity and knowledge graphs
- Codebase and infrastructure dependency tracking
- Identity-aware retrieval
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06
Unified Enterprise Endpoints
Local AI endpoints exposed to your whole organization and used like ChatGPT.
Technical specifications
- OpenAI-compatible API gateway (LiteLLM)
- SAML / OIDC single sign-on
- Rate limiting and quotas
- Department-level token chargeback
- Audit logging on every call
Reference architecture
Everything runs inside your boundary.
Select a layer to see what runs there. Frontier models sit outside the perimeter and are reachable only through the Policy Router.
Inside your perimeter
Outside cloud
Inside your boundary
Your Apps and Use Cases
Engineers build on familiar OpenAI-style APIs, or AiSE builds copilots using ready patterns: code assistant and repo search, document and policy Q&A, support and ops copilots, reports and analysis.
Inside your boundary
AI Gateway
LiteLLM-based gateway with SAML / OIDC SSO, rate limits, token chargeback and the Policy Router. Every call is logged.
Inside your boundary
Private Models
Open-weight models served with vLLM on your GPUs. Adapters are versioned in a registry and gated by regression tests. Weights stay in your RAM and chassis.
Inside your boundary
Knowledge Layer
Retrieval across documents and code plus Neo4j entity and dependency graphs, honoring source-system permissions.
Inside your boundary
GPU Platform
Hardened OS, NVIDIA GPU Operator, Kubernetes, MIG / time-slicing and a local Harbor registry so the platform runs air-gapped.
Inside your boundary
Hardware
H100, H200 or L40S class GPUs, burn-in validated before handover.
Outside your boundary, by policy only
Public Frontier Models
Optional. Only sanitised, non-sensitive requests are routed here, under strict policy and audit logging.
Runs through every layer
Security and Identity
Identity, encryption, network policy and signed audit logs apply at every layer of the stack.
Security and policy router
Private by default. Frontier by policy.
The Policy Router is a real-time data classifier. It checks the sensitivity of every request, applies your rules and logs every call.
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Sensitive corporate IP
Routed to private local vLLM instances. Weights never leave RAM or chassis, and data never leaves your boundary.
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Sanitized generic questions
Optionally routed to a frontier model under strict audit logging. No local data attached.
Try the router
Request→Policy Router→Private model, inside your boundary
Classified: Restricted: corporate IP. Matches confidential-document and deal-data policy rules.
route=private-vllm egress=none policy=conf-docs-v3
Request→Policy Router→Private model, inside your boundary
Classified: Internal: source code. Contains internal code and service identifiers.
route=private-vllm egress=none policy=source-code-v2
Request→Policy Router→Public frontier model
Classified: Generic: no local data. No sensitive content detected. Frontier routing is allowed by policy.
route=frontier egress=sanitised policy=generic-v1 audit=signed
Request→Policy Router→Public frontier model
Classified: Generic: public knowledge. Public-knowledge question. No local data attached.
route=frontier egress=sanitised policy=generic-v1 audit=signed
Illustrative example. Real classification rules are defined with you during design.
- ProtectSensitive data never leaves your boundary.
- BenefitFrontier quality for generic work.
- ControlAudit trail and spend visibility per team.
3-month implementation
Path to go-live in about 90 days.
A 14-week plan. GPU sourcing starts on day 1 because it is the critical path: if it starts late, go-live slips.
Weeks 1–2
Assess & Design
Use cases, data, compliance and hardware option.
- Workload sizing
- Compliance scope (DPDPA, EU AI Act, HIPAA)
- Hardware path selection
Weeks 1–6
GPU Procurement
Starts day 1 and sits on the critical path. Lead times vary.
- Vendor or NVIDIA order placed at kick-off
- Delivery tracking against rack-and-stack dates
- Go-live slips if sourcing starts late
Weeks 3–6
Foundation
Hardware, GPU platform, serving and security.
- Racking and burn-in report
- Kubernetes GPU platform
- Air-gapped Harbor registry
Weeks 5–10
Data Prep & Fine-Tuning
Data prep, fine-tune, RAG, graphs and evals.
- RAG and Neo4j graph construction
- QLoRA / PEFT fine-tuning
- Evaluation harnesses on your benchmarks
Weeks 11–13
Endpoints & Go-Live
Endpoints, routing, first use cases and handover.
- OpenAI-compatible endpoint and SSO
- Policy Router and audit logging
- Production go-live and handover
Week 14+
Run & Evolve
Managed service, AgentOps and data re-indexing from week 14.
- Patching, upgrades and incident response
- Scheduled data refresh and re-indexing
- Monitoring, evaluation gates and cost reports
Week 14 onward
Run and evolve
Your private AI always has your latest data.
- Maintenance and SupportPatching, upgrades and incident response for the full AI stack.
- Data RefreshNew and changed data flows in so answers stay current.
- Model UpgradesMove to better base models and re-tune when it pays off.
- Evaluation GatesEvery change is tested against your benchmarks before release.
- Monitoring and AgentOpsQuality, latency, failures and drift tracked in production.
- Cost and Usage ReportsGPU use and cost per team and per use case.
Architecture deck
Explore the AiSE Private AI Architecture Deck (13 Slides)
Browse the full presentation. Use the arrow keys, jump to any slide, zoom into detail or go full-screen.
AiSE Private AI approachYour Data, Your Model, Your Hardware, Your Boundary.
Commercial model
Three ways to engage. One clear next step.
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01
Setup & Build
One-time milestone fee
Covers assessment through validation sign-off. Hardware cost depends on the path you choose: your DC, local vendor or new NVIDIA GPUs.
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02
Managed Operations
Monthly retainer
Drift monitoring, CVE patching, data refresh and model updates.
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03
Use Case Sprints
4–6 week fixed-scope sprints
Optional. Custom departmental copilots, scoped and priced per use case.
Why AiSE
- One accountable partner
Tools, processes and experts from one team, from design to run.
- Hardware-neutral
Your DC, a local approved vendor or new NVIDIA GPUs. We set up any of them.
- Proven building blocks
Identity-aware retrieval, anti-regression gates and signed audit logs, ready to reuse.
- Compliance-ready design
Built for regimes like DPDPA, EU AI Act and HIPAA. Open-weight model licenses are reviewed for you.
Next step
Start with our 2-Week Architecture & GPU Sizing Assessment.
Use cases, data, compliance and hardware option, in two weeks. Share a few details and the intake form returns an indicative GPU sizing straight away.