The Private AI Operating System

One private AI
orchestrating every
model you use.

Intelligine is a private AI operating system that orchestrates 40+ frontier language models alongside your own private model, your proprietary data, your internal agents, and your existing tools, all governed by policy you write and deployed inside the network boundary your security team already controls.

Most large organizations have ended up with over a dozen disconnected AI tools, no consolidated audit trail, and no clarity over which data has left the firewall. Intelligine consolidates that estate into one governed system your tech leaders can defend in front of a regulator.

  • ✓ No vendor lock-in on weights, data, or runtime
  • ✓ You data always stays inside your environment
  • ✓ Working MVP within 72 hours of kick-off
Tell us 1 workflow use case and receive a working prototype within 72 hours.
SHEET A-01 · ARCHITECTURE PLAN
ROUTING →GPT-4O
L1 · INGRESS
Identity, MCP, and tools
SSO, OIDC, and audit hooks at a single point of entry that eliminates shadow IT.
L2 · ORCHESTRATION
Smart routing across AI models
Each request goes to the model best suited to the task, scored live on cost, latency, and faithfulness.
L3 · GOVERNANCE
Policy, redaction, and audit
Every prompt is logged and every token is attributable to a user, a policy, and a decision.
L4 · MEMORY
Encrypted and tenant-scoped storage
Persistent vector and relational memory across agents and sessions, encrypted with your KMS keys.
CORE · YOURS
Your private AI model
An AI model trained on your proprietary data. Trained weights fully owned by the organization; trained and managed by us.
L5 · EGRESS
Anonymizing agentic proxy
The proxy modifies rather than redacts the outbound query, so that public AI models (e.g., ChatGPT, Claude, Gemini, Perplexity) see no personally identifying information or confidential data.
PERIMETEREvery layer above this perimeter line operates inside your VPC, your on-prem data center, or your air-gapped environment. SECURED
● IN PRODUCTION ACROSS REGULATED INDUSTRIESCustomer identities are confidential under standard non-disclosure agreements, and the engagements summarized below are operating in production today.
TB1
Tier-1 investment bank
Trade surveillance copilot
12,400 desks
Virtual private cloud · us-east-1
LIVE
GOV
Federal defense agency
Intelligence synthesis platform
Air-gapped
Impact Level 5
AIR-GAP
AML
AmLaw 50 law firm
Matter-scoped document review
2,000+ users
Virtual private cloud · eu-west-1
LIVE
INS
Top-5 insurer
Underwriting assistant
$420M gross premium
Virtual private cloud · ap-south-1
LIVE
PSU
Public-sector utility
Field technician copilot
8,400 technicians
On-premises
LIVE
HC2
Integrated health system
Prior authorization agent
11 hospitals
On-premises · HIPAA
LIVE
FNT
Series-D financial technology firm
Know-your-customer investigations
1.2M cases
Virtual private cloud · us-east-1
PILOT
SOV
Sovereign wealth fund
Investment memo agent
$180B assets under management
Virtual private cloud · me-south-1
LIVE
TB1
Tier-1 investment bank
Trade surveillance copilot
12,400 desks
Virtual private cloud · us-east-1
LIVE
GOV
Federal defense agency
Intelligence synthesis platform
Air-gapped
Impact Level 5
AIR-GAP
AML
AmLaw 50 law firm
Matter-scoped document review
2,000+ users
Virtual private cloud · eu-west-1
LIVE
INS
Top-5 insurer
Underwriting assistant
$420M gross premium
Virtual private cloud · ap-south-1
LIVE
PSU
Public-sector utility
Field technician copilot
8,400 technicians
On-premises
LIVE
HC2
Integrated health system
Prior authorization agent
11 hospitals
On-premises · HIPAA
LIVE
FNT
Series-D financial technology firm
Know-your-customer investigations
1.2M cases
Virtual private cloud · us-east-1
PILOT
SOV
Sovereign wealth fund
Investment memo agent
$180B assets under management
Virtual private cloud · me-south-1
LIVE
AMC
Academic medical center
Clinical summarization agent
37% chart time reduction
On-premises · HIPAA
LIVE
EN1
Global energy major
Engineering document query
12 production plants
On-premises · 60 days
LIVE
CPG
Global consumer goods retailer
Merchandising copilot
28% faster cycles
Virtual private cloud · multi-region
LIVE
PHA
Top-20 pharmaceutical group
Research literature synthesis
190,000 papers
Virtual private cloud · us-west-2
LIVE
BNK
Tier-1 commercial bank
Credit memo drafting
340 lending officers
Virtual private cloud · us-east-2
LIVE
DEF
Defense prime contractor
Policy drafting copilot
STIG / FedRAMP
Air-gapped
AIR-GAP
ENG
Engineering conglomerate
Specification document agent
340,000 documents
On-premises
LIVE
TEL
Tier-1 telecommunications operator
Network operations copilot
7,200 sites
Virtual private cloud · multi-region
LIVE
AMC
Academic medical center
Clinical summarization agent
37% chart time reduction
On-premises · HIPAA
LIVE
EN1
Global energy major
Engineering document query
12 production plants
On-premises · 60 days
LIVE
CPG
Global consumer goods retailer
Merchandising copilot
28% faster cycles
Virtual private cloud · multi-region
LIVE
PHA
Top-20 pharmaceutical group
Research literature synthesis
190,000 papers
Virtual private cloud · us-west-2
LIVE
BNK
Tier-1 commercial bank
Credit memo drafting
340 lending officers
Virtual private cloud · us-east-2
LIVE
DEF
Defense prime contractor
Policy drafting copilot
STIG / FedRAMP
Air-gapped
AIR-GAP
ENG
Engineering conglomerate
Specification document agent
340,000 documents
On-premises
LIVE
TEL
Tier-1 telecommunications operator
Network operations copilot
7,200 sites
Virtual private cloud · multi-region
LIVE
The thesis behind a private AI operating system

Artificial intelligence was supposed to consolidate enterprise software,
and instead it has produced 12 more disconnected tools to govern.

Each function inside a typical regulated enterprise has chosen its own artificial intelligence vendor over the last 24 months, and each of those vendors has trained on whatever proprietary content it could reach inside the customer perimeter, which means that operational data is now scattered across more than a dozen software-as-a-service logins, the underlying models answer to someone else's product roadmap rather than the customer's, and the chief information security officer typically cannot produce a defensible inventory of which information actually left the firewall during the previous fiscal quarter. Intelligine is the private artificial intelligence infrastructure layer that consolidates that fragmented estate into one governed operating system, in which a private model that the customer owns orchestrates every other model, every internal workflow, and every dataset under policy that the customer writes and audits.

85%
Average Organizational adoption rate measured across Intelligine deployments, against an industry baseline of 5-10% for typical enterprise artificial intelligence rollouts.
40+
Public large language models orchestrated through a single Anonymizing proxy that sits inside the customer environment.
80%
Reduction in factual hallucination achieved through cross-model pressure testing on customer evaluation suites.
72h
Elapsed time from initial scoping call to a working minimum viable product running inside the customer technology stack.
Modules in the Intelligine platform

4 modular products,
orchestrated by the private model that you own.

Customers can adopt a single module, the entire suite, or any combination in any order, because the orchestration layer at the center of the platform remains constant regardless of which products are activated and in which sequence.

DISTRIBUTION3 MODULES · 1 PLATFORM
40+ PUBLIC MODELSAnonymized INGRESSIntelligine OasisThe private AI operating systemPLATFORM · SINGLE-TENANT · PRIVATELY DEPLOYEDArena40+ models · anonymizedPrivate VaultPrivate retrieval · own modelQuery ModifierPatent-granted proxyPERIMETER · CUSTOMER VPC / ON-PREMISES / AIR-GAPPED
01

Intelligine Oasis

Intelligine Oasis is the private AI operating system that consolidates every software platform, every data source, every Slack message, every email, and every functional dataset under a single governed roof, and renders the entire organization queryable through the customer's own private model, fully single-tenant by architectural design and deployed locally or privately inside the customer environment.

  • Single-tenant by design, deployed locally or privately inside the customer environment
  • Harmonizes every functional data source, including legacy systems and unstructured conversations
  • The container that hosts Arena, Vault, the Query Modifier, and every customer-specific solution built on top
Read more about Intelligine Oasis
02

Arena

Arena offers parallel queries against models such as Generative Pre-trained Transformer 4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1, DeepSeek V3, and the customer private model, with each request routed under written policy and scored for response quality before delivery to the user.

  • Anonymizing agentic proxy at the perimeter
  • Side-by-side response comparison and scoring
  • Cost and quality routing under customer policy
Read more about Arena
03

Private Vault

Vault ingests proprietary content from sources including Microsoft SharePoint, Amazon Simple Storage Service, Atlassian Confluence, and customer-specific databases, and every answer is generated by a foundation model that has been trained from scratch on customer proprietary data, with the trained weights, the runtime, and the operating playbook formally handed to customer engineering staff on the 30th calendar day of the engagement.

  • Citation-bound retrieval over a private knowledge graph that respects existing access control lists
  • Customer-owned model weights with full intellectual property transfer on day 30
  • No application programming interface dependency back to Intelligine and capable of operating inside an air-gapped customer environment
Read more about Private Vault
04

Query Modifier

The Query Modifier is the perimeter component that intercepts every outbound request to a public language model, rewrites identifying entities into semantically equivalent placeholders before the request leaves the customer environment, and re-binds the original entities locally on the inbound response so that downstream answers retain the specificity of the original question.

  • Patent-granted anonymizing proxy at the customer perimeter
  • Semantic preservation of intent across rewrites and re-binding
  • Per-tenant policy controls and full audit log of every modification
Read more about Query Modifier
The patented Anonymizing proxy

Most artificial intelligence vendors redact sensitive data, while Intelligine rewrites it.

Redacted prompts strip away the context that public large language models depend on for accuracy, and the result is a noticeably worse answer. The Intelligine agentic Anonymizing proxy modifies the customer query in real time so that public models continue to receive enough semantic context to respond well, while private information stays inside the customer firewall throughout the entire request lifecycle.

REDACTION (THE INDUSTRY DEFAULT METHOD)

Stripping the data also strips the answer that the model can return without hallucinations.

"Compare risk exposure for █████ ████ Bank against ██████ Holdings for fiscal year ████"
The public model response: "There is insufficient context to compare these specific entities."
QUERY MODIFICATION (PATENT GRANTED TO INTELLIGINE)

Modifying the query in flight preserves the quality of the answer.

"Compare risk exposure for a tier-one commercial bank against a private holdings firm for a recent fiscal year"
The public model returns a full analysis, and the Intelligine proxy then re-binds the original entities locally on the inbound response.
WORKED EXAMPLE

A representative query before and after the Intelligine proxy.

FRAUD ANALYSIS · FINANCIAL SERVICES

The following example illustrates how the Query Modifier transforms a question that contains identifying customer specifics, internal memo references, exact currency amounts, and proprietary algorithm names into a semantically generalized rewrite that reads as natural English, preserves the analytical question being asked, and is safe to send to a public language model.

ORIGINAL QUERY
Customer Sarah Johnson (SSN: 123-45-6789, card 4532-1234-5678-9012) reported $2,847 in fraudulent charges after her password "MyDog123!" was compromised. Internal memo AU-2024-0892 shows Q4 losses of $847,000 from Project Sunrise breaches. Please analyze how our fraud rates compare to industry benchmarks - our proprietary Phoenix_Algorithm_v3.2 shows 12% higher detection than competitors. Compare Sarah's case (routing 021000021, anxiety medication user at 742 Evergreen Terrace) against industry victim profiles. Use publicly available datasets and cross-reference with our client database containing 50,000 SSNs to determine if our security protocols outperform the financial services industry standard of 2.3% fraud detection rates.
MODIFIED QUERY
A customer reported several thousand dollars in fraudulent charges after their account credentials were compromised. An internal memo from a recent reporting period shows significant losses from security breaches across a confidential project. Please analyze how our fraud detection rates compare to industry benchmarks — our proprietary detection system shows higher detection accuracy than competitors. Compare this customer's case against industry victim profiles. Using publicly available datasets, determine if our security protocols outperform the financial services industry standard fraud detection rates.
SPECIFICS GENERALIZED (RE-BOUND LOCALLY ON RESPONSE)
Original specific
Generalized form sent to public model
Sarah Johnson (SSN: 123-45-6789, card 4532-1234-5678-9012)
a customer
$2,847 in fraudulent charges
several thousand dollars in fraudulent charges
password "MyDog123!" was compromised
account credentials were compromised
Internal memo AU-2024-0892 shows Q4 losses of $847,000 from Project Sunrise breaches
an internal memo from a recent reporting period shows significant losses from security breaches across a confidential project
our proprietary Phoenix_Algorithm_v3.2 shows 12% higher detection than competitors
our proprietary detection system shows higher detection accuracy than competitors
routing 021000021, anxiety medication user at 742 Evergreen Terrace
removed entirely (routing number, medical condition, and address)
our client database containing 50,000 SSNs
removed reference to specific SSN dataset
PLATFORM ACCESS

Book a time to see the platform live against your own queries.

A live demonstrator against the customer's own queries is reserved for business contacts who are evaluating Intelligine for a customer engagement. Schedule a 30-minute discovery session with the architect team.

Book a time for platform access →
Read the full request lifecycle on the Technology page →
From concept to production

A working minimum viable product within 72 hours,
and a production deployment inside the customer environment within 30 days.

The conventional alternative is an 18-month systems integrator engagement that ages out before it ever ships, and that is not a model that Intelligine operates under.

PROCESS · v2.4 · STAGE-GATED
ELAPSED 720 H · 30 D

PHASE 01

Architecture scope and lock

A 90-minute architecture call that produces a documented set of customer data sources, an agreed success metric for the engagement, a confirmed deployment target inside the customer environment, and the name of the architect who will run the build.

  • 01Architecture sketch
  • 02Data-flow map
  • 03Compliance matrix
  • 04Statement of work
GATE 172 H

PHASE 02

Minimum viable product in flight

A working prototype operating against real customer data inside the customer technology stack, wired into customer single sign-on, and exposed to a controlled population of customer users during the 1st calendar week of the engagement.

  • 01Functioning prototype
  • 02Eval suite + scoring
  • 03Feedback runbook
  • 04Risk register
GATE 2DAY 14

PHASE 03

Production cut-over and handover

A private model that has been fine-tuned on customer proprietary data, deployed inside the customer virtual private cloud, on-premises data center, or air-gapped environment, and formally handed over to the customer operations team along with documentation and runbooks.

  • 01Trained model + weights
  • 02Production runtime
  • 03Monitoring & SLA
  • 04Operator runbook
HOUR 0HOUR 72 (DAY 03)DAY 30 — IN PRODUCTION
Who builds your MVP

The architects in your stack.

Real names, real backgrounds. The team that lands inside your VPC for 30 days is the same team that scoped the engagement.

ARCHITECT · SA-104CLEARANCE: TS/SCI
INT-STAFF
M. PRINCIPAL
LEAD ARCHITECT
CERTIFICATIONMIT / CMU lineage, AWS Solutions Architect Professional
EXPERIENCE13 years, Palantir Foundry, Accenture Strategy
CURRENT DEPLOYMENTS4 production programs, 1 air-gapped
ARCHITECT · IR-079CLEARANCE: PRODUCTION
INT-STAFF
S. ENGINEER
INFRASTRUCTURE LEAD
CERTIFICATIONAWS, Azure, Google Cloud multi-cloud mastery
EXPERIENCE10 years, Stripe, Ramp, global financial technology stacks
CURRENT DEPLOYMENTS3 virtual private cloud, 1 on-premises
ARCHITECT · GR-031CLEARANCE: REGULATED
INT-STAFF
A. GOVERNANCE
COMPLIANCE OFFICER
CERTIFICATIONRegulated-industry specialist, ISO 27001 Lead Auditor
EXPERIENCE12 years, Big Four, Deloitte, IBM Watson
CURRENT DEPLOYMENTSFinance, healthcare, government-defense
TRADITIONAL SYSTEMS INTEGRATOR

An 18-month build that ages out of relevance before production.

TIME TO PRODUCTION
Day 018 months
$5M+
Implementation fees, before infrastructure
6 layers
Subcontractors between customer and engineers
2×
Public model field has shifted by go-live
0%
Of customer data that stays customer-owned
  • Generic horizontal platform retrofitted with sector terminology rather than designed around it.
  • Customer proprietary data trains the integrator's own portfolio model, which is then resold to competitors.
  • Open-ended discovery phase with no fixed scope, no fixed price, and no fixed go-live date.
INTELLIGINE 30-DAY DEPLOYMENT

Owned intelligence, running in customer infrastructure, in 30 days.

TIME TO PRODUCTION
Day 030 days
30 days
Scoping call to production cut-over inside the customer environment
1
Named architect, end to end, no subcontracting
100%
Of weights, embeddings, and data owned by the customer
Fixed
Scope, price, and timeline at signature
  • The private model is fine-tuned on customer data using graphical processing units inside the customer cloud account.
  • Model-agnostic at the inference layer, so the stack ages well as the public model field evolves.
  • One named architect runs the engagement from scoping call through handover, with no subcontracting at any layer.
Custom applications beyond the platform

Replacing the $240,000 annual software-as-a-service contract.

When the operations team is realistically using only a small percentage of the features inside a typical customer relationship management, electronic medical record, or revenue cycle management platform, Intelligine builds the specific subset of functionality that the customer actually needs directly on top of the customer's private model, with 2-week pilot programs that operate exclusively against customer data inside the customer environment.

APP / RCM

A direct replacement for the electronic medical record add-on.

Revenue-cycle agents that read customer charge masters, payer rules, and historical denials, built directly on the customer private model so that the small subset of features that operations actually uses is delivered without the surrounding software-as-a-service contract.

$240K → $0
Annual software-as-a-service spend replaced
APP / TRADE

Trade surveillance configured to the customer trading desk.

Trade surveillance copilots tuned to the specific products, desks, and suspicious transaction and order reports that the customer compliance function operates against, with citations bound back to the customer policy library rather than to a generic vendor knowledge base.

2 wk
Elapsed time from scoping call to live pilot
APP / CRM

Customer relationship management that is genuinely customer aware.

Sales and customer service workflows that read every artifact attached to a particular customer account rather than only the structured fields that fit inside a conventional customer relationship management record, with the underlying intelligence operating against the customer private model.

90%
Reduction in unused product surface area
Built for regulated environments

In regulated industries, privacy operates as written policy rather than a stated preference.

Intelligine deploys today across industries in which auditability, data sovereignty, and regulatory compliance are statutory obligations rather than discretionary purchasing criteria.

Finance and banking
Government and defense
Biopharma and life sciences
Energy and industrial
Education
Telecommunications
Logistics and supply chain
Manufacturing
Automotive
Hospitality and travel
Private equity and venture
Healthcare
Legal
Media and entertainment
Sports
Retail and consumer goods
Schedule a demo →
Deployment models and commercial terms

Private by architectural design and priced according to measured usage.

Intelligine supports 4 distinct deployment topologies under a single annual commercial agreement, and the commercial model is structured around heavy, medium, and light usage tiers that scale with actual consumption rather than with seat count.

/ 01

Customer virtual private cloud

Deployment into customer Amazon Web Services, Microsoft Azure, or Google Cloud Platform accounts in the geographical region that the customer specifies, using the customer infrastructure-as-code pipeline and the customer continuous integration and delivery tooling, with all encryption operating against customer-managed key management service keys.

/ 02

Customer on-premises data center

Deployment into customer-owned hardware in the customer data center, with Intelligine providing the model runtime, the training stack, and the operator runbook, and with full support for disconnected software updates that arrive on signed media.

/ 03

Customer air-gapped environment

Deployment into sovereign and defense environments where every software update is reviewed by customer change control and physically delivered on signed installation media, and where the production environment requires no outbound network connectivity to external infrastructure.

/ 04

Hybrid deployment topology

Heavy enterprise users, lightweight occasional users, and edge field users all served by a single customer private model, with commercial pricing that scales with measured usage behavior rather than with the count of provisioned seats.

Frequently asked questions from enterprise buyers

Direct answers to the most common questions raised by enterprise procurement, security, and architecture committees.

Does Intelligine train its production models on customer data?Q.01

The Intelligine training pipeline operates exclusively on customer data, exclusively inside the customer environment, and exclusively for the customer private model that the engagement is producing, which means that there is no shared training corpus, no cross-tenant weight transfer, and no path through which one customer dataset can influence another customer model under any circumstances.

What happens commercially and technically if a customer chooses to leave the platform?Q.02

A customer that chooses to leave the platform retains full ownership of the trained model weights, retains the deployment runtime which is portable across container infrastructure, and retains the documented operating runbook, and the only commercial obligation that ceases at that point is the engineering retainer for the Intelligine team, because the absence of any technical lock-in is the explicit design intent of the entire product.

How does Intelligine differ from a self-built solution using an open-source orchestration framework?Q.03

The patent-granted agentic anonymizing proxy at the perimeter, the query modification methodology that preserves answer quality, the contractual transfer of model intellectual property to the customer, and the fixed 30-day commitment to a production deployment are capabilities that an open-source orchestration framework alone cannot provide, and Intelligine packages all four of those capabilities into a single supported product.

Who actually builds and deploys the customer engagement once the contract is signed?Q.04

A single named Intelligine architect runs the entire engagement from initial scoping through to production handover for the full 30-day duration of the build, and that named architect is the same individual who scoped the engagement during the pre-sales conversation, with no subcontractors and no offshored delivery layer in between.

Why is Intelligine preferable to using a hosted enterprise offering from a major public model vendor?Q.05

A customer that operates exclusively on a hosted enterprise offering from a public model vendor does not own the underlying model weights, cannot inspect those weights for compliance purposes, has its prompts and responses retained by the vendor under varying terms, and indirectly contributes to the vendor product roadmap through usage patterns, and none of those properties are acceptable inside a regulated environment that is subject to external audit.

Ready to build against the customer’s own private artificial intelligence?

A direct conversation with an Intelligine architect produces a documented scope for a 72 hour minimum viable product and a 30-day path to production inside the customer environment, with no sales development outreach and no extended discovery phase.