Private model inference
Local or dedicated inference infrastructure selected for each deployment.
TECHNOLOGY & SECURITYPrivate AI.
Private AI.
Engineered with control.
A technology approach built around privately deployed large language models, integrated information sources and industry-specific software engineering.
OUR ENGINEERING APPROACHA complete AI system.
A complete AI system.
Not just a model.
Lorexa develops applications around language models: document processing, retrieval, calculators, data-access tools, policy checks, user workflows and evaluation methods.
The foundation models themselves may be third-party open-weight models privately deployed and adapted within Lorexa's systems. Our engineering focus is on the specialized system and its operational reliability.
Domain-specific workflows
＋ Private LLM inference
＋ Controlled data tools
＋ Evidence verification
＋ Private LLM inference
＋ Controlled data tools
＋ Evidence verification
Private by architecture.LOREXA / TECHNOLOGY
THE INTELLIGENCE STACK
Each layer has a purpose.
Document processing
Extraction, field identification, cross-document checks and evidence preparation.
Data access tooling
Application-mediated access to permitted business records and verified context.
Validation & guardrails
Domain checks, quantitative validation, source alignment and uncertainty handling.
ENTERPRISE INTELLIGENCEApproved people. Approved context.
Approved people. Approved context.
Reviewable outputs.
Industry reasoning becomes operationally useful when information flows through defined roles, controlled integrations and clear human decision points.
Authorized users
Defined teams, permissions and accountable roles.
Enterprise information
Approved databases, documents and appropriate current research sources.
Lorexa AI engine
Sector knowledge, retrieval, comparisons and validation.
Human decision support
Reports, comparisons, exceptions and recommended review steps.
Enterprise integration, permissions, hosting and data residency must be defined and verified for each actual deployment. No system should bypass the organization's approval controls.
DATA HANDLING PRINCIPLESConfidentiality requires
Confidentiality requires
real architecture.
We design around separation of concerns: private records inform private inference, while public research, when enabled and approved, is treated as a distinct external data source.
A privately hosted model does not automatically mean an entirely offline application. Web hosting, networking, document storage and other infrastructure must be assessed separately for each customer deployment.
What our privacy approach means
- No need to paste confidential business files into consumer chatbots for the core AI workflow.
- Access to private data should be limited to the authorized use case and identities.
- External retrieval should request only public information, never transmit customer records as search terms.
- Security controls and data residency are documented and verified per deployment.
ACCOUNTABLE ENGINEERING
What we do and what we don't claim.
Technical claims grounded in deployment
We describe the model hosting, connections and data flows that have actually been implemented for a given customer.
No invented certifications
Compliance and security certifications should only be advertised after they have been independently obtained and verified.
Evaluation before broad rollout
Commodity-specific evidence and reasoning need structured testing before the system is relied on operationally.
Human authority stays in the loop
Systems should support human review rather than silently replace contractual approval and trading judgment.
CLIENT-SPECIFIC AIYour company's knowledge.
Your company's knowledge.
Your AI's context.
We tailor each deployment to the client's own products, services, data structures, approved records and document formats. Authorized retrieval and validation connect the model to relevant company information, so responses can reflect business-specific evidence.
Client-specific grounding does not imply that customer documents are used to train a public foundation model. Data-access policies and any model customization are defined per deployment.
01Approved company sourcesContracts · Structured records · Operational documents
02Controlled understandingPrivate inference · Context retrieval · Evidence rules
03Relevant recommendationsGrounded analysis · Citations · Human review
Discuss a private AI architecture for your organization.
Tell us about your data sensitivity, existing systems and preferred deployment controls.