Data privacy and mounting cloud costs are driving a shift toward localised AI. Enterprises want the power of Large Language Models (LLMs), but they cannot afford to send sensitive files over public networks.
Here’s where the Rhea: Latent Loom project comes in to play, a self-hosted LLM service designed to keep your data local, secure, and instantly searchable.
Table of Contents
What is Rhea: Latent Loom?
Rhea: Latent Loom is an independent, locally deployable LLM (AI) service that can run standalone or integrate deeply with the broader Rhea Generative Framework. Built on an OpenAPI specification, it mimics cloud infrastructure but operates locally entirely within your own perimeter.
- Drop-in replacement: Seamlessly swaps out cloud providers using standardised API endpoints.
- Full compatibility: Supports standard Chat Completion and Responses object types.
- Absolute data control: Keeps your organisational intelligence locked down locally.
The core strength: Local RAG and file ingestion
The real strength of the Rhea: Latent Loom project lies in its Retrieval-Augmented Generation (RAG) capabilities. It transforms messy, disorganised local business archives into a structured, highly intelligent vector database.
Instead of manually reading thousands of pages, you ingest your knowledge base and query it directly using natural language.
Universal Office file support
Latent Loom uses a custom internal document-parsing service to resolve complex file conversion steps. It handles:
- Standard Documents: PDFs and rich text.
- Office Formats: Word files, spreadsheets, and presentation formats.
- Visual Data: Embedded image data and graphics.
Inside the API: How it works
The project’s Swagger UI API Explorer maps out exactly how developers can build private AI tools. The core architecture relies on several highly optimised ingestion and query endpoints:
- Unified Ingestion: Merges document parsing into a single endpoint for streamlined pipelines.
- Assisted RAG (
/rag/ingest/assisted): Leverages internal document engines to parse files cleanly. - Synthetic Generation (
/v1/rag/synthetic): Automatically manufactures synthetic questions from your data during ingestion to train and improve the AI model’s target accuracy. - Customised Search (
/v1/search): Allows developers to tune vector distance thresholds to balance precision versus broad discovery.
Key business use cases
Private Legal & Financial Discovery
Review local case files, financial reports and sensitive customer data completely offline.
- Air-gapped Litigation Preparation: Analyse decades worth of internal client communications, witness statements, and historical case law locally to identify patterns, inconsistencies, or key evidence safely.
- Automated Know-Your-Customer (KYC) Audits: Query structured and unstructured identity documentation, background checks, and financial history to flag risks while strictly adhering to data sovereignty laws.
- Secure M&A Due Diligence: Ingest thousands of sensitive corporate tax records, balance sheets, and unannounced merger documents to evaluate liabilities without leaking market-moving data to cloud models.
Regulatory healthcare & medical device management
Accelerate R&D and simplify strict regulatory compliance for highly sensitive medical data.
- ISO 13485 Design History Files (DHF): Ingest product data, hardware designs, and CAD specifications locally. Engineers can query legacy blueprints to track design choices, maintaining the strict traceability required for ISO 13485 certification.
- Audit-Ready Test & Sample Data Analysis: Cross-reference batches of laboratory test results, material stress tests, and clinical trial records. Regulatory teams can query this data to prepare for FDA/CE submissions or unexpected audits.
- HIPAA-Compliant Localised Knowledge Base: Query internal documentation, device manuals, and sample data safely. Because the architecture is entirely local and air-gapped/behind your firewall, it fundamentally bypasses the risk of HIPAA or patient-data privacy violations.
Automated document enhancement & extrapolation
Extrapolate text and map questionnaire data onto localized templates safely.
- Secure Contract Generation: Extract key variables from legacy agreements to auto-populate new, localized legal templates without exposing proprietary clauses to public LLM training sets.
- Regulatory Questionnaire Mapping: Automatically cross-reference engineering data or financial sheets to fill out complex compliance questionnaires, RFPs, and standard operating procedures (SOPs).
Localised context assistants
Provide internal teams with chat bots that use your organisations deep knowledge of proprietary product codebases, medical designs, and company wikis.
- Proprietary Technical Onboarding: Allow new engineers or analysts to query your entire historical codebase, product wikis, and architectural diagrams using natural language without cloud (third-party) exposure.
- Real-Time Field Support: Provide localised chat bots for field technicians or customer support agents, allowing them to pull exact troubleshooting steps from complex product manuals instantly.
Get started
Explore the complete schema, request payloads, and response objects on the interactive Rhea: Latent Loom API Explorer.
Interested and would like to learn more? Get in touch.
