foundation4 - Your Secure Knowledge Infrastructure

Secure knowledge infrastructure for teams that refuse to compromise. Embed, store, retrieve, and generate — entirely within your perimeter.

Deploy Anywhere

Self-managed infrastructure. Run foundation4 on your own hardware, in your VPC, or any environment you control. Your proprietary data never leaves your perimeter.

On-Premises Data Custody

Most AI platforms require sending your documents to external embedding APIs, hosted vector databases, or third-party LLM endpoints — often all three. foundation4.ai runs the entire pipeline inside your infrastructure: embedding, storage, retrieval, and generation. No proprietary data crosses your network boundary at any stage.

Data Lineage & Source Tracing

Track metadata, expiration dates, and clearance levels for every piece of data in your pipeline. Enable tracing on any query to see exactly which document fragments were retrieved, the full prompt assembled for the LLM. When an auditor, inspector general, or compliance officer asks "what did your AI know, and how did it arrive at this answer?" — you have a full audit trail from ingestion to retrieval.

Air-Gapped Deployment

Classified environments, SCIF networks, and regulated enclaves can't tolerate outbound connections. foundation4.ai deploys fully disconnected — container images pre-pulled, embedding models loaded from local storage, LLMs running on local hardware. No runtime downloads, no external registry calls, no telemetry.

OpenAI-Compliant REST API

Drop-in compatible with the OpenAI API spec. Swap in foundation4 without changing your existing LLM integration code.

Flexible Data Ingestion

Documents are posted via REST or MCP and processed asynchronously through a NATS JetStream cluster. Ingestion never blocks retrieval — processing workers are scaled independently during bulk imports and throttled-down during steady-state. Choose the embedding model and text splitting strategy that fits your organization's needs.

Fine-Grained Access Control

API keys carry Unix-style permissions (READ / WRITE / EXECUTE) and are scoped to specific classification subtrees. A key restricted to secret/operations/* physically cannot access documents under top-secret/programs, regardless of the query. Enforcement happens at the data layer — not in your application code.

Connect Any LLM

Select the language model at query time via a single request header. Route to GPT-5, Claude, Llama, Qwen, or any OpenAI-compatible endpoint — cloud-hosted or running on your own hardware. Switch between models for different environments or use cases without changing agent configuration.

Metadata & Taxonomy Filtering

Layer SQL-style filters — exact match, range, pattern, set membership — on top of classification to target documents by department, clearance level, status, date, or any custom field. Define taxonomies to express hierarchical relationships (region to office to team) so a query for a parent value automatically matches its children, without denormalizing metadata across documents.

Single-Stack Deployment

foundation4.ai ships as a single Helm chart: PostgreSQL with pgvector for storage, NATS for message queuing, Redis for caching, Prometheus for monitoring — all in one Kubernetes namespace. One backup strategy, one access control model, and operational tooling your infrastructure team already knows. No external SaaS dependencies, no vendor lock-in.

Ready to see it in action?

Ready to start building? We'd love to talk with you about your use case.