Private AIEvidence-firstOffline-capableOperator-owned
Forge Private AI Node
A deployable, operator-owned AI node you can run inside your environment: retrieval + inference + guarded tools + evidence export — packaged as the last-mile layer between AI infrastructure and real workflows.
Time-to-proof
Days
Single-node deployment
Data boundary
In-house
No external vendors required
Trust
Evidence
Citations + raw artifacts
Built for teams that need outcomes without standing up a generic AI platform or shipping sensitive data to third-party clouds.
What you get
deployablePrivate inference (GPU/CPU) with predictable performance
RAG over docs, tickets, filings, logs, and datasets
Tool execution (APIs, DB, storage, workflows) with audit trails
Evidence export: citations, raw artifacts, reproducible bundles
Metrics/logs/traces with low-cardinality defaults
Deployment modes
• Single-node “appliance” (fastest time-to-proof)
• On-prem cluster (scale + HA)
• Air-gapped / disconnected environments
Reference node (inside your environment)
v1-readyUser Surface
UI / CLI / API clients (reporters, analysts, operators)
SSORBAC
API Facade
Stable, versioned endpoints mapped to user actions
/v1Auth boundary
Retrieval
Index + search over docs, logs, tickets, filings
RAGEmbeddings
Inference
Local models (GPU/CPU) with predictable latency
vLLMQuant
Tools
Approved actions: APIs, DB ops, workflows (audited)
AllowlistAudit
Evidence Export
Citations + raw artifacts + reproducible bundles
ZIPSHA256
Database
Entities, jobs, lineage, audit trail
PG
Object Store
Raw artifacts + evidence packs
S3/MinIO
Observability
Metrics, logs, traces (low-cardinality defaults)
OTEL
Deployment: single-node appliance → on-prem cluster → air-gapped. Same interfaces; scale the plumbing, not the product.
Designed to be defensible
Outputs can include citations, raw object keys, hashes, timestamps, and “how generated” metadata so reviewers can reproduce results.
Fits customer-approved foundations
Use stable interfaces. Fit the node onto customer-approved Linux, Kubernetes, or appliance-style foundations without rewriting the product layer.
Operationally sane
Shipping means predictable behavior: retries, timeouts, and failure modes you can explain.
Cost control
Predictable infra costs. Choose hardware + models that match ROI. Avoid surprise token bills.
Why this exists
Private AI nodes that actually ship
Most orgs don’t need a research lab. They need a reliable stack that turns internal knowledge into defensible actions — inside their security boundary.
Keep sensitive data in-house
Run inference and retrieval where your data already lives. Keep policy, access, and audit under your control.
Evidence-first outputs
Every answer can be backed by raw artifacts and citations. Build trust with users and reviewers.
Operational reliability
Shipping means predictable behavior: retries, timeouts, and failure modes you can explain.
Cost control
Predictable infra costs with measurable ROI. No surprise bills. No forced vendor roadmap.
Reference architecture
Simple components, stable interfaces
Adopt incrementally: start with a thin UI + API facade, then add tools, evidence export, and governance as you prove value.
User surfaces
UI, CLI, or API clients — focused on workflows, not infrastructure.
Auth boundary lives here.
API facade
Stable endpoints that map to user actions (search, drilldown, export).
Versioned contracts (v1, v2...).
Core services
Retrieval + inference + tools + evidence export, with an auditable trail.
Observable + policy-driven.
Object store
Raw artifacts, evidence bundles, immutable logs.
Presigned URLs for safe downloads.
Database
Entities, jobs, indexes, user actions, audit trail.
Stable schemas; deliberate migrations.
Queue / workflows
Ingest, parse, enrich, export; retries + DLQ.
Deterministic job runs.
Observability
Metrics + logs + traces.
Cardinality-safe defaults.
Adoption path
Step 1: Thin UI + stable v1 API
Step 2: Retrieval + evidence export
Step 3: Tools + governance + automation
Security model
Clear boundaries, minimal surprises
If you can’t explain how data moves, you can’t deploy it safely. This stack is designed to be auditable.
Auth boundary at the API facade
UI calls your server-side routes. Your server decides what upstream calls are allowed and what data is returned.
Least privilege everywhere
Service tokens are scoped per capability (read evidence, build exports, run tools). Object storage access is presigned per request.
Evidence and audit trails
Outputs can include citations, raw object keys, hashes, timestamps, and “how generated” metadata so reviewers can reproduce results.
Offline / air-gapped readiness
Works in disconnected environments — but updates must be treated as a controlled process.
If you run disconnected, plan for: model distribution, signed artifacts, and explicit update workflows.
Capabilities
What teams build with it
Start with evidence-heavy internal workflows, then expand deliberately. Forge is a deployment layer, not a blank platform.
Knowledge + Q&A with citations
Query internal documents and datasets; return grounded answers with pointers back to sources.
Investigations & evidence export
Build a reproducible “case file”: raw artifacts, derived notes, hashes, and downloadable bundles.
Ops copilots
Turn tickets, logs, runbooks, and metrics into guided troubleshooting steps with safe tool execution.
Batch enrichment
Ingest sources, normalize fields, dedupe entities, and maintain change history over time.
Tool-driven automation
Allow approved actions (update status, create tasks, fetch artifacts) with explicit allowlists and audit.
Governance & outcome tracking
Define success criteria, measure impact, and keep the system honest with evaluation and monitoring.
Want to scope a private AI node?
Fastest path: single-node proof → workflow hardening → scale.