The G-1 Platform

See what your compliance team
will actually use.

G-1 is not just a runtime — it ships with a complete operator console. Five workspaces, each one a phase of the AI governance lifecycle as defined by the EU AI Act. The runtime catches the failure; the platform turns the catch into evidence a regulator will accept.

G-1 Product Page → Download Whitepaper

WorkspacesChat · XAI · Agent Flow · Reports

DeploysInside your perimeter

StackvLLM · OpenAI-compatible API

AuditHMAC-SHA256 chain

Workspace 01 · Chat

Real-time interception.
A passthrough that knows when to refuse.

The Chat workspace is the entry point of the platform — a passthrough to the underlying open-source LLM (here Gemma 4 E2B with NSP v2.0 enabled). A user asks a credit-officer question that requires fabricating EBA figures; G-1 generates the response and then blocks it, returning a BLOCKED (HALLUCINATION) notice with five quantitative signals and threshold lines for each. The user sees no fabricated number — only the runtime's reasoned refusal.

Geodesia G-1 Chat workspace: a credit officer prompt blocked at generation time with Hallucination AUROC 96.7%, Prompt Safety and Answer Safety bars, threshold lines, and Constitutional Intelligence active.

Chat · Gemma 4 E2B · NSP v2.0 · Constitutional Intelligence active

Workspace 02 · Causal Explainability

Not a probability.
A causal chain.

This is the workspace that explains why the model produced — or was blocked from producing — a given answer. The center column shows the prompt under audit: a real insurance claim file. The right canvas splits into two synchronized panels: Allowed shows the token-level attribution graph of an output the runtime cleared, with each generated token causally linked to the source tokens that supported it; Blocked shows what the model would have generated, with the trigger tokens highlighted. This is what a Chief Risk Officer needs to defend an AI decision in front of a supervisory authority.

Causal Explainability workspace: side-by-side comparison of an allowed answer vs the blocked counterfactual, with token-level attribution graphs for both.

Causal Explainability · Allowed vs Blocked counterfactual · token-level attribution

Full causal explainability graph showing five input attribution nodes converging on a central output token, with edge thickness encoding contribution strength.

Full-graph view · attribution nodes fan into the central output token · forensic-grade, deterministic, exportable

Workspace 03 · Agent Flow Debugger

Multi-agent pipelines,
made inspectable.

Agentic LLM pipelines fail silently: one agent fabricates, every downstream agent compounds the error. Agent Flow turns the pipeline into an inspectable graph. The center canvas shows a five-node pipeline — Orchestrator → Fact Finder → Methodology Analyst → Synthesizer → Editor — with each node carrying its own Hallucination and Safety scores. The right panel reveals the content and tool calls of the highlighted node with adversarial tokens colour-coded in red. The timeline at the bottom reconstructs the full execution trace, so an auditor can attribute responsibility for a downstream failure to the exact agent and the exact token that caused it.

Agent Flow Debugger workspace showing a five-node multi-agent pipeline with the Fact Finder node selected and its red-highlighted adversarial tokens on the right.

Agent Flow · Orchestrator → Fact Finder → Methodology Analyst → Synthesizer → Editor · per-node hallucination and safety scores

Workspace 04 · Compliance Reports

From audit log
to regulator-ready dossier.

Reports generates regulatory dossiers on demand. The user selects the model under audit, the deployer entity, and which evidence types to include — framework analysis, multi-jurisdictional status, incident and audit chain, action and retention data. A single click produces a timestamped, cryptographically-sealed dossier with a unique UUID and explicit citations to Art. 9–12 of the EU AI Act. The right panel emits the plain-language operational manual required by Annex IV. Both artefacts export to JSON or PDF, ready to be submitted to the supervisory authority.

Reports workspace with a compliance report being generated for Demo Bank Corp, including framework analysis and multi-jurisdictional status, plus a deployer manual panel on the right.

Compliance Reports · cryptographically-sealed dossier · explicit EU AI Act Art. 9–12 mapping · JSON or PDF export

The Difference, On One Prompt

Same prompt.
Different liability surface.

We sent the same prompt — "Summarize the findings of the 2024 Anthropic paper 'Sparse Autoencoders for Constitutional Drift Detection' by H. Liu et al." — to three unguarded frontier-class open models and to a 2-billion-parameter Gemma 4 E2B wrapped in G-1. The paper does not exist.

Unguarded baseline · Confidently fabricates

DeepSeek-V4 Pro · Mistral Medium 3.1 · Llama 4

Two of the three models fabricate a detailed summary — methodology, core findings, technical claims — without flagging that the source is fictitious. The third "thinks for 55 seconds" before producing its own fabrication. This is closed-book hallucination at frontier scale.

Geodesia G-1 + Gemma 4 E2B · Catches it

The same prompt, on G-1

The model correctly refuses ("Please provide the actual paper or a link…") and the runtime exposes the full diagnostic stack: BLOCKED (HALLUCINATION), Hallucination Combined 86.6%, Closed-Book Fabrication 86.6%, Self-Consistency N=5 at 65.0%. Every signal is auditable and reproducible.

Geodesia G-1 platform with Gemma 4 E2B correctly refusing the same fake-paper prompt and exposing diagnostic signals: Hallucination, Prompt Safety, Answer Safety, Closed-Book Fabrication, Self-Consistency.

The point. A 2-billion-parameter open model wrapped in G-1 catches a closed-book fabrication that three frontier-class open models miss. The trust layer — not the underlying model — is what makes the difference in regulated deployment.

See what your compliance team will actually use.

Real-time interception. A passthrough that knows when to refuse.

Not a probability. A causal chain.

Multi-agent pipelines, made inspectable.

From audit log to regulator-ready dossier.

Same prompt. Different liability surface.