Connect target
Existing RAG app, agent workflow, custom HTTP target, or the RAGhelm Pinecone reference runtime.
AI release control for private RAG and agents
Fail-closed release approval for AI systems over proprietary data.
Gateproof connects to a RAG or agent target, captures release evidence, applies versioned policy, and emits a scorecard, run manifest, and redacted proof bundle before production.
Category: Gateproof is the CI release gate for private AI systems. It blocks unsafe RAG and agent changes before production.
Release gate
Versioned evidence
Disclosure-safe proof
Backend-neutral
From candidate build to release decision.
Gateproof does not replace RAG apps, agent frameworks, vector databases, or observability stacks. It sits at the release moment: after a candidate exists, before it reaches production.
Capture evidence
Golden examples, traces, retrieval records, tool calls, latency, cost, policy, and hashes.
Apply policy
A deterministic evaluator runs over versioned evidence. AI behavior may be stochastic. Gate logic is not.
Emit artifacts
Scorecard, manifest, release decision, and redacted proof bundle for stakeholders.
Enforce gate
CI/CD, dashboard, or human review ships, blocks, or requires approval.
The release path in one view.
Gateproof turns private AI release review into a reproducible chain of target state, evidence, policy, artifacts, and enforcement.
RAG or agent target
Existing application, HTTP endpoint, agent workflow, or RAGhelm reference runtime.
Capture layer
Golden examples, traces, retrieval records, tool calls, latency, cost, policy, and hashes.
Deterministic gate
Versioned thresholds, freshness rules, judge configuration, and fail-closed disclosure checks.
Proof bundle
Scorecard, manifest, release decision, redacted failures, and artifact hashes.
CI or review gate
Ship, block, or require approval with an archived approval record.
Not another eval dashboard.
Incumbents generate useful evidence. Gateproof governs the release decision and creates a disclosure-safe artifact stakeholders can accept.
Tracing and evals
Useful evidence generation, experiments, datasets, prompts, production monitoring, and debugging.
RAG and agents
Application construction, orchestration, retrieval, memory, tool calls, and runtime behavior.
Generic enforcement
Build rules, workflow checks, approvals, and deployment controls across software systems.
AI release approval
Normalize evidence, apply versioned policy, separate private evidence from public-safe proof, and archive the approval record.
Sample proof artifacts.
Static samples make the product tangible while keeping sensitive evidence out of the public page.
Readiness scorecardHuman release decision with regression, latency, freshness, and disclosure checks.
Run manifestAudit record of target, corpus, policy, evaluator, models, thresholds, and artifact hashes.
Redacted failuresDisclosure-safe examples that explain why a release ships, blocks, or needs review.
AI teams can build faster than platform, security, and compliance can approve.
The buyer needs one artifact that says what changed, what failed, what evidence is private, and why the release can ship or must be blocked.
✓
Corpus updates create release risk
Answers can cite stale or superseded policies when the knowledge base changes.
✓
Model and prompt changes hide regressions
Teams can cut cost while increasing unsupported claims or failures on golden examples.
✓
Agents expand the blast radius
Tool calls can drift outside approved workflows unless release evidence is reviewed.
✓
Private data needs disclosure control
Metadata and filter bugs can expose tenant-specific context. Proof bundles need redaction by design.
Clear wedge. Credible pilot path. No overclaiming.
Gateproof governs AI-specific release evidence and approval. It normalizes evidence across tools, applies versioned policy, separates private evidence from public-safe proof, and archives the approval record.
Private-data AI teams
AI platform and security-conscious engineering teams at regulated, vertical SaaS, or private-data companies.
4-week engagement
One production RAG or agent target, 30 to 100 golden examples, one CI gate, and one evidence report.
CLI, dashboard, CI gate
Connect one target, emit scorecard and manifest, render proof bundle, and block a bad release.
Accepted evidence format
Policy packs, failure-pattern library, release-path integrations, and trace-to-score loops without exporting private data.
$20k-$50k
Target design-partner pilot range from the financing memo.
$75k-$150k
Target annual private deployment range after conversion.
90 days
Goal: paid pilots or LOIs, quantified ROI case, customer quote, and repeatable ICP.
Founder-market fit
Built by an AI infrastructure engineer focused on private-data RAG reliability.
Gateproof is founder-led and grounded in the same reliability posture behind RAGhelm: reproducible architecture, explicit release evidence, and investor-ready engineering discipline.
Production reliability posture
Release gates, deterministic checks, evidence artifacts, and audit-friendly workflows instead of subjective model vibes.
Open-source reference path
RAGhelm is the Pinecone-first reference runtime and proof harness. Gateproof is the commercial release-control layer.
Near-term focus
The next milestone is paid proof that buyers will let Gateproof sit in the release path.
Investor and design-partner access
Request the brief or discuss a pilot.
Use this form for investor diligence, design-partner conversations, or early buyer intros. The public site keeps claims tight and routes sensitive material to direct follow-up.
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Do not send proprietary customer data through this form. Sensitive diligence materials and pilot data should be shared directly after mutual agreement.