For teams shipping AI to production

Make every AI session start with your team's standards.

Veriova is the production layer for teams already building with Claude, Cursor, Codex, ChatGPT, or Lovable. It gives every project one shared standard, one Ship Check, one handoff path, and one memory layer your whole team can build on.

Stop re-explaining your stack, standards, and edge cases in every session
Run Ship Check before code reaches production, not after something breaks
Turn AI-built prototypes into engineering handoff docs your team can actually use

Works with

ChatGPT (Actions API)Claude (MCP)Codex (AGENTS.md)Cursor (MCP)Windsurf (MCP)Any app (REST API)

Fast wedge

Run your first Ship Check before changing any team workflow.

Internal proof

Share score cards, context packs, and prototype briefs with stakeholders.

No rip-and-replace

Keep Claude, Cursor, Codex, and your current stack. Veriova layers on top.

Ship Check

Payments v2 · Claude-built · 13 criteria

Shareable
71/100

3 gaps before this is production-ready

Gaps · 3

Auth rate limiting not configured

Error handling & logging incomplete

Observability setup missing

Met criteria · 10

Input validationTypeScriptAuth headersHTTPS onlyDB indexesRate headersError messagesAudit logUnit testsDocs

What Veriova does

One loop that turns AI-built work into production-ready work.

Set the production bar. Run Ship Check on real work. Turn gaps into an engineering handoff. Save the decisions back into shared memory. That is the product, and it gets stronger every time your team uses it.

Wedge feature

Production Standards + Ship Check

Define your real production bar, run Ship Check against AI-built work, and show exactly what blocks release before engineering inherits the mess.

71/100Needs work

Payments v2 · Claude-built · 13 criteria

Auth rate limiting not configured

Error handling & logging incomplete

Observability setup missing

Input validationTypeScriptPaginationHTTPS onlyDB indexesRate headersError messagesAudit logUnit testsDocs

Memory

Save the standards, decisions, and architecture rules that Ship Check and handoff should keep using across every session.

Decision

PostgreSQL + pgvector for retrieval

Rule

Strict TypeScript across all services

Runbook

Zero-downtime deploy procedure

AI Config

Generate CLAUDE.md, Cursor rules, and Windsurf config from your team's reviewed memory and guardrails — one source of truth for every AI tool.

CLAUDE.md
.cursor/rules
.windsurfrules

Knowledge

Sync GitHub repos and docs so AI retrieval is grounded in the code your team actually ships, not hallucinated references.

veriova/apisynced
veriova/websynced
design-systemsynced

Team

Invite teammates via link. Shared context, standards, and API access — no key distribution.

H
A
S
M
+ invite

Governance

Detect stale or drifted memories before they mislead your AI. Scan for exposed secrets before they leave your workspace.

Secret Redaction

Every response leaving Veriova is scanned and redacted — AWS keys, JWTs, database URLs, and API tokens never leave in plain text.

Session Start

One short project instruction tells your assistant to call veriova_session_start at the start of every conversation, then use standards and Ship Check tools when needed.

How it works

Accumulate, enforce, bridge.

Three moves that turn scattered AI usage into a system your whole team relies on.

01

Accumulate team context

Connect Claude, Cursor, ChatGPT, or any AI tool via MCP or REST. Every decision, convention, ADR, and runbook stores in a shared layer — searchable by you and your AI in every future session. Connect via MCP and the session start tool runs automatically — no manual recall, no prompting. Context compounds. Repeated prompts disappear.

Works via MCP, REST API, or ChatGPT Actions

02

Enforce your production bar

Define org-wide criteria for what production-ready means. Score any AI-built feature — a prototype, an experiment, a vibe-coded PR — instantly. See exactly what's missing before anyone commits to ship. Share the readiness card with your team.

Shareable score card — URL you can send anyone

03

Bridge builders to engineering

Built something on Lovable or Base44 to prove an idea? Veriova turns your prototype into a structured spec — data model, user flows, edge cases, gaps — that engineering can actually work from. Non-technical builders included.

No GitHub required for the handoff flow

Why veriova

Why teams buy this instead of adding more prompts.

Most teams already have Claude, Cursor, and ChatGPT. What they do not have is a shared memory layer, a visible production bar, and a repeatable way to prove AI-built work is safe to ship.

CapabilityVeriovaManual reviewShip and hope
Automatic context injection via MCP (Claude, Cursor, Windsurf)
Instant readiness score on AI-built features
Shareable readiness card (URL you can send anyone)
Score against your own production criteria
Works across Claude, Cursor, ChatGPT simultaneously
Shared team context — no repeated prompts
AI Config generation (CLAUDE.md, cursor rules)
Built-in secret redaction on all outputs
Team invites & shared workspace

Manual review = hiring a developer to audit your AI-built code. Averages $500–$2,000 per feature and takes days.

Security

Enterprise-ready from day one.

Built with security-first principles so you can trust it with your most sensitive context.

Encryption & isolation

All data encrypted at rest and in transit. Each project is fully isolated with its own namespace.

Role-based access

Reader, Reviewer, Admin roles with project-scoped API keys. Superadmin for global oversight.

Audit logging

Every API call, memory change, and key event is logged with timestamps and actor attribution.

Secret Redaction

Outbound scanning scans every response leaving Veriova and redacts detected AWS keys, database URLs, JWTs, API keys, and other sensitive values.

Your team's context layer, ready in minutes.

Connect one AI tool, save the project rules you repeat most, and run a Ship Check on something your team already built. That is usually enough to know whether Veriova earns a permanent place in the workflow.