VaultIQ installs directly inside your Veeva Vault and answers questions in plain English — with citations — using only your own documents. Your documents never leave your infrastructure.
Veeva Vault holds your SOPs, protocols, regulatory submissions, and quality records. Getting answers out of them is still manual, slow, and unreliable.
Standard Vault search gives you a list of documents. Someone still has to open each one, read it, and find the relevant paragraph — on every query, every day.
A search for "storage temperature" misses a document that says "maintain between 2°C and 8°C." VQL FIND matches words, not intent.
Every commercial AI document tool asks you to upload your documents to their cloud. In pharma and life sciences, that's a non-starter for IT and compliance.
An AI that makes up answers is worse than no AI at all. If you can't trust every sentence, you still have to read the document — the tool adds friction, not value.
Four steps. All running inside your Vault and your cloud account.
A user types a question in plain English inside Vault. No query syntax, no special commands.
VaultIQ searches simultaneously by keyword (Vault VQL) and by meaning (AI vector search). Documents that match by intent appear even without exact keywords.
Results are filtered through Vault's own access controls. If a user cannot see a document in Vault, that document does not appear in the answer.
A plain-English answer is returned with numbered citations. Every sentence traces back to a specific document. Any sentence that can't be cited is removed before you see it.
VaultIQ runs entirely inside your cloud account. Switch providers by changing a single Vault Setting — no JAR rebuild, no redeployment.
Your documents stay in your Vault. Your embeddings stay in your cloud account. VaultIQ never touches either.
Only embedding vectors and routing metadata are stored in the vector index. Document text is never written anywhere outside Vault.
All Vault API calls run under the calling user's session token. Vault's own permission system governs what each user can see — the vector index is never a shortcut around permissions.
AI models are called at inference time with document snippets as context. No document content is retained by VaultIQ after the query is answered.
Vault makes outbound HTTPS calls to your cloud AI endpoints. No inbound connections required. No firewall rules to open inward.
Every feature exists because a pharma or life sciences team needs it — not because it's on a generic AI checklist.
Set monthly token limits per user or Vault role in Vault Settings — no code changes. Users see a clear message when their limit is reached; admins can monitor usage at any time.
Combines Vault VQL keyword search with semantic vector search simultaneously. Documents that match by meaning appear even without exact keywords.
Every sentence must cite a document snippet. Sentences without citations are stripped automatically — the model cannot say something it cannot prove.
All queries run under the calling user's Vault session token. Vault's own permission system governs what each user can see — VaultIQ never elevates privilege.
"Show me documents expiring in the next 30 days" returns a formatted table from Vault metadata — document name, version, expiry date — with no LLM call needed.
Documents index automatically when they reach a configured lifecycle state. No manual step. New versions replace old index entries automatically.
Automatically routes queries to the right search strategy — document content, metadata attributes, or expiry — without the user learning any query syntax.
The JAR is the entire deployment artifact. No containers, no servers, no infrastructure to manage. Upload to Vault, configure settings, done.
Simple queries use a faster, cheaper model. Complex multi-document synthesis uses a more capable model. Cost is optimized per query — automatically.
Repeated queries skip the embedding call entirely — saving ~100–180ms per cache hit. Cache size and TTL are tunable via Vault Settings.
User queries are sanitized to prevent attempts to override system instructions through crafted input — role prefixes, HTML tags, and injection patterns are stripped.
When documents don't contain the answer, VaultIQ says: "I cannot answer this from the available documents." It does not fill gaps with training data.
We scope setup at under a week to give your cloud and Vault teams realistic time to provision infrastructure, complete internal change management, and run thorough smoke tests — without rushing.
A per-tenant subscription covers the license, the JAR, updates, and support. Your cloud AI costs are billed directly to your cloud account — VaultIQ does not mark these up.
License key issuance — cryptographically signed JWT, pasted into one Vault Setting
The JAR — versioned releases, backwards compatible, no rebuild required for config changes
Updates and support — new cloud provider support, Vault SDK compatibility updates
Cloud AI costs go to your cloud bill directly — AWS, Azure, or GCP. VaultIQ takes no cut.
cloud_provider__c to azure or gcp in Vault Settings and provide the corresponding endpoints and credentials. No JAR rebuild required. Azure uses Azure OpenAI + Azure AI Search. GCP uses Vertex AI (Gemini + text-embedding-004) + Vertex AI Vector Search.Runs inside your infrastructure. Live in under a week. Your documents stay yours.