THE MAKER
The AI model
Drafts the report, retrieves the answer, or runs the check. It generates the work.
WE VERIFY AI WORK
The Verification Layer for AI Workflows
KnowledgeVerse sits between AI models and the systems where regulated work happens. Every output is validated, verified against your policies, and approved by a person before it becomes a decision.
VerifiedHuman-approvedContinuously optimized
UPSTREAM
AI Models
ClaudeGPTGeminiOpen weights
KNOWLEDGEVERSE
The Verification Layer
Validates every input, verifies every output against your Knowledge Map, and routes it to a person for approval before release.
AuditabilityTraceabilityOptimization
DOWNSTREAM
Enterprise Systems
Veeva VaultQMSERP / MESDatabases
01 · THE PROBLEM
AI is probabilistic. Regulated decisions must be deterministic.
A model can answer the same question two ways and sound right both times. That is fine for a draft. It is not acceptable for a regulated decision, where the same inputs checked against the same policies must always produce the same, defensible outcome. KnowledgeVerse adds a deterministic verification layer on top of any model: every output is checked against your policies and sources, resolved to one governed decision, and approved by a named person.
Accuracy alone is not enough. An answer can be fluent and even factually clean and still be wrong for your organization, because it ignores the policy, the roles, and the rules that govern a real decision. KnowledgeVerse is an enterprise AI verification and governance platform for regulated industries. It acts as a maker-checker layer that makes every AI output policy-compliant, auditable, and traceable, and delivers it as a workflow with a human sign-off, not a chatbot.
PROBABILISTIC
AI
Looks compliant.
Likely compliant.
Mostly compliant.
Deterministic
Verification
Verification
DETERMINISTIC
✓
One approved
decision
decision
THE CHECKER
KnowledgeVerse
Operates a Zero Trust Framework: nothing the model produces is assumed correct. Every input is validated and every output is checked against your Policies, Org Hierarchy, and sources, with each step logged.
THE APPROVER
Your team
A named person reviews the verified output, with every claim linked to its source, and signs off. Nothing is released without them.
WE SERVE REGULATED INDUSTRIES
PharmaBiotechMedical DevicesFinancial ServicesManufacturing
Regulatory affairs, quality, medical writing, pharmacovigilance, and the compliance functions that own the sign-off.
02 · HOW IT WORKS
Six steps. Every Workflow. No step skipped.
Every KnowledgeVerse Workflow follows the same verified path. You configure what goes inside each step. You never change the order, and you can never skip Verify or Review & Approve.
01
Sources
Your documents and data, checked for completeness before anything runs.
02
Verification Setup
The Policies, structure, and language every output is checked against.
03
Generate
Any AI model drafts the report, answer, or finding.
04
Verify
Every claim is checked against your sources and Policies. Passed, Flagged, or Failed.
Passed · Flagged · Failed05
Review & Approve
A named person reviews the evidence and signs off.
Named approver06
Export
The approved result goes where the work lives, with the audit trail attached.
Steps 2 to 5 run inside KnowledgeVerseevery step writes an immutable, timestamped audit record
What your Workflows are verified against
The Knowledge Map
Documents
Approved sources every claim must trace to
Policies
SOPs, regulations, and internal rules
Org Hierarchy
Who owns what, across business units, teams, and processes
Taxonomy
Your categories, codes, and controlled terms
Standard Instructions
Your house standards for every output
03 · PRODUCTS
One verification core. Two ways to use it.
Build verification into your own systems with Platform, or give business teams verified Workflows with Studio.
FOR DEVELOPERS AND SYSTEM INTEGRATORS
Platform
Verification APIs for teams building AI into regulated systems.
Extract: Structured fields from documents, in your schema.
Compare: Differences in format or content between documents.
Generate: Drafts, forms, and questionnaire answers grounded in your sources.
Search: Answers across your documents, with citations.
Verify: Check any AI output, from any model, against your Policies and sources.
POST /v1/verify
{
"output": "Root cause: operator deviation...",
"sources": ["deviation_2291.pdf"],
"policy_ids": ["SOP-QA-014"]
}
200 · status: flagged · confidence: 0.82
citations: 4 · rule_checks: 11 · audit_id: aud_8f31c- Every response carries citations, rule checks, and an Audit ID
- Works with any model, including your own
- Usage-based, per call
FOR QUALITY, REGULATORY, AND COMPLIANCE TEAMS
Studio
Verified workflows for business teams, built from ready-made Blueprints.
BLUEPRINT
CAPA Report
The template
WORKFLOW · V3
Line 3 CAPA
Your configured version
RUN
Run 0142
Each execution, with its audit trail
- Start from a Blueprint, not a blank page
- Six guided steps, no code
- Locked versions, so every Run is reproducible
- Review screen with every claim linked to its source
If the result feeds another system, use Platform. If a person reads it, approves it, and owns it, use Studio.
04 · BLUEPRINTS
Start from a Blueprint, not a blank page.
Blueprints are ready-made, verification-first workflows for regulated work. Choose one, configure it with your sources, Policies, and approvers, and publish it as your own Workflow.
CAPA ReportDeviation ReportChange Control ReportOOS ReportMarket Complaint ReportQuestionnaire and RFP ResponseForm CompletionDraftRegulatory Submission ComplianceStandards ConformancePolicy AdherenceVersion Change ReviewVerified Q&ATimelineStructured Extraction
Blueprints at work
Compliance CheckPHARMA
ICH Guidelines Check
Check whether a clinical report adheres to ICH E3 and CTD guidelines, marking every guideline as met or not met.
Built on: Regulatory Submission Compliance
Report GenerationMANUFACTURING
CAPA Reporting
Produce a complete CAPA report from investigation records, structured to your CAPA standard and template.
Built on: CAPA Report
Knowledge RetrievalOPERATIONS
SOP Knowledge Base
Answer operational questions grounded strictly in your controlled SOP library, with citations to the passage.
Built on: Verified Q&A
No Blueprint that fits? Every Workflow follows the same six steps. Bring your process and we will configure it.
Discuss your workflow05 · STUDIO
Built for the teams that own the sign-off.
Studio puts verification and approval where accountability already sits. Start from a Blueprint and most of the Workflow is already filled in.
Quality and regulatory teams
Own the Workflow end to end. Set the Policies, review findings, and approve in one place.
Medical writers and reviewers
Generate to your template, then verify against the guideline before the document leaves your desk.
Operations and knowledge owners
Give teams verified answers from your controlled SOP library, and see what every answer came from.
IT and platform teams
Connect sources and systems of record, set hosting and retention, then hand Studio to the business. Or build directly on Platform.
06 · WHY KNOWLEDGEVERSE
Frontier models generate. KnowledgeVerse verifies, governs, and proves.
Claude Enterprise and ChatGPT Enterprise are excellent general-purpose assistants. The capabilities below are not available in them out of the box. KnowledgeVerse provides them as standard, with any model underneath.
Maker-checker approval
Every output is held until a named reviewer signs off. Overrides need a written reason.
Verification of any model
Check outputs from Claude, GPT, Gemini, or your own model against the same Policies.
Evidence for every rule
Each requirement is marked Passed, Flagged, or Failed, with the source behind it.
Decision lineage, not chat logs
Sources, Policies, model version, reviewer, and decision, all under one Audit ID.
Reproducible Runs
Every Run is pinned to a locked Workflow version, so results stay defensible after models change.
Never pay twice for the same work
i-Check reuses prior results for the same or similar tasks and produces only what is new.
07 · TECHNOLOGY
Built for documents that are too large, too messy, and too regulated for a prompt.
Large, complex data
Regulated work arrives as 300-page dossiers, scanned batch records, and tables that span pages. KnowledgeVerse processes the whole document set, not a sample.
- Full-document coverage instead of human sampling
- Layout-aware parsing of tables, forms, and scans
- Cross-document reasoning across a controlled corpus
i-Check optimization
When a new task and its constraints match work already done, i-Check reuses the existing result and produces only the incremental output needed.
- Consistent, repeatable results
- Lower cost on repeat work
- Faster turnaround
Tiered Verification
Checks start with the fastest reliable method: rules, then pattern matching, then a focused decision model. A frontier model or a person steps in only when confidence is low.
- Frontier-level assurance
- A fraction of the cost
- Human attention where it matters
Integrations and hosting
Results go where the work lives, with the audit record attached.
- Veeva Vault, QMS, EDMS, PLM, ERP / MES, and ticketing
- Databases, APIs, and system exports
- KV Cloud or your own private cloud
REFERENCE ARCHITECTURE
INGEST
Sources
Controlled documents, templates, databases and APIs
GATE
Input validation
Format checks, prompt-injection screen, redaction and PII
CORE
Verification layer
Knowledge Map: Documents, Policies, Org Hierarchy, Taxonomy, Standard Instructions
REVIEW
Human approval
Named approvers, override with reason
RELEASE
Approved output
In-app decision, system of record, audit trail export
every stage emits an immutable, timestamped audit record
08 · RESEARCH
Our research on what models
know, and what they do not.
The verification layer exists because model self-knowledge is unreliable. Our published work measures where that boundary sits, and how to optimize around it.
Paper 1
Line of Duty
Evaluating LLM self-knowledge via consistency in feasibility boundaries.
Read more →
Paper 2
Mirage of Mastery
Memorization tricks LLMs into artificially inflated self-knowledge.
Read more →
Paper 3
TeXpert
A multi-level benchmark for evaluating LaTeX generation in language models.
Read more →
Paper 4
i-Check
An idempotence-driven optimization framework for AI agents in enterprise workflows.
Read more →
09 · FAQ
Questions we get asked first
When I already have a frontier model (enterprise edition), why would I need KnowledgeVerse?
The model generates the work. KnowledgeVerse makes it usable for regulated work:
- Large documents: processes full 300-page dossiers and scans, not a sample.
- Lower token cost: i-Check reuses prior work and produces only what is new; a frontier model runs only when confidence is low.
- Verified: every claim checked against your Policies and sources, marked Passed, Flagged, or Failed with evidence.
- Human sign-off: a named person approves before release.
- Audit trail: sources, Policies, model, and approver under one Audit ID.
- Any model: the model is your replaceable cartridge. You no longer depend on the model.
How is KnowledgeVerse different from Claude Enterprise or ChatGPT Enterprise?
Those are general-purpose assistants. KnowledgeVerse is the verification layer around any model: it checks every output against your Policies and sources, routes it to a named approver, and records the full decision lineage. It works with Claude, GPT, Gemini, or your own model.
What is the difference between a Blueprint and a Workflow?
A Blueprint is a ready-made template for a regulated task, such as a CAPA report. A Workflow is your team's configured version of it, with your sources, Policies, and approvers, published in locked versions. Each time you use a Workflow, that is a Run.
How is pricing structured?
Usage-based. A prepaid annual tier buys a balance of transactions, where a transaction is one page processed or one API call. Rates depend on whether you host on KV Cloud or in your own private cloud.
Where does our data live, and is it used to train models?
Your documents stay inside your tenant and are never used to train a model. Data residency follows your deployment choice — a dedicated cloud region or your own infrastructure — and retention is configured per workflow, with deletion on your schedule.
Which AI models does KnowledgeVerse use?
We are model-neutral. Each workflow names a primary model and a health-checked backup drawn from the frontier providers, open-weight models, or a model you host yourself. Because verification sits outside the model, swapping models does not change the governance.
Can we deploy on-premise or in a private cloud?
Yes. KnowledgeVerse runs as a dedicated single-tenant cloud deployment, in your own VPC, or fully on-premise for air-gapped environments. The verification layer, the Knowledge Map and the audit store deploy together.
Is there a human in the loop, and what does the audit trail contain?
Always. KnowledgeVerse is a maker-checker layer, not an autonomous approver — a governed decision is released for your reviewer to accept, and every guideline result carries the source location behind it. The audit trail records the input, the guardrail results, the model and version, each verification check, every correction-loop pass and the human sign-off, with timestamps.
What does it integrate with?
Outputs route into Veeva Vault, RIM and EDMS platforms, QMS, PLM, ERP / MES and ticketing systems, or return in the app. Inputs can be pulled from document stores, databases, APIs and system exports. Where a native connector does not exist, the API covers it.
How long does implementation take?
A pilot on a readymade solution typically runs in weeks, not quarters: pick the use case, connect the sources, load your policies, roles and SOPs, then run on real work. Most of the configuration is already done in the template — the effort is in your verification documents, not in build.
10 · CONTACT
See it on your own workflow.
Pick a Blueprint such as CAPA Report or Regulatory Submission Compliance, connect your sources, and run a pilot on real work. The audit trail comes with it.