DMS and matter workspaces
Matter files, emails, pleadings, notes, billing context, and client records stay inside matter permissions.
Legal Services Agentic OS Control Plane
Legal AI agents that turn matter objectives into privilege-safe execution
VDF.AI sits above DMS, contracts, matter systems, precedents, e-discovery, data rooms, and approved research sources. State the legal objective and the OS activates the right agents, tools, citation checks, ethical-wall controls, and reviewer sign-off while keeping privileged work product under your control.
6Legal workflows across contracts, research, diligence & discovery
0DMS, matter, research or e-discovery migrations required
100%On-premise or sovereign cloud for privileged data
Every runCited, matter-scoped, reviewed and audit logged
Built for confidentiality
Attorney–Client Privilege GDPR EU AI Act ISO 27001 SOC 2 Ethical Walls
Legal control plane
Matter intelligence in. Privilege-safe control plane. Cited execution out.
VDF.AI sits above DMS, contracts, matter systems, precedents, billing, e-discovery, data rooms, and authorized research sources. It coordinates legal agents while preserving privilege, ethical walls, and matter-level access.
01 Intelligence in 02 Control plane 03 Agents and tools 04 Execution out
Intelligence in
Contracts and playbooks
Agreements, clause libraries, fallback positions, negotiated terms, and risk positions are indexed with provenance.
Precedents and authorized sources
Firm know-how, memoranda, model documents, and approved research sources are retrieved with citations.
Data rooms and e-discovery sets
Large document sets are classified, summarized, and prioritized without leaving the legal data boundary.
Conflicts, policy and regulatory feeds
Client restrictions, conflicts rules, retention policy, and jurisdictional requirements define the agent boundary.
Live governed execution
VDF AI - Legal - Model agnostic - Sovereign cloud - Any LLM
Legal Services Agentic OS Control Plane
Objective engine, privilege controls, citation grounding, agent registry, memory, and feedback across contracts, research, diligence, discovery, and drafting.
Legal objective engine
State the target: review contracts faster, prepare diligence, research a point, draft first-pass language, or reduce discovery cost.
Privilege and ethical-wall gates
Matter permissions, client confidentiality, review rules, and citation requirements are enforced before output is used.
Orchestration
Matter agent router
Routes work by client, matter, privilege status, jurisdiction, source authority, deadline, and reviewer requirement.
Contract review pod
Extracts clauses, flags deviations, summarizes positions, and prepares red-flag reports for lawyer review.
Legal research pod
Searches authorized sources and internal know-how, then returns grounded answers with cited authorities.
Diligence and discovery pod
Classifies large document sets, surfaces issues, and prepares structured summaries with review status.
Drafting and matter-knowledge pod
Drafts memos, clauses, correspondence, and matter updates from approved templates and evidence.
Sovereign legal knowledge fabric
Runs on-premise or private cloud so client documents, privileged communications, and work product remain controlled.
Precedent and decision memory
Approved positions, reviewer decisions, citations, and matter lessons become reusable knowledge without breaking walls.
Execution out
Clauses extracted
Key terms, deviations, obligations, and risks are captured with source links.
Research cited
Answers cite approved sources and expose the reasoning chain for lawyer review.
Diligence summarized
Data-room issues are converted into structured, reviewable summaries.
Discovery prioritized
Document populations are classified and routed for review with defensible logs.
Drafts reviewed
First-cut memos, clauses, and correspondence move to a named reviewer.
Matter knowledge retained
Closed-matter lessons and approved arguments are captured for future matters.
From objective to outcome
How legal operations transform
The control plane turns matter objectives into privilege-safe work with source citations, reviewer sign-off, and an audit trail for supervision.
- 1
Your matter systems stay
DMS, contracts, e-discovery, data rooms, precedents, billing, and research subscriptions remain the source of truth. The OS works above them.
No DMS replacement - 2
The legal objective defines scope
Review a contract set, summarize a diligence room, research a point, draft a memo, or prepare discovery batches. The OS maps data, access, citations, and reviewers.
Matter-led planning - 3
Agents respect privilege boundaries
Contract, research, diligence, discovery, and drafting agents only retrieve what the user can access, with ethical walls and approval gates enforced.
Privilege-safe autonomy - 4
Outputs are cited and reviewable
Every clause finding, research answer, diligence issue, and draft is linked to source evidence and preserved with the reviewer decision.
Defensible by design
The Industry Challenge
The AI dilemma in legal services
Legal work is document work — contracts, filings, due-diligence rooms, and decades of precedent. AI could transform review and research, but the data is privileged, and a single confidentiality breach or fabricated citation can end careers.
01
Privilege & Confidentiality
Sending privileged material to a hosted LLM risks waiving privilege and breaching client confidentiality and professional-conduct obligations.
02
Conflicts & Ethical Walls
Information must stay within the right matter and team. AI tools without matter-level access controls threaten ethical walls and conflict boundaries.
03
Hallucination Risk
Public chatbots invent case law and citations. In legal work, an unverified, fabricated authority is a professional and reputational catastrophe.
04
Review Workload
Contract review, due diligence, and e-discovery consume enormous associate hours. Manual review doesn't scale to today's document volumes.
The VDF AI Solution
Citation-grounded AI your risk partner will approve
Data Sovereignty
Complete Data Sovereignty
Privileged data never leaves the firm.
Deploy VDF AI entirely on-premises or in your private cloud. No external API calls. No matter files, contracts, or privileged communications traveling to third-party servers — and nothing training an external model. Confidential material stays exactly where your risk and IT teams require it.
"Our risk partner approved it once she understood privileged material never leaves our control. That was the whole conversation."
100%
On-Premises Deployment
Privilege-preserving by design
Air-gap readyCustomer-managed keysNo model training on your data
Trust & Governance
Citations, Ethical Walls & Audit Trails
Defensible by design.
VDF AI provides the governance infrastructure legal risk teams demand:
- Citation-Grounded Output — answers generated from your documents and authorised sources, cited back to source text
- Matter-Level Access — agents only retrieve what the user is entitled to see; ethical walls enforced via RBAC
- Complete Audit Trails — every retrieval and response logged with full context for conflicts and supervision
- Human-in-the-Loop — review gates before any output is relied on or sent
- Explainability — show exactly which documents an answer was built from
Cited
Grounded & Auditable
Privilege · Ethical walls · Audit
Source citationsMatter-level RBACImmutable logs
Cost Control
Intelligent Cost Management
Leverage AI without runaway spend.
Firms need to justify AI against billable economics. VDF AI delivers:
- Per-Operation Cost Tracking — know exactly what each review or research task costs
- Model Routing Optimization — route routine review to small models, reserve frontier models for complex analysis
- Budget Controls — set limits by practice group, matter, or client
- ROI Reporting — tie AI assistance to review hours saved and realisation
- 40–60% Cost Reduction — compared to traditional cloud AI approaches
40–60%
Cost Savings
vs. hosted cloud alternatives
Per-op trackingTier-aware routingBudget guardrails
Where it pays back
Use cases for legal services
VDF AI agents and tools
Improve legal workflows with agent-tool networks
Each workflow combines a focused legal agent pattern with the tools needed to retrieve authorized evidence, verify citations, draft documents, route reviewer sign-off, and preserve defensible audit records.
Lawyer reviewed
Contract analysis and review
Extract clauses, compare to playbooks, flag deviations, and prepare risk summaries with source citations.
Document Analysis Agent
OCR ExtractionCitation VerifyReport Compile
Explore workflow Citation grounded Legal research
Search firm know-how and authorized sources, then return cited answers with verification and reviewer controls.
Enterprise Search Agent
Private RAGSource CrosscheckCitation Verify
Explore workflow Diligence assist Due diligence
Summarize data-room documents, surface liabilities, and structure issues for associate and partner review.
Risk Classification Agent
OCR ExtractionCSV AnalyzerReport Compile
Explore workflow Defensible review E-discovery review
Classify, prioritize, and summarize document sets while preserving review status and audit evidence.
Document Analysis Agent
CSV AnalyzerExecution TraceAudit Trail
Explore workflow Human sign-off Drafting assistance
Draft memos, clauses, and correspondence from templates, cited context, and matter instructions.
Writing Assistant Agent
Document GeneratorCitation VerifyHuman Approval
Explore workflow Knowledge reuse Matter knowledge management
Convert closed matters, approved positions, and lessons learned into access-controlled institutional knowledge.
Record Keeping Agent
Private RAGPermission CheckAudit Trail
Explore workflow Under the hood
Technical specifications for legal services
| Requirement | VDF AI Capability |
|---|---|
| On-premise deployment | Full on-premises, private-cloud, sovereign-cloud, or air-gapped deployment options |
| Privilege & confidentiality | Privileged data stays in-house — no external API calls, no training on your documents |
| Citation grounding | Answers generated from your documents & authorised sources, cited back to source text |
| Private RAG | Matters, contracts, precedents & know-how stay on-premise inside your governed vector-store boundary |
| Ethical walls / RBAC | Matter- and client-level access controls enforce conflicts boundaries & ethical walls |
| Model routing | Tier-aware routing keeps routine review on smaller models — frontier models reserved for complex analysis |
| Audit logs | Immutable audit logs for prompts, retrievals, tool calls & responses — SIEM export & long-term custody |
| Integration examples | Document management (iManage / NetDocuments-style), practice management & e-discovery platforms via MCP adapters |
| Encryption | At-rest and in-transit, customer-managed keys |
| Authentication | SSO, LDAP, Active Directory, MFA |
| Uptime SLA | 99.9% (Enterprise tier) |
ROI Snapshot
What changes after rollout
−60%
Contract & document review time
40–60%
Lower AI operating costs vs. cloud
10×
Faster document processing
−50%
Due-diligence turnaround
FAQ
Questions legal teams ask
How does VDF.AI preserve attorney–client privilege and confidentiality?
VDF.AI deploys fully on-premise, so privileged communications, matter files, and client documents never leave your firm's perimeter or train an external model. There are no third-party API calls. Combined with strict matter-level role-based access, ethical walls, immutable audit logs, and customer-managed encryption keys, the platform keeps privileged material inside your control — which is what makes generative AI defensible for a law firm or in-house legal team.
Can VDF.AI enforce ethical walls and conflict-of-interest boundaries?
Yes. Access is scoped at the matter and client level, so an agent only retrieves documents a given user is entitled to see. Ethical walls are enforced through role-based access controls, and every retrieval is logged — giving risk and compliance teams an auditable record that confidential information stayed within the right boundary.
Does VDF.AI hallucinate case law, and how is output verified?
VDF.AI is a retrieval-grounded platform: answers are generated from your own documents and authorised sources, with citations back to the source text, rather than from the model's memory. Every output is traceable to the documents it relied on, and human-in-the-loop review is built into the workflow — so lawyers verify before anything is relied upon, eliminating the fabricated-citation risk that comes from ungoverned public chatbots.
Why on-premise AI instead of a hosted cloud provider for legal services?
Hosted providers require sending prompts and document fragments to third-party infrastructure. For law firms and legal teams, that risks waiving privilege, breaching client confidentiality obligations, and conflicting with data-residency and professional-conduct rules. On-premise AI keeps privileged and client data inside your boundary — no third-party access, no cross-border transfer questions, no surprise terms-of-service changes.
Ready to deploy privilege-safe legal AI?
Talk to our team about your review, research, and confidentiality requirements.