Case File 05 · Documents
Touchless contract processing for a legal team
Representative — corporate legal
▸ ENGAGEMENT DETAILS
Representative scenario modeled on published industry benchmarks. Not a named client.
The Challenge
A representative corporate legal team reviews a high volume of third-party paper, and the first pass is the bottleneck.
Manual first-pass review is slowest exactly when it matters most — during diligence, when volume spikes and deadlines do not move. Deloitte's benchmark for intelligent document processing in finance is 60-80% less processing time and 50-70% lower cost, with extraction accuracy commonly reported in the 90-99% band and heavily workload-dependent.
The risk in this scenario is not throughput alone. It is a missed clause in a long agreement, which is what a consistent, auditable first pass is meant to prevent.
Our Agent Solution
We deploy DocuMind against the legal team's own document management system. Extraction and clause models are tuned and evaluated on the client's own historical corpus under a signed data agreement and validated against senior attorney review. The system produces a structured first pass — parties, dates, obligations, and deviations from the client's standard positions — with every extracted field linked back to its source span, so a reviewer can verify it in one click. Risk flags are ranked for attorney attention; the system does not approve, sign, or advise.
Ingestion Agent
Accepts contracts in PDF, Word, and scanned formats, performing OCR where needed
Extraction Agent
Identifies key clauses, dates, parties, and obligations, linking each field to its source span
Analysis Agent
Compares against the client's standard positions and surfaces deviations
Compliance Agent
Checks against the client's own policy set and the regulatory requirements in scope
Output Agent
Generates the structured summary and a ranked risk list for attorney review
Implementation Timeline
A representative 14 weeks delivery path, from discovery to handover.
Legal Domain Assessment
Analyzed contract types with the legal team, defined the extraction taxonomy and the client's standard positions
Weeks 1-3
Architecture Design
Designed the multi-agent pipeline, the source-linking model, and the attorney review workflow
Weeks 4-5
Build & Evaluation
Built the agents and tuned and evaluated extraction on the client's own historical corpus under a signed data agreement, validated against senior attorney review
Weeks 6-11
Integration
Integrated with the document management and matter management systems, with access controls mapped to existing roles
Weeks 12-13
Handover & Rollout
Phased rollout with an operator runbook, review-workflow training, and a documented rollback path
Weeks 14
Target ranges — and today's baseline
Both sets of figures are drawn from published research, not from VelocityMind client results. Targets are the ranges this scenario is scoped against; baselines describe where the status quo sits. Actual results depend on your data, systems, and scope.
Extraction accuracy (published ceiling)
Source · Industry IDP benchmarks — commonly 90-99%, workload-dependent
99%
Faster first-pass review
Source · Deloitte — IDP, 60-80% less processing time
70%
Lower processing cost
Source · Deloitte — IDP, 50-70% lower cost
60%
Where the baseline sits today
Published measurements of the problem, shown so the target ranges above can be read against something. These are not outcomes we delivered.
Of work hours are technically automatable today
Source · McKinsey MGI, 2023 — GenAI technical automation potential
60–70%
What ships — and what stays human
The artifacts you own at handover, and the decisions the agents never take.
▸ DELIVERABLES
What you receive
- Extraction and analysis agents running against your own document management system
- Clause taxonomy and standard-position library built with your legal team
- Evaluation report scoring extraction against senior attorney review, per contract type
- Source-linked review interface, access controls mapped to existing roles, and an operator runbook
- All source code in your repository, with the deployment owned by your team
▸ GUARDRAILS
What stays human
- The system produces a first pass. Attorneys make every call — nothing is approved, signed, or advised by an agent.
- Every extracted field links to its source span in the original document, so no output has to be taken on trust.
- Accuracy is stated as a published ceiling, not a commitment; your own evaluation against attorney review sets the operating threshold.
- Client documents stay inside the environment named in the data agreement and are not used to train shared models.
Method and sources
This scenario is constructed, not reported. Every figure on this page traces to one of the published sources below.
IDP: 60-80% less processing time, 50-70% lower cost
Deloitte
Extraction accuracy up to ~99%, commonly 90-99% and workload-dependent
Industry IDP benchmarks — treat as a ceiling
60-70% of work hours are technically automatable with GenAI
McKinsey MGI, 2023
GenAI value to banking $200B-$340B/yr (9-15% of operating profit), much of it document and knowledge work
McKinsey, 2023
Ranges are published industry benchmarks. They describe what programs of this shape have achieved elsewhere, not a commitment for your environment — the range we would scope against for you is set after discovery.
A representative perspective
“Contract review that took hours now takes minutes for a first pass, with clause-level risk flags — and our attorneys still make the final call.”
▸ NEXT STEP
Run this against your own workflow
Send us one workflow, its monthly volume, and the systems it touches. We reply within one business day with a first read on whether an agent system is the right tool, the agent shape we would propose, and an engagement range.
No commitment. We reply within one business day.