Legal Services / Agent Consulting · Training · Ongoing Implementation Support

From facts to fulfillment,
make the evidence behind legal work
and its handoffs clearer.

Provide AI transformation consulting, training and ongoing implementation support for lawyers and legal services teams. Map out core work, introduce AI-assisted material organization, legal research and document verification, and produce evidence matrices, citation indexes and verification checklists, with clear lawyer confirmation steps.

Let's talk about your industry and AI transformation ↗

Full workflow: fact intake → issue framing → legal and evidence research → drafting → review and advancement → execution tracking. The example below is at step 3.

Full value flow / six nodes · eighteen tasks

From client needs to execution tracking, complete the six steps of legal services

First receive the facts and define the problem, then research, draft, review, and advance, and finally sign or submit and track execution. Each step includes three tasks; click a node to view inputs, lawyer confirmations, and handoff deliverables.

Current example: Step 3 · Regulation / case law / evidence research | Task 3.3 Evidence organization

How does a set of attachments become a reviewable evidence matrix?

Input　Scanned materials, originals and records of how they were obtained.

AI actions　Parse page order and available metadata, flag missing pages, contradictory statements and items pending proof, binding each finding to the source page number.

Lawyer confirmation　Combining the original documents and applicable procedures, verify authenticity, legality, relevance, admissibility and probative value.

Get　An evidence matrix and a table of gaps and additional evidence needed. These clarify what evidence to gather, reduce time spent searching attachments and support the proposed legal approach.

View Step 3: Research milestones and three tasks ↓

Value flowCapability networkTechnology stack

Work path, professional capabilities, and technology support each other

The value flow organizes work from requirements to execution; capabilities such as evidence review, source verification and version control are reused across tasks; OCR, retrieval, models and rule scripts provide concrete support for these capabilities.

The lawyer decides legal characterization, evidence evaluation and solution trade-offs. Each AI output enters the review and handoff for the corresponding task.

01Receiving customer needs and factsClarify the engagement goal and establish a traceable foundation of facts and materials.

Work inputs Customer interviews, event records, source materials and entity information

1.1Interview objectives and the sequence of events

Required capabilities: Listening, fact verification
AI and technical actions: Extract timelines from transcripts by person and date
Human confirmation: The lawyer reviews the original words and confirms the contradictions with the client
Work outputs: Engagement summary, timeline
Quality changes: Separate facts from inferences
Efficiency changes: Reduce note-taking

1.2Register the source of materials and the time obtained

Required capabilities: Evidence organization, permission control
AI and technical actions: OCR recognizes the content of scanned documents; the file parser reads available metadata and binds the original file; the model extracts source statements and acquisition time
Human confirmation: Lawyer verifies acquisition method, authorization, confidentiality requirements, and access scope
Work outputs: Material catalog, sources, and permission records
Quality changes: Source and acquisition method traceable
Efficiency changes: Reduce data entry and retrieval

1.3Check conflicts of interest and agency boundaries

Required capabilities: Risk assessment, professional communication
AI and technical actions: Search historical matters by entity and flag conflicts
Human confirmation: The lawyer decides whether to take on the case and the scope of authorization
Work outputs: Conflict records, supplementary document forms
Quality changes: Clear delegation boundaries
Efficiency changes: Reduce repeated verification

Advancement criteria After the lawyer confirms the engagement boundary and key facts, move into problem definition.

02Legal issues and scopingClarify legal issues, applicable premises, and scope of work.

Work inputs Engagement summary, timeline, contract, and registered materials

2.1Break down the legal relationships and points of dispute

Required capabilities: Analysis of legal elements
AI and technical actions: The model generates an issue tree bound to factual sources
Human confirmation: Lawyer confirms legal characterization and priority
Work outputs: Issue tree
Quality changes: Problem scope is clear
Efficiency changes: Reduce manual extraction

2.2Determine jurisdiction and procedural stage

Required capabilities: Jurisdiction assessment, process management
AI and technical actions: The model extracts locations, agreements and dates from contracts and event records to form a connection point table
Human confirmation: Lawyers separately determine jurisdiction, applicable law, procedural stage and relevant timing
Work outputs: Scope table, connection point records
Quality changes: Jurisdiction and applicable law premises are clear
Efficiency changes: Reduce scope rework

2.3Plan research and evidence needs

Required capabilities: Research design, risk assessment
AI and technical actions: The model lays out research routes and gaps by issue
Human confirmation: Lawyer confirms review thresholds and client decision items
Work outputs: Research plan
Quality changes: Evidence requirements complete
Efficiency changes: Reduce aimless retrieval

Advancement criteria After the lawyer confirms the issues in dispute, jurisdiction and research scope, conduct the research.

03Regulatory/case law/evidence researchGive both legal judgment and evidence evaluation a basis that can be reviewed.

Work inputs Issue tree, research plan, primary evidence and official legal texts

3.1Retrieve and verify legal source versions

Required capabilities: Legal source research, version identification
AI and technical actions: The legal database searches by jurisdiction and issue, and the model extracts promulgation, effective, amendment, repeal and transitional provisions and locates the original text
Human confirmation: Lawyers verify the hierarchy of authority, scope of application and intertemporal rules against official texts
Work outputs: Legal source cards, versions, and applicable-time table
Quality changes: Legal source versions and applicable basis can be reviewed
Efficiency changes: Reduce cross-database comparison

3.2Compare the court's reasoning with the facts of this case

Required capabilities: Legal reasoning, case comparison
AI and technical actions: Retrieval tools find similar and unfavorable cases, and the model extracts the court's reasoning and locates the paragraphs
Human confirmation: The lawyer verifies the full text, judgment status, court level, case type and its persuasive or binding effect in the relevant jurisdictions, and compares key factual differences
Work outputs: Case comparisons, citation index
Quality changes: Reasoning basis and case applicability boundaries are reviewable
Efficiency changes: Reduce excerpts

3.3Review the evidence chain and the purpose of proof

Required capabilities: Evidence review, source verification
AI and technical actions: The document parser reads available metadata and page order; the model flags missing pages, contradictory statements and items to be proven, and binds them to the original page numbers
Human confirmation: Lawyers verify authenticity, legality, relevance, admissibility and probative value against the original documents, the acquisition process and applicable procedures
Work outputs: Evidence matrix, gaps and supplementary evidence table
Quality changes: Evidence weaknesses and directions for additional proof are clear
Efficiency changes: Reduce searching through attachments

Advancement criteria After review of applicable legal basis and evidence gaps, a plan or supplementary evidence arrangement is formed.

04Solution or document draftingOrganize facts, legal basis and client goals into a reviewable proposal.

Work inputs Confirmed facts, legal source cards, evidence matrix, and client goals

4.1Draft legal document structure and argumentation

Required capabilities: Document expression, argument organization
AI and technical actions: The model generates a draft citation locator based on confirmed facts and legal sources
Human confirmation: Lawyers review every claim and professional judgment
Work outputs: Documents, citation tables
Quality changes: Argument matches completely
Efficiency changes: Reduce orchestration

4.2Compare terms or handling strategies

Required capabilities: Solution design, risk trade-offs
AI and technical actions: The model lists options by obligation deadline and evidence
Human confirmation: The lawyer determines the bottom line and the customer makes the choice
Work outputs: Solution risk table
Quality changes: Clear trade-offs
Efficiency changes: Reduce organizing multiple options

4.3Verify the entity, amount, date and attachments

Required capabilities: Detail validation, version control
AI and technical actions: Rule scripts scan numbers, definitions and cross-references
Human confirmation: The lawyer returns to the original documents to confirm the differences
Work outputs: Validation report, revised version
Quality changes: Reduce mechanical errors
Efficiency changes: Reduce page-by-page comparison

Advancement criteria Once claims, citations and key details are verified, it moves into review and progression.

05Review/negotiation or procedural advancementPut term trade-offs, negotiation authority and procedural materials into action.

Work inputs Revised documents, solution risk table, customer authorization and procedural requirements

5.1Review revised terms

Required capabilities: Risk identification, version comparison
AI and technical actions: Document diff tools locate additions and deletions, and the model flags semantic changes
Human confirmation: The lawyer decides whether to accept or modify
Work outputs: Difference table, opinions
Quality changes: Substantive changes are hard to miss
Efficiency changes: Reduce version hunting

5.2Prepare the negotiation agenda and alternative wording

Required capabilities: Negotiation strategy
AI and technical actions: The model ranks issues based on client objectives and the risk table
Human confirmation: Lawyer confirms authorization, bottom lines and disclosure boundaries
Work outputs: Negotiation package
Quality changes: Clear permissions
Efficiency changes: Reduce pre-meeting preparation

5.3Prepare procedural materials and deadline checks

Required capabilities: Procedural management
AI and technical actions: Assemble packages per the procedure checklist and calculate deadlines using the confirmation rules
Human confirmation: Lawyers verify the start date, exceptions and final version
Work outputs: Submission package, checklist
Quality changes: Procedural requirements are complete
Efficiency changes: Reduce rework on packaging

Advancement criteria After final version, authorization and procedural requirements are confirmed, sign or submit.

06Signing / submission / execution trackingVerify the status of legal matters, keeping obligations, deadlines, and responsibilities continuously visible.

Work inputs Signed documents, submission receipts, obligation lists and confirmed deadlines

6.1Check the signing and submission results and the conditions for effectiveness

Required capabilities: Status verification, responsibility handover
AI and technical actions: Parse receipts and signed documents, then read the target records back to verify them
Human confirmation: Lawyer confirms effective conditions and liability
Work outputs: Receipts, obligation ledger
Quality changes: Status has a basis
Efficiency changes: Reduce data entry

6.2Track fulfillment deadlines and exceptions

Required capabilities: Deadline management, exception judgment
AI and technical actions: Send reminders based on confirmed obligations and flag overdue items
Human confirmation: Lawyer confirms legal consequences and escalation actions
Work outputs: Calendar, exception table
Quality changes: Open items visible
Efficiency changes: Reduce follow-up chasing

6.3Review results and next steps

Required capabilities: Knowledge accumulation, customer communication
AI and technical actions: Summarize verified decisions and outcomes
Human confirmation: Lawyers screen reusable experience and confirm confidentiality permissions
Work outputs: Stage reports, handover packages
Quality changes: Complete basis and responsibilities
Efficiency changes: Reduce wrap-up work

Advancement criteria After results and responsibilities are verified, move to fulfillment tracking or stage handover.

Carry the process into the next conversation

In your legal services, which segment is most worth introducing AI into?

Start from a piece of work in fact intake, research, documentation, or fulfillment; together clarify the inputs, professional capabilities, technical actions, and confirmation conditions, then integrate it into daily operations through training and ongoing implementation support.