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    Use-case library
    Source-to-Pay

    Contract & Clause Extraction

    Pull obligations, terms and price conditions from contracts into structured fields.

    LLM workflowAutonomy: DraftRisk & ControlQualityMedium effortOrg maturity: AugmentedCFOPartner / MD
    What changed

    A language model now reads each contract and extracts payment terms, renewal and rebate clauses, performance obligations and unusual terms into a fixed schema, citing the clause each field came from, so every value can be checked against the source text.

    How it's built

    LLM workflow (rung 4 of 6). A coded control path: chain, router, parallel fan-out, evaluator-optimizer.

    Not an agent: the sequence of steps is fixed in advance, so the whole path can be tested end to end.

    A language model handles steps 2, 3 and 4. Rules: steps 1 and 5.

    Autonomy: T1 · Draft
    Produces a complete output for a human to review and release. Human role: reviews every item and releases it.
    How it is governed
    The path is testable end to end. Each step is independently evaluable.
    What the certification exams call this
    • Anthropic (Claude Certified Architect – Foundations (CCAR-F))Workflow: models and tools on predefined code paths (chaining, routing, parallelization, evaluator-optimizer). source
    • OpenAI (OpenAI agent guidance (Agents SDK, Codex, OpenAI Academy))Orchestrating via code, which the Agents SDK calls more deterministic and predictable. source
    • Google Cloud (Generative AI Leader)Workflow agents (sequential, parallel, loop) that run steps in a fixed order. source
    • Microsoft (AI-901 (Azure AI Fundamentals) and AB-100 (Agentic AI Business Solutions Architect))Sequential and concurrent orchestration patterns, with steps in a set order. source

    How Koko classifies AI · the full ladder is taught in AI & Agentic Delivery Foundations.

    Try it

    Illustrative demo

    Worked example of "Contract & Clause Extraction" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.

    Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.

    What this run should concentrate on.

    Run live in Claude

    Workflow

    1. 1.RulesIngest the contract set and split each contract by clause.
    2. 2.Generative AIExtract payment terms, renewals, rebates, SLAs and performance obligations into a fixed schema.
    3. 3.Generative AICite the exact source clause for every extracted field.
    4. 4.Generative AIFlag non-standard or high-risk terms against the policy playbook.
    5. 5.RulesExport structured fields to the system of record or obligation register.

    Prompt recipe

    Read the attached contract and extract: payment terms, renewal/auto-renewal clauses, rebates/discounts, performance obligations, termination and liability terms. For each, quote the source clause. Flag anything that deviates from [standard terms]. Do not infer terms that are not present.
    Try in Ask KokoAI
    Controls required
    • Every extracted field cites a source clause for verification
    • Legal/commercial review of flagged non-standard terms
    • Confidential document handling and access control
    • Human sign-off before terms feed revenue recognition or payment
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