# Billing, Time Capture, and the Ethics of the Automated Entry > Legal · AI Course · LEG-8 > Source: https://ibl.ai/solutions/legal/course/billing-time-capture-ethics > Last updated: 2026-08-25 **AI time capture and narrative drafting — reconstructing the day accurately, writing entries clients approve, and the fee question AI efficiency creates.** ## The Short Answer **AI time capture recovers unbilled hours and simultaneously raises a Rule 1.5 question about billing for work it compressed. ibl.ai runs time capture and narrative drafting inside the firm where you own all the code and the data — so activity data revealing matter strategy never leaves the firm's control.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Legal](https://ibl.ai/solutions/legal) ## Course facts - **Level:** Intermediate - **Duration:** 4 hours across 8 modules - **Format:** Cohort workshop with billing labs - **Modules:** 8 - **Catalog code:** LEG-8 - **Frameworks covered:** ABA Model Rules, Client outside counsel guidelines, Trust accounting rules ## What is this course about? Passive time capture recovers real unbilled hours, and AI-drafted narratives pass billing guidelines more often. Both create an ethics problem: Rule 1.5 reasonableness when the underlying work took a fraction of the time. This course covers the mechanics and treats the fee question as the substantive issue it is. ## Who is this course for? - Billing partners and practice group leaders - Legal operations and finance staff - Associates managing their own time - Firm ethics counsel ### What do I need before starting? - Familiarity with your firm's billing process - Access to client billing guidelines ## What will I be able to do afterwards? - Implement passive time capture and quantify recovered hours - Generate narratives that pass client billing guidelines - Apply Rule 1.5 reasonableness when AI compresses work substantially - Evaluate alternative fee arrangements as the honest response - Protect confidentiality in narratives that leave the firm ## What does each module cover? ### Module 1 — What does passive time capture actually recover? The unbilled hours that reconstruction finds, measured rather than claimed. _(40 min)_ **Objectives** - Implement passive capture - Measure recovered hours honestly - Assess the realization impact **Topics:** Passive capture · Recovery measurement · Realization impact · Accuracy **Activity:** Run passive capture for a week and compare against contemporaneous entries. ### Module 2 — How do you reconstruct the day accurately? Turning activity signals into entries that reflect what actually happened. _(45 min)_ **Objectives** - Reconstruct activity into billable entries - Handle multitasking and interruption - Avoid over-attribution to a matter **Topics:** Activity reconstruction · Multitasking · Interruption handling · Over-attribution risk **Activity:** Reconstruct a real day and compare against your own recollection. ### Module 3 — How do you write a narrative clients approve? Entries that survive billing guideline review and the client's outside counsel software. _(45 min)_ **Objectives** - Write narratives meeting client guidelines - Avoid the phrases that trigger automatic reduction - Describe work accurately at the right granularity **Topics:** Guideline compliance · Trigger phrases · Granularity · Accuracy **Activity:** Rewrite ten rejected entries to meet a real client's guidelines. ### Module 4 — Is it reasonable to bill an hour for six minutes of work? Rule 1.5 when AI compresses the work, treated as a genuine question rather than resolved by convenience. _(45 min)_ **Objectives** - Apply Rule 1.5 to AI-compressed work - Distinguish value from time honestly - Reach a defensible firm position **Topics:** Rule 1.5 · Value versus time · Efficiency capture · Defensible positions **Activity:** Work through four billing scenarios and state your firm's position on each. ### Module 5 — Are alternative fees the honest answer? AFAs as the structural response to an efficiency gain the hourly model cannot capture. _(45 min)_ **Objectives** - Evaluate AFA structures for AI-heavy work - Model firm economics under AFAs - Discuss the transition with clients **Topics:** AFA structures · Firm economics · Client conversations · Transition **Activity:** Model one practice area's economics under an AFA versus hourly. ### Module 6 — How do you check guideline compliance automatically? Catching guideline violations before the invoice goes out rather than after a write-down. _(40 min)_ **Objectives** - Encode client billing guidelines as checks - Flag violations before submission - Track write-down causes **Topics:** Guideline encoding · Pre-submission checks · Write-down tracking · Feedback loop **Activity:** Encode one client's guidelines and run last month's entries through the check. ### Module 7 — What must stay away from trust accounting? The controls AI must not touch, and why trust accounting is a hard boundary. _(30 min)_ **Objectives** - Identify trust accounting controls that must stay manual - Prevent automation from touching client funds - Maintain the audit trail **Topics:** Trust accounting · Hard boundaries · Client funds · Audit trail **Activity:** Audit your workflow to confirm nothing automated touches trust accounting. ### Module 8 — Building the time capture workflow The lab module: capture and narrative drafting with a mandatory attorney review step. _(45 min)_ **Objectives** - Build the workflow with attorney review - Verify confidentiality in outbound narratives - Measure recovery and guideline compliance **Topics:** Workflow build · Attorney review · Confidentiality check · Measurement **Activity:** Build the workflow and run a full billing cycle through it. ## What is the capstone project? **Time capture workflow with a firm position on Rule 1.5.** Build a time capture and narrative workflow with attorney review, encoded guideline checks, and confidentiality screening — plus a written firm position on billing AI-compressed work, reviewed by ethics counsel. _Deliverable:_ A working billing workflow and a firm position paper on Rule 1.5 and AI efficiency. ## How are learners assessed? - Recovered hours measured against contemporaneous entries - Guideline check tested against real historical write-downs - Rule 1.5 position reviewed by ethics counsel ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Billing Time Agent](https://ibl.ai/solutions/legal/agent/billing-time-agent) - [Compliance Agent](https://ibl.ai/solutions/legal/agent/compliance-agent) - [Knowledge Agent](https://ibl.ai/solutions/legal/agent/knowledge-agent) - [Legal Assistant](https://ibl.ai/solutions/legal/agent/legal-assistant) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [Model Rules of Professional Conduct](https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/) — American Bar Association. Rule 1.5 fee reasonableness and Rule 1.15 trust accounting. - [Internal Revenue Service](https://www.irs.gov/) — IRS. Recordkeeping requirements for firm financial records. - [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) — NIST. Controls framework for the automated billing workflow. - [CourtListener](https://www.courtlistener.com/) — Free Law Project. Fee dispute and disciplinary case law relevant to Module 4. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 4 is the course. Everything else is mechanics; the fee reasonableness question is the substantive issue and it should get the most time and the least resolution. - Module 7's trust accounting boundary must be absolute. Automation touching client funds is a disbarment-level risk and the course should be maximally conservative. - Passive capture raises staff surveillance concerns. Address them directly rather than avoiding them, and note where notice or consent is required. - Module 3 needs real client guidelines. Generic examples do not prepare anyone for the specificity of a large client's outside counsel guidelines. - Have ethics counsel review the Rule 1.5 material. The course should present the competing positions rather than telling firms what to charge. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the Billing, Time Capture, and the Ethics of the Automated Entry course cover? Passive time capture recovers real unbilled hours, and AI-drafted narratives pass billing guidelines more often. Both create an ethics problem: Rule 1.5 reasonableness when the underlying work took a fraction of the time. This course covers the mechanics and treats the fee question as the substantive issue it is. It runs 4 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: Time capture workflow with a firm position on Rule 1.5. ### Who should take Billing, Time Capture, and the Ethics of the Automated Entry? It is written for Billing partners and practice group leaders, Legal operations and finance staff, Associates managing their own time, Firm ethics counsel. Prerequisites: Familiarity with your firm's billing process; Access to client billing guidelines. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for legal teams that cannot send work to a public AI tool. ### How do we get access to Billing, Time Capture, and the Ethics of the Automated Entry? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for legal cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Legal courses - [AI and Attorney-Client Privilege: The Architecture Question](https://ibl.ai/solutions/legal/course/ai-and-attorney-client-privilege): Whether sending client material to a third-party AI service waives privilege — the confidentiality analysis, the reasonable-efforts standard, and the deployment that avoids the question. - [Verifying AI Legal Research: Never Cite a Hallucination](https://ibl.ai/solutions/legal/course/verifying-ai-legal-research): A verification protocol for AI-assisted research — why fabricated citations happen, how to catch them every time, and the supervision structure that makes it non-optional. - [Contract Review with AI: Redlining, Risk, and Playbooks](https://ibl.ai/solutions/legal/course/contract-review-with-ai): Encode your firm's negotiating positions into an AI review workflow — clause extraction, deviation detection, risk scoring, and where a partner still reads every word. - [AI in eDiscovery: TAR, Privilege Screening, and Defensibility](https://ibl.ai/solutions/legal/course/ai-in-ediscovery): Use AI across the discovery lifecycle while keeping the process defensible — technology-assisted review, privilege screening, validation, and the meet-and-confer record. - [Client Intake and Conflicts Checking with AI](https://ibl.ai/solutions/legal/course/client-intake-conflicts-with-ai): Faster intake without a missed conflict — entity resolution across a matter history, adverse party detection, and why the conflicts decision stays human. - [Ethical AI Use Under the ABA Model Rules](https://ibl.ai/solutions/legal/course/ethical-ai-under-aba-model-rules): A rule-by-rule walk through AI in practice — competence, confidentiality, supervision, fees, and communication — with a firm policy you can adopt.