Contractual deadlines under control
How can you build a verifiable alert system without delegating legal interpretation to AI?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you design a prototype linking assisted clause extraction, controlled deadline calculation and alert simulation. You test its reliability on fictitious contracts, document exceptions and prepare a limited pilot, subject to legal validation before any operational use.
Why this training?
A missed deadline can limit the scope for renegotiation or exit. Isolated dates in a spreadsheet are not enough: the source clause, the applicable notice period and any changes introduced by amendments must also be retained. Generative AI can propose structured extraction, but may confuse signature, commencement and renewal. The setup therefore separates extraction, human validation, deterministic calculation and internal notification, with explicit handling of uncertainties. Running case study: The fictitious organisation Valsorne Capteurs manages twelve maintenance contracts containing renewals, notice periods and contradictory amendments. Its team builds a traceable register and simulates alerts before considering a pilot. Application at work: Following validation by a qualified professional, propose a pilot covering a limited portfolio: compare the prototype with manual tracking, examine each discrepancy and decide whether to continue based on the agreed thresholds.
Objectives of this training
- Model deadlines, notice periods and responsibilities in a traceable register.
- Build an assisted extraction process with references to clauses.
- Configure deterministic calculations and simulated alerts.
- Measure extraction errors and missed alerts.
- Formalise the conditions for launching a pilot.
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Legal professionals and contract managers overseeing renewals.
- Procurement managers and automation leads involved in contract monitoring.
Prior skills
- Have practical experience of reading contracts and tracking notice periods.
- Know how to use generative AI and work with a structured spreadsheet.
- Understand the principles of a workflow: trigger, condition, action.
What you will work on
Define the boundaries of the contractual workflow
Objective: Define the scope of the prototype. Renewal, termination, notice periods; responsibilities and decisions reserved for humans.
Structure the register
Objective: Define the essential fields. Identifier, version, clause, effective date, notice period, owner and status.
Extract without inventing
Objective: Obtain source-referenced proposals. Extraction instructions, citation, abstention and treating the document as data.
Validate the timing rules
Objective: Separate legal interpretation from calculation. Months, days, deadlines; conventions to check with your legal department.
Build the date calculation engine
Objective: Calculate reproducible deadlines. Deterministic formulae, missing data and blocking conditions.
Design the alerts
Objective: Define recipients and escalations. Advance warning, acknowledgement, internal reminders and duplicates.
Assemble the prototype
Objective: Link the register and simulated notifications. Triggering, validated status, logging and access rights.
Test the exceptions
Objective: Identify failures in contract tracking. Late amendment, month-end, ambiguous clause and absent person responsible.
Measure reliability
Objective: Evaluate extraction, calculation and alerts separately. Reference dataset, false positives, omissions and acceptance thresholds.
Prepare the pilot
Objective: Organise a reversible trial. Validation by a qualified professional, maintenance, shutdown and parallel manual monitoring.
What you take away
- Register data dictionary and instructions for source-referenced extraction.
- Prototype for calculation and alert simulation.
- Acceptance test report and anomaly log.
- Pilot plan with responsibilities and a stop procedure.
Method and assessment
- Short instructional sessions followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with peer review of the outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare fictitious or anonymised contracts, with amendments and expected deadlines. Check which tools the organisation permits and arrange a simulation environment with no external sending.
Observable criteria: Every validated deadline refers to a clause and a contract version. ; Calculated dates are compared against the reference dataset ; every discrepancy is explained. ; Rates of correct extraction and missed alerts are calculated on the corpus tested. ; Any ambiguity blocks activation of the deadline concerned until validation. ; The pilot specifies responsibilities, acceptance thresholds and a return to manual monitoring..
Frequently asked questions
Can real contracts be used?
Only if anonymised and used within authorised tools; otherwise, work with fictitious cases.
Does AI resolve an ambiguous clause?
No. A qualified professional validates the interpretation before the deadline is activated.
Does the prototype send a termination notice?
No. No sensitive decisions are automated; only internal alerts are simulated.
Will the system be rolled out at production scale?
No. The two days produce a tested prototype and a pilot plan.
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