Promise to pay workflow

Concepts covered

Step breakdown

1. Define the system to deployStart from this Collections operating system and confirm the business outcome, source signals, owners, and review gates.
2. Create the Frontline assetsBuild the workflow canvas, agent prompt, channels, CRM records or tables, routing logic, and Max Activity evidence described in the blueprint.
3. Launch the narrow versionDeploy the smallest reliable version first, keep human review visible, then use activity and analytics to expand the system.

What you will build

A promise-to-pay tracking system that captures payment commitments from WhatsApp conversations, logs them to a Studio Table, monitors the commitment date, confirms kept promises, and automatically escalates broken promises to a human collector — creating a reliable, auditable commitment management loop.

After deployment: when a customer says 'I will pay on Friday', Frontline captures the date and amount, confirms the commitment, monitors Friday, checks for payment, and either closes the case or escalates to a collector — without any manual follow-up.

When to use this

Customers make payment promises that are tracked manually or not tracked at all.

Broken promises slip through because nobody monitors the commitment date.

You need a documented record of every payment commitment for compliance or dispute resolution.

Collectors spend time re-contacting customers to find out if they kept their promise.

System components

Studio Table: Promise to Pay Log — debtor_id, promise_date, promise_amount, status (pending/kept/broken/partial), created_date.

CRM record: debtor with balance, payment_status, collector_owner.

AI commitment capture agent: extracts promise date and amount from conversation text.

Studio workflow A: captures promises from WhatsApp replies during collections outreach.

Studio workflow B: daily monitoring check — runs each morning, finds promises due today, checks payment status.

Max Activity: promise captured, commitment confirmed, payment kept, or escalation with broken promise context.

Step-by-step implementation

Agent prompt

You are analyzing a customer's WhatsApp message to determine if they have made a payment commitment.

Look for: explicit payment promise ('I will pay', 'I can pay on', 'Send you the money by'), date reference (specific date, day of week, 'next week', 'end of month'), and amount if mentioned.

If a promise is detected: extract the date (convert relative dates to YYYY-MM-DD using today's date as reference), and the amount if stated.

Output JSON: { promise_detected: boolean, promise_date: string | null, promise_amount: number | null, confidence: 0-100, original_text: string }.

If confidence < 60, set promise_detected to false — do not capture uncertain commitments.

Workflow logic

Promise captured: log to table, confirm to customer, update CRM debtor record, write Max Activity.

Promise date arrives, payment received: update Log status = 'Kept', write Max Activity, exit monitoring.

Promise date arrives, payment not received after 4-hour grace: update Log status = 'Broken', escalate to collector, write Max Activity.

Partial payment received (less than promised amount): update Log status = 'Partial', create collector task for remaining balance.

Customer extends promise (new date provided before original date): update Log with new date, write Max Activity with extension history.

Final operating state

Every payment commitment made via WhatsApp is captured in the Promise to Pay Log with date, amount, and status.

The daily monitoring workflow checks every due promise and updates status automatically — kept, broken, or partial.

Broken promises escalate to collectors within hours of the missed commitment — no manual follow-up check required.

Max Activity shows the full promise history for each debtor: commitment made, confirmed, monitored, and resolved.

Troubleshooting

Agent not detecting promises correctly: add example customer messages to the prompt showing what promise language looks like in your market (including common phrasing in Spanish or Portuguese if applicable).

Relative dates not converting correctly: pass today's date explicitly to the agent as a context variable. Never assume the model knows the current date without being told.

Daily monitoring missing some promises: verify the Table query filters by today's date in the correct timezone. UTC vs. local time discrepancies cause missed checks.

Duplicate promise rows: add a deduplication check — before creating a new row, check if a 'Pending' promise already exists for this debtor today.

Operational playbook

Use Promise to pay workflow as part of the Frontline Solutions Collections operating loop: inspect the current product state, confirm the source context, and decide what should happen next.

The goal is not to memorize screens. The goal is to understand how the product surface supports repeatable work, AI assistance, and accountable handoff.

Best practices

Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.

Prefer narrow, inspectable setups over broad automation. Teammates should be able to explain why the system took an action from the visible product state.

Platform layers involved

Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.

Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.

Outcome metrics

Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.

The metric should reflect the business outcome, not only whether the automation ran.

FAQs

Related workflows