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Receivables review: how the workflow works

Short answer

For a payment reminder, one model writes a draft by the company's communication rules, and a second with the opposite goal looks for threats, pressure and promises nobody made. Rules hold back disputed cases, and the operator sees the draft with the check result and decides alone whether to send it. At Syntalith the automation costs from €3,000 net, and at 300 reminders a month of 8 minutes each it pays back in 3.1 months.

How we workDetails ↓

  1. 01Call30 minutes with an engineer, free
  2. 02PrototypeA working system on your process and data, free
  3. 03ImplementationTarget and fixed price in the contract; 50% at signing
  4. 04Acceptance50% once the agreed target is met
analysis with an engineer and a free prototype
€0
team in ChatGPT or Claude, up to 10 people
from €1,150
automation, agent or application for one process
from €3,000

We belong to the OpenAI and Anthropic partner programmes and learn through their official courses. What this means ↓

Author: Syntalith · 3 min read

Example: the morning request inbox
Agent · ready

Statistics

Receivables and AI: statistics 2026

3.1 mo.

payback of a €3,000 automation at 300 reminders a month: €1,396 of work today, €424 with checking and the model. Scenario.

Figures checked: 22 October 2026

Should this case receive a reminder at all?

An overdue payment may concern a dispute or a due date already agreed with the customer. Before a draft is written, rules hold back disputed and paused cases, and the operator sees why. Only a case with no obstacle reaches the model, which writes the reminder by stage and by the company's communication rules.

The second model has the opposite goal. It looks in the draft for threats, pressure and promises the company never made. A risky tone sends the draft back for correction. In our demonstrator, a draft with a separate check cost $0.015652, and the public run covered 250 test cases and 90 counterparties. The demonstrator sends no messages and is not connected to email: the specialist sends the message from their own mailbox.

Receivables overview in the Syntalith demonstrator
An archived screen of the demonstrator, August 2026
A reminder draft with a tone check
  1. 01Rulesdisputed and paused cases held back before a draft
  2. 02Writerthe model writes by stage and by the communication rules
  3. 03Checkera second model looks for threats, pressure and promises
  4. 04Holda risky tone goes back for correction
  5. 05Operatorsees the draft with the check result and decides
What the evidence covers
  • 250 test cases and 90 counterparties on synthetic data
  • Harm test: 5 harmful and 3 safe examples
  • No sending and no email integration
  • No measurement of recovered payments or working time

What payment reminder automation costs and when it pays back

Scenario: 300 reminders a month
Today
300 reminders × 8 min = 40 h (€1,396)
With the system, a person checks the output in 30% of the time
€419
Model, 2 calls per case, Claude Sonnet 5.5
€5.28
Cost of one case
€4.65 today, €1.41 with the system
Saving
€972 a month

Payback of €3,000

3.1 months

The volume, the time and the checking share are the scenario's assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat. Model: 3,000 input tokens and 400 output tokens per call at Anthropic's rates, converted at the ECB rate of 1.1205 USD per euro on 22 October 2026.

The count covers the work around a case: searching, reading, writing and passing things on. What stays is the time to check the result, 30% of today's work in this scenario. Before the quote we measure both times on 20 to 100 cases the team has already handled, in a free test that takes 5 working days.

We leave 30% of today's work in the count because the operator reads every draft. The cost of the model is small next to the cost of one badly worded reminder to a customer.

Why a second model next to the instructions

The instruction „write politely” in the writer's prompt usually works. A separate checker with a different goal catches the cases where the writer added pressure or a promise, and a rule before the model keeps disputed cases away from drafting altogether. The harm test in the demonstrator was small, so before a pilot we widen it with examples from the client's correspondence. Holding a message before sending and leaving the decision to a person is a pattern that the OpenAI Agents documentation describes as human in the loop. The full receivables workflow is described in receivables monitoring.

Template, one model or two
  • A few stages, fixed templates

    templates with fields

    no AI

  • Different customer situations, tone matters

    a writer and a tone checker with the operator deciding

    from €3,000

  • Disputed cases in the correspondence

    an agent that links the invoice to the complaint

    from €3,000

A first trial in the business

The pilot covers one reminder stage and a receivables owner who compares the drafts with their own decisions. We also measure the cases where a reminder should have been held back. We build this kind of system as part of our AI automations. For the free analysis, a description of one case from the last few weeks and the tools it went through is enough. You do not need to send confidential documents, and you get a written plan within 2 working days.

Worked example · Scenario

How we count the payback: payment reminders

We run the same count for your receivables in the free analysis.

€1,396

a month for preparing 300 receivable cases by hand

Payback: 2.5 mo.

8 minutes per case before a reminder and checking in 15% of the old time are our assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat, for companies with 10 or more employees. Model cost: 0.015652 USD per reminder draft with a separate tone check, measured in our receivables system, at the ECB rate of 1.1205 USD per euro on 22 October 2026. The payback counts saved time only. A reminder that goes out on time also recovers invoices that would otherwise go to an external agency or be written off, and we count that on your receivables during the review.

See the full calculation →

Client results.

All projects →
foap.comcustomer service

foap.com customer service: 95% of replies are sent automatically.

Users from several countries write to foap.com, in several languages. The system reads the entire conversation and the terms of that person, and then sends a response in that person's language. Questions about money and exceptions go to the team with a ready draft, so they have to check and send them.

the system sends the answers itself, without human typing
95%
time from the arrival of the e-mail to the ready response (median)
30 s
e-mails that require a response receive a ready-made draft
100%
less work for every 1000 answers (estimate)
47 h

Data from foap.com production, October 20-21, 2026. Estimate: 3 minutes for handwritten response.

How foap.com customer service works →
Sample messages. Money matters always come to the person.

Other implementations

jaktako.design

studio projektowe

−87%

Time to look for new customers, every day

4 hours → 30 min

approx. 73 hours less per month (21 days of work)

according to the studio

How the agent loop works →
Signature Estates

real estate

approx. −90%

Operator time spent browsing portals, per day

2–3 hours → 20 min

approx. 45 hours less per month (21 days of work)

respect · contact the seller 5 minutes after the announcement

How the agent finds property listings →
Kuba Koziej, CEO and co-founder of MoreGrowth and board member at Natu.Care

Kuba Koziej

CEO and co-founder, MoreGrowth

„We started with my own AI agent. During the training that followed, I learned to build new applications myself. Today I am bringing this way of working with AI into my business. Working with Syntalith gave me exactly the skills and tools I needed.”

View the original training review on LinkedIn ↗

Start with the process that takes the most time each week.Book a free process review (30 min) →

How we build automation and agents

From a conversation to a working system in four steps.

  1. 01 · 30 min

    Free process review

    €0

    You're talking to an engineer. You receive the written plan within 2 business days.

  2. 02 · 5 days

    Free prototype

    €0

    The system works on 20-100 cases that your team has already handled. You see every result.

  3. 03 · 2–16 weeks

    Build

    50%

    Fixed price and goal written in the contract. First installment upon signing.

  4. 04 · after reaching the goal

    Reception

    50%

    You pay the second installment when the system reaches its target on your cases.

How a free prototype is created

Mon.

We set a goal

What result do you consider good?

Tue–Wed

The system is working

It goes through your affairs, one by one.

Thu

We compare

We check every difference with the team's work.

Fri

Result and price

List of errors and fixed implementation price.

You choose the matters. We anonymize them upon request, they stay in the EU and we delete them after the test.

On the market, such a test usually costs from PLN 3,000 to PLN 8,000.

Book a free process review (30 min)

We establish a prototype during a free analysis.

Data checked on October 25, 2026.

„Is my data safe?”

Yes. You choose where they go: from your company account to the model on your server.

  1. 00Employee's private account

    Customer and company data ends up in an account that the company cannot see or control. There is no data processing agreement, so it is a violation of the GDPR. When an employee leaves, the chat history goes with them.

  2. 01ChatGPT or Claude corporate account

    The provider does not use your data to train models. A copy of the queries remains in his logs for up to 30 days.

  3. 02API with zero retention (ZDR)

    The provider processes the request and does not save anything. It requires his consent, we prepare the application together.

  4. 03Processing in the EU

    Queries are counted and stored in Europe: OpenAI in the EU region, Claude via clouds with the EU region.

  5. 04Model at your place

    The open model runs on your server or in your cloud in the EU. The data does not go outside the company.

What we do with every implementation

  • Confidentiality agreement and data processing agreement before we receive any data.
  • You choose the cases for the prototype and we anonymize them upon request.
  • The data stays in the EU and we delete it after the test.
  • Corporate accounts with an administrator on your side.

When data cannot leave the company

The open model runs on your server or in your cloud in the EU. To choose from:

White-tailed eagle
Polish model, SpeakLeash and ACK Cyfronet AGH
PLLUM
Polish model, HIVE consortium commissioned by the Ministry of Digitization
Qwen
Alibaba
GLM
Zhipu AI
DeepSeek
DeepSeek

We select a model for the task and check it on your cases. We run it, tune it and maintain it.

Source: Bielik and PLLuM, ACK Cyfronet AGH ↗ · PLLuM, Ministry of Digitization ↗

Do you have a lot of your own data? We will fine-tune the small model.

  1. 1 Your examples
  2. 2 tuning
  3. 3 test on new cases
  4. 4 model at your place

When you have thousands of similar emails, documents or reports, we teach a small open model based on your examples. It works for you, responds quickly and costs less to maintain than a large model. We measure quality on issues that the model has not seen while learning.

Przemysław and Piotr did it in large programming and consulting companies. Artem did it before and does it today at Syntalith. It's about models on your own infrastructure, tuning and quality measurement.

You choose the protection level during the interview. We sign the data processing agreement before we see any data.Schedule a free analysis →

AI implementation

Start with the process that takes the most time each week.

The engineer chooses one process with you and calculates how much it costs today. Then we build a free prototype on 20-100 cases that your team has already handled. We write down the goal and the fixed price in the contract.

See AI implementation →
analysis with an engineer and a free prototype
€0
team in ChatGPT or Claude, up to 10 people
from €1,150
automation, agent or application for one process
from €3,000

Questions

  • How do you control the tone of AI-written payment reminders?

    With a second model with the opposite goal, which looks for threats, pressure and unfounded promises in the draft, and with rules that hold back disputed cases before a draft is written. The operator sees the check result and decides.

  • Can AI send the reminders on its own?

    The demonstrator has no sending feature. The specialist sends the message from their own mailbox, and automatic sending would need a separate decision, integration and tests.

  • How much does this automation cost?

    At Syntalith from €3,000 net. At 300 reminders a month of 8 minutes each, the payback is 3.1 months. We agree integrations and maintenance before the quote, and the model costs about €5 a month.

Let's talk

Just write, call or choose a date.

We work fast because we hate corporate cotton wool. We don't have salespeople or managers to keep an eye on managers. You talk immediately to engineers who then build your system. We respect each other's time and yours too.

We will accept 3 more companies by the end of the year.

Mon–Fri 9:00–17:00 CET

What our OpenAI and Anthropic partnerships mean for you.

OpenAI makes ChatGPT and Anthropic makes Claude. We belong to the partnership programs of both companies, so we work with their tools every day and learn from the creators themselves.

OpenAI Select PartnerClaude Partner Network
01We will select a tool for the task
We implement both ChatGPT and Claude. We check both on your documents, and you choose the one that makes fewer corrections and saves more time.
02Knowledge straight from the creators
We learn from official OpenAI and Anthropic courses and materials. Your team learns about the features and settings that work today.
03Licenses without our margin
When we roll out ChatGPT, Claude or Copilot, you buy the licences directly from OpenAI, Anthropic or Microsoft and they invoice you. We add nothing to the licence.
04Certified engineers
On the team, we have an AWS certification in machine learning and an Anthropic certification in systems architecture on Claude.

Participation in programs does not mean that OpenAI or Anthropic endorses our services.