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An SLA report you can explain to the customer

Short answer

An SLA report you can explain to a client shows, next to each result, the ticket, the time it arrived, the employee's reply and the calendar used for the count. The automatic acknowledgement stays in the history and does not end the first-response measurement, if that is what was agreed with the client. At Syntalith the automation costs from €3,000 net, and with 40 SLA reports a month at 90 minutes each it pays back in 1.8 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
net for the automation of one process, build paid once
from €3,000
from signing the contract to acceptance
2–6 weeks
free prototype on your cases
€0
Book a free process review (30 min)

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

Service desks: statistics 2026

1.8 mo.

payback of a €3,000 automation at 40 SLA reports a month: €2,094 of work today, €419 with checking and the model. Scenario.

Figures checked: 22 October 2026

SLA reporting: a Friday ticket and a reply on Monday morning

The agreement with the client measures the time to the first reply from an employee within service hours. A ticket arrives late on Friday afternoon, the system sends an automatic acknowledgement at once, and an employee replies on Monday morning. The client asks where the report's result comes from, and the service manager opens tickets looking for the messages the time was counted from.

The report shows the time the ticket arrived, the time of the employee's reply and the service calendar. The automatic acknowledgement is in the history, and under the agreed definition it does not end the measurement. A first reply also differs from resolving the case, so both times have their own results and events. A ticket missing a needed record stays visible with a note of what was not established, and does not vanish from the report.

An SLA result with its source
  1. 01Ticketthe time it arrived and the client
  2. 02Calendarservice hours from the agreement
  3. 03Eventthe employee's reply; the automatic acknowledgement does not count
  4. 04Resultresponse time and resolution time separately
  5. 05Missing datathe ticket stays visible for clarification
A new version of the report after a correction
  • The previous report stays available
  • You can see which ticket changed the result
  • A disputed classification goes to the person responsible
  • AI commentary only on checked results

What automated SLA reporting costs and when it pays back

Scenario: 40 SLA reports a month
Today
40 SLA reports × 90 min = 60 h (€2,094)
With the system, a person checks the output in 20% of the time
€419
Model, 1 call per case, Claude Sonnet 5.5
€0.46
Cost of one case
€52.35 today, €10.49 with the system
Saving
€1,675 a month

Payback of €3,000

1.8 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: 4,000 input tokens and 600 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 each case: searching, reading, writing and handing over. What remains is the time to check the result, 20% of today's work in the 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.

The scenario assumes 40 reports a month at 90 minutes each: collecting tickets, counting by the calendar, clarifying gaps and preparing the document.

Can your current help desk prepare this report

Atlassian describes in Jira Service Management SLA goals, work calendars and the conditions that start, stop and pause the measurement. If the ticket history and the rules are in one system, improving its report is enough. A connection makes sense when the manager collects events from several tools every month or retypes the result into a document for the client. Counting time by a calendar needs no AI, and a model can help prepare a commentary on checked results. Changes to SLA rules and their versions are covered in the article on changing SLA rules.

The help desk report, an integration or an app

  • History and rules in one system

    improve the report in the help desk

    configuration

  • Events from several tools

    an automation of the report for the client

    from €3,000

  • Frequent rule changes for many clients

    an app for rule versions

    €3,000–7,000

Where to start

The first scope covers one client and the summary that is put together by hand today. The team names the calendar, the measurement definitions and the person who settles unclear cases. A client review by customer success is covered in training for client account reviews. We build this kind of system as part of our AI automations. For the free process review, 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 receive a written plan within 2 working days.

Worked example · Scenario

How we count the payback: SLA reports

We run the same count for your clients in the free process review.

€1,815

a month for 12 h a week of report preparation

Payback: 1.9 mo.

The hours for each firm size 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. We measured the model cost in our report system: a commentary with a check of the figures costs about €0.0002, converted at the NBP rate of 21 October 2026 and the ECB rate of 1.1205 USD per euro on 22 October 2026.

See the full calculation →

This is how we implement it in companies

All case studies →

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 ↗

This process can go on its own. You will check this on your cases before you pay.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.

Private AI models →

Source: OpenAI, training on data from private accounts ↗ · OpenAI, who sees conversations ↗ · Anthropic, changes for private accounts (August 2025) ↗ · OpenAI, data in API ↗ · AWS Bedrock, EU profile ↗

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

AI automations

This process can go on its own. You will check this on your cases before you pay.

The system takes over repetitive work from emails, documents and spreadsheets, and hands over uncertain matters to a human. A prototype for 20-100 cases that your team has already handled costs €0. You pay the other half of the price when the system reaches your goal.

See AI automations →
net for the automation of one process, build paid once
from €3,000
from signing the contract to acceptance
2–6 weeks
free prototype on your cases
€0
Book a free process review (30 min)

or call+48 888 78 48 78WhatsAppMon–Fri 9:00–17:00 CET

Questions

  • How do you explain an SLA result to a client?

    Show next to the result the ticket, the time it arrived, the event that ends the measurement and the service calendar. Tickets missing a needed record stay visible, with a note of what was not established.

  • Does an automatic acknowledgement end the first-response measurement?

    It depends on what was agreed with the client. If you measure the employee's reply, the automatic acknowledgement stays in the history and does not end the measurement.

  • How much does such an automation cost?

    At Syntalith from €3,000 net. With 40 SLA reports a month at 90 minutes each, the payback is 1.8 months. We agree integrations and maintenance before the quote, and the model costs under €1 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.

Write
contact@syntalith.ai
Call
+48 888 78 48 78
WhatsApp
write on WhatsApp

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.