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Reports with inspectable numbers: how the workflow works

Short answer

A management report written with AI holds no made-up numbers when the figures and the commentary are kept apart. Every amount has an ID and an approved source, and the release check rejects any value outside that set. A conflict between sources stays visible until the controller decides. At Syntalith an automation costs from €3,000 net. At 12 reports a month of 240 minutes each, it pays back in 2.4 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

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

Reporting and AI: statistics 2026

2.4 mo.

payback of a €3,000 automation at 12 reports a month: €1,675 of work today, €420 with checking and the model. Scenario.

Figures checked: 22 October 2026

Two sources give different revenue

The sales report shows a different amount from the ledger. Fluent commentary will not settle that difference. The controller needs a view that exposes the conflict and leads back to both sources. In our demonstrator, on synthetic data for July 2026, the finance system and the CRM show different revenue. The view shows both values and the reason: different booking periods.

The renderer receives a set of figures with IDs, sources and formatting. The commentary may point only to those IDs. A rule checks that the text brings in no outside values, and the reconciliation compares amounts within a set tolerance. A difference above the tolerance stays a visible discrepancy. The CSV export holds the status of each figure, the version of its definition and the date it was read, and missing data never turns into a zero.

A management report with figure checks in the Syntalith demonstrator
The rebuilt local application, 25 September 2026. Synthetic data.
The release check for a report
  1. 01Figuresa set with IDs, sources and format
  2. 02Reconciliationamounts compared within a tolerance, any excess visible
  3. 03Commentaryreferences to figure IDs only
  4. 04Checkoutside values and sources that do not exist are rejected
  5. 05Controllerdecides the business meaning and the release
What the evidence covers
  • 3 correct reports, 4 rejected and 5 reconciliations, 12 cases in total
  • Synthetic data, with no integration with a client's systems
  • Commentary written without a model, so the editing work is not measured
  • The system shows the conflict, but it does not choose the right value

What it costs and when it pays back

Scenario: 12 reports a month
Today
12 reports × 240 min = 48 h (€1,675)
With the system, a person checks the output in 25% of the time
€419
Model, 2 calls per case, Claude Sonnet 5.5
€1
Cost of one case
€139.60 today, €35.00 with the system
Saving
€1,255 a month

Payback of €3,000

2.4 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: 15,000 input tokens and 1,500 output tokens per call at Anthropic's rates, 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 handing over. What remains is the time to check the result, 25% 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 12 reports a month at 4 hours of work each: gathering the figures, reconciling differences and the commentary. In the demonstrator, the release check cost about €0.0002 for preparing the commentary and checking the figures.

Who decides the definition of revenue

The release check shows where the check on figures belongs. Someone still has to settle the definition of revenue, the accounting period and the right source. When two correctly read values describe different measures, an arithmetic comparison will not solve the problem. Anthropic, in Building effective agents, advises checking a model's output with a program wherever possible, and the figures in a report are such a place. The full report cycle is described in automated management reports.

How to check the figures in a report
  • One system, fixed definitions

    a report in your current tool

    no build

  • Several sources, commentary written by hand

    automation with reconciliation and a release check

    from €3,000

  • AI commentary on every report

    a figure check before the commentary is released

    from €3,000

Where to start

The first trial covers one recurring report: first we compare the data and corrections with the current version, then we switch on the release preparation. To prepare: definitions of measures and periods, the sources of the figures with their tolerances, and the person who approves the report. We build this kind of rollout as part of AI automations. For the free analysis, a description of one case from recent weeks and of the tools it passed 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: a management report

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

€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.

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

Questions

  • How do you avoid made-up numbers in an AI report?

    Keep the figures apart from the commentary. The figures come from approved sources and carry IDs, the commentary refers only to them, and a check before release rejects any value outside the set.

  • What should happen when two sources give different amounts?

    Show both values, the difference and the reason, for example a different booking period. Picking one amount just so the table adds up removes the information the controller needs.

  • How much does such an automation cost?

    At Syntalith from €3,000 net. At 12 reports a month of 240 minutes each, the payback is 2.4 months. We agree integrations and maintenance before the quote, and the model costs about €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.

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.