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Engineering decision registers: why did we choose this approach?

Short answer

A decision register keeps the options considered, the assumptions and the reason for the choice, so the next change starts from understanding the previous one. For a few decisions, a wiki template and a link from the task are enough. An application makes sense when decisions are scattered across many repositories and projects. At Syntalith the application costs from €3,000 net. At 120 searches for a rationale a month of 20 minutes each, it pays back in 2.9 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 per application, fixed price before contract
from €3,000
from signing the contract to acceptance
4–10 weeks
free prototype on your documents
€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

Team knowledge and AI: statistics 2026

2.9 mo.

payback of a €3,000 application at 120 searches for a rationale a month: €1,396 of work today, €350 with checking and the model. Scenario.

Figures checked: 22 October 2026

Why the data was refreshed at night

A team built a reporting application with a nightly data import, because readers needed the report in the morning and the source system released a file at the end of the day. More frequent pulls were considered, but under those assumptions they were not needed. After a while, a request comes in for the report to support work during the day.

A new developer sees the nightly job and may decide it is enough to run it more often. In the decision record they find the reason for the choice and the limit of the data source, so the team discusses the right change: readers need fresher data, but it has to be established whether the source can deliver it. The engineer notes which assumption no longer matches how users work. The new decision points to the earlier one and explains the reason for the change.

A decision record in the register
  1. 01Contextthe need and the limits at the moment of choice
  2. 02Optionswhat was considered and why it was rejected
  3. 03Decisionthe choice, with the person responsible
  4. 04Materiallinks to sources and tasks
  5. 05Changethe new decision points to the earlier one
A test before you buy
  • The team finds the decision for one planned change
  • The rationale leads to the source material
  • You can see who to talk to about changing an assumption
  • A replaced decision stays in the history

What it costs and when it pays back

Scenario: 120 searches for a rationale a month
Today
120 searches for a rationale × 20 min = 40 h (€1,396)
With the system, a person checks the output in 25% of the time
€349
Model, 1 call per case, Claude Sonnet 5.5
€1
Cost of one case
€11.63 today, €2.92 with the system
Saving
€1,046 a month

Payback of €3,000

2.9 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 400 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.

When the tool changes, you need the rationales together with their links to projects, material and later decisions, so the export has to keep those links.

When a wiki page is enough

AWS describes Architectural Decision Records as a way to keep architecture decisions together with their context and consequences, and when the assumptions change, a new record replaces the earlier one. If the team has a few decisions in one project, a shared template and a link from the task are enough. Your current wiki or tracker may already provide search, labels and links between pages. An extension helps when the records are good but hard to connect with work across several repositories. A decision record also helps when you work with a coding assistant, for example Codex, while a person judges whether the old assumptions still hold.

Template, an extension or an application
  • A few decisions in one project

    a wiki template and a link from the task

    no build

  • Good records, several repositories

    an extension that gathers the links

    from €3,000

  • A coordinator keeps compiling decisions by hand

    a decision register built to order

    from €3,000

Where to start

The first scope covers one team and the step from a changed element to its rationale. For the call, bring a decision whose rationale recently had to be reconstructed, and a note on where the team looked for it. We build this kind of rollout as part of AI applications. 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: an engineering team

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

€4,188

a month for the work on 600 questions

EU cloud version payback: 0.8 mo.

Our assumptions are 12 minutes to find an answer in the documentation, and checking in 15% of the old time. €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: Claude Sonnet 5.5, about 6,000 + 500 tokens per answer in the EU cloud version, at the ECB rate of 1.1205 USD per euro on 22 October 2026. We count the payback for an app with a model in the EU cloud (from €3,000 net). We price the model on your server after a call, and the cost of people's work stays the same.

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 ↗

When an off-the-shelf tool does not fit, we build your own.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 →

Custom AI apps

When an off-the-shelf tool does not fit, we build your own.

We build an application with a language model for the work of your team: searching the company's knowledge, case panel or portal. We check the validity of your documents before launch, and the code stays with you.

See AI applications →
net per application, fixed price before contract
from €3,000
from signing the contract to acceptance
4–10 weeks
free prototype on your documents
€0

Questions

  • What is a design decision register?

    A place where the team records the context, the options considered, the choice and the consequences of a decision, with links to the material. When the assumptions change, the new decision points to the earlier one.

  • Does a decision register need AI?

    No. AI can help search or summarise the records, but the team writes the content, and the decision and its reason come from people.

  • How much does such an application cost?

    At Syntalith from €3,000 net. At 120 searches for a rationale a month of 20 minutes each, the payback is 2.9 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.