What if an off-the-shelf SaaS is enough?

Then you'll hear it straight. If a tool exists that handles the process cheaper, the scan recommendation will say: buy it instead of building. A build makes sense when the process or the data is too specific for ready-made tools.

How do you keep the model from making things up?

With an eval set on your data, guardrails, checking whether the answer follows from the cited source (RAG), and a fallback. We measure accuracy instead of trusting a demo, and uncertain answers go to a human, not to the end user.

What if the model gets it wrong in production?

Every answer carries a trace: the input, the sources used and the decision, so a mistake can be reconstructed and fixed at the source rather than guessed at. Guardrails and confidence thresholds hold doubtful cases back from the user, and after launch the app runs under maintenance that tightens the rules and the eval set.

Where does our data live, and does it leave the company?

We build on production cloud (AWS, Google Cloud or Azure) with isolated tenancy, minimum permissions and encryption, or on your own infrastructure. The app reaches only the data it needs for the task, and the code, prompts and eval data stay on your side; that is one of the main reasons companies choose a build over off-the-shelf SaaS.

What about security and the EU AI Act?

We work on minimum permissions and with a decision log, and uncertain answers go through a human. We also provide technical input to the operator documentation the EU AI Act expects (accuracy, transparency, oversight), but we don't give legal advice or classify the system.

What if the app doesn't work in our process?

We set the quality threshold on your data before the build, so it's clear when the app is ready and when it isn't. If the scan or the eval shows the model can't reach a sensible accuracy on your process, we'll say so plainly before you spend a build budget.

Who maintains the app after launch?

The same engineer who built it. After launch the app runs under maintenance: monitoring, fixes, and evolution of the guardrails and eval set as the data and the process change. Scope and response time are set in the maintenance contract.

How much does an AI app cost?

From 25 000 PLN net. The price follows the number of integrations, data sources and quality requirements, not the number of screens. You get a fixed quote in the proposal after the free process scan, and you know the full cost before signing.

Custom AI apps

Your team spends hours hunting for knowledge scattered across documents, and an LLM call bolted on in a hurry looks great in the demo and falls apart in production: the first confident-sounding hallucination wipes out trust in the whole tool. We build an app that does named work: it answers from your document with the source cited, and when confidence is low or there is no source it hands the case to a human instead of guessing. From 25 000 PLN net, 4–10 weeks to production, accuracy measured with an eval set on your data against a written threshold, and the code, prompts and eval data stay yours.

Book a free process scan (30 min)

In short

Entry: from 25 000 PLN net · 4–10 weeksprice tracks the work, not the number of screens; a fixed quote before the contract
How we guard quality: Accuracy measured on your dataan eval set and a threshold; with no source or low confidence it asks a human, it doesn't guess
What stays yours: The code, prompts and eval datano lock-in to a single provider
For whom: When off-the-shelf SaaS does not fitor data cannot leave the company; not for sites or chat widgets

Book a free process scan (30 min)

Problem

Your team spends hours hunting for knowledge scattered across documents and pulls data from PDFs and scans by hand, because off-the-shelf SaaS doesn't fit the process or can't be given your data. A quick API call bolted onto an app looks great in a demo and breaks in production: without an eval set and guardrails, the first confident-sounding hallucination kills trust in the whole tool.

Outcome

In 4–10 weeks you get an application where the LLM does named work: it answers a question from a document with the source cited, classifies a case, or extracts data from an invoice. We measure accuracy on your data, not on a demo impression, and uncertain answers go to a human, not to the end user. The pilot on real data has to hit that accuracy target before you commit to the full build.

This is how one sourced answer is built.

From a question or a document to an answer you can check. The green line is the boundary the app does not cross alone.

01
A query arrives
a user question or a document enters the app
02
Source retrieval (RAG)
the app retrieves and reranks the right passages in your data
03
Answer with a source
the model answers, pointing to the passage it uses

Confidence threshold

confident answers go to the user, doubtful ones wait

Answer with the source cited

a confident case reaches the user, with a trace

Uncertain answer to a human

a doubtful case the app does not serve on its own

We set the confidence threshold where a human takes over on your data during scoping. Operations with production impact can require approval.

The difference shows when the model is wrong.

Bolted-on API

Quality
“Works on my examples”
Hallucinations
Reach the user
Boundaries
The model does as it pleases
Trace
No idea where the answer came from
Ownership
Locked in someone else's SaaS
Cost and time
Lower entry, a subscription you switch on today

An app built around the process

Quality
An eval set and measured accuracy on your data
Hallucinations
Guardrails, checking the answer against its source, fallback
Boundaries
Content from documents is data, not commands; actions under conditions and minimum permissions
Trace
A log of input, sources and decision
Ownership
Code, prompts and eval data are yours
Cost and time
Higher upfront, no subscription to someone else's SaaS forever

A build costs more upfront and takes longer than switching on an off-the-shelf SaaS. It is worth it when the process or the data is too specific for ready-made tools, or when the data can't leave the company. If a ready tool is enough, we'll say so. You get a fixed quote in the proposal after the free process scan.

An app built around the process

Answer with a source

a confident case reaches the user, with a trace

Sources

Passage from your knowledge baseRAG
Record from your systemRAG

Eval and guardrails

Guardrails, checking the answer against its source, fallback
Content from documents is data, not commands; actions under conditions and minimum permissions
A log of input, sources and decision

Code, prompts and eval data belong to you

Scope

Internal copilots and panels on your data
Document search with reranking, extraction and classification (RAG) with a typed output schema and answers that cite the source
An eval set on your own examples: accuracy measured, not asserted
Guardrails and a fallback to a human for uncertain answers
Integration with your systems over APIs, on minimum permissions and with a log
Code, prompts and eval data belong to you

Data & compliance

Production cloud (AWS, Google Cloud, Azure) or your own infrastructure, minimum permissions and a decision log, uncertain answers through a human. We provide technical input to EU AI Act documentation (accuracy, transparency, oversight), not legal advice. If the app talks to users or generates content, the Art. 50 transparency duty (disclosing it is AI) applies from 2 August 2026.

Not for

Marketing websites and landing pages
Web development with no AI component
A chat widget pasted onto a site
A process an off-the-shelf SaaS handles cheaper and faster, then that's what we recommend

Entry price

from 25 000 PLN

Delivery window · 4–10 weeks

The price follows the work, not the number of screens.

You get a fixed quote in the proposal after the free scan. You know the full cost before signing, with no hidden items.

Book a free process scan (30 min)

What drives the price

The number of integrations and data sources
Quality requirements and the scope of evaluation
Security, consent and an auditable trace

Cost over time

Implementation buildfrom 25 000 PLN4–10 weeks
Maintenance and oversightpriced individuallymonthly

Free process scan

Start with a free process scan.

30 minutes with the engineer who would build it, not a salesperson.
A review of the processes that cost you the most time and money.
A written summary: what to automate, in what order, with cost ranges.

No sales deck and no obligations. If automation doesn't make sense, we'll write that too.

0 PLN

30 minutes · written takeaway within 2 business days

Book a free process scan (30 min)Prefer to write? No-obligation form

You leave with a plan, not a sales pitch.

30 minutes with the engineer who would build it, not a salesperson.
A review of the processes that cost you the most time and money.
A written summary: what to automate, in what order, with cost ranges.

0 PLN30 minutes · written takeaway within 2 business days

Book a free process scan (30 min)

No sales deck and no obligations. If automation doesn't make sense, we'll write that too.

Full pricing

AI app questions

What if an off-the-shelf SaaS is enough?
Then you'll hear it straight. If a tool exists that handles the process cheaper, the scan recommendation will say: buy it instead of building. A build makes sense when the process or the data is too specific for ready-made tools.
How do you keep the model from making things up?
With an eval set on your data, guardrails, checking whether the answer follows from the cited source (RAG), and a fallback. We measure accuracy instead of trusting a demo, and uncertain answers go to a human, not to the end user.
What if the model gets it wrong in production?
Every answer carries a trace: the input, the sources used and the decision, so a mistake can be reconstructed and fixed at the source rather than guessed at. Guardrails and confidence thresholds hold doubtful cases back from the user, and after launch the app runs under maintenance that tightens the rules and the eval set.
Where does our data live, and does it leave the company?
We build on production cloud (AWS, Google Cloud or Azure) with isolated tenancy, minimum permissions and encryption, or on your own infrastructure. The app reaches only the data it needs for the task, and the code, prompts and eval data stay on your side; that is one of the main reasons companies choose a build over off-the-shelf SaaS.
What about security and the EU AI Act?
We work on minimum permissions and with a decision log, and uncertain answers go through a human. We also provide technical input to the operator documentation the EU AI Act expects (accuracy, transparency, oversight), but we don't give legal advice or classify the system.
What if the app doesn't work in our process?
We set the quality threshold on your data before the build, so it's clear when the app is ready and when it isn't. If the scan or the eval shows the model can't reach a sensible accuracy on your process, we'll say so plainly before you spend a build budget.
Who maintains the app after launch?
The same engineer who built it. After launch the app runs under maintenance: monitoring, fixes, and evolution of the guardrails and eval set as the data and the process change. Scope and response time are set in the maintenance contract.
How much does an AI app cost?
From 25 000 PLN net. The price follows the number of integrations, data sources and quality requirements, not the number of screens. You get a fixed quote in the proposal after the free process scan, and you know the full cost before signing.