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Custom AI apps

A custom AI app is a tool with an LLM inside, built for your process when an off-the-shelf SaaS doesn't fit or your data can't leave the company: a copilot, document search (RAG), extraction, classification. From €6 000 net, 4–10 weeks to production. Accuracy is measured with an eval set on your data, uncertain answers go to a human instead of guessing, and the code, prompts and eval data stay yours.

Written by the Syntalith engineering team · Updated 10 June 2026

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In short

Price and time
from €6 000 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 dataaccuracy checked on your real documents before launch; when the system is not sure, it asks a human, it does not 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. One made-up answer delivered with confidence is enough for the team to stop trusting any of them. Those hours have an hourly rate; the status quo is not free.

Outcome

In 4–10 weeks you get an application where the LLM does specific, repeatable 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, and uncertain answers go to a human. The pilot on real data has to hit that accuracy target before you commit to the full build.

Who is accountable

We run LLM-powered systems in production, including triage of a shared inbox at about 3,000 emails a month, with a confidence threshold and human handoff for exceptions.

See the case study

  • Artem Lisovtsov

    Founder, Full-Stack & AI/ML Architect

  • Serhii Ivanchatenko

    Head of Backend & AI Integrations

This is how one sourced answer is built.

From a question or a document to a checkable answer. The confidence gate marks where a person takes over.

  1. 01

    A query arrives

    a user question or a document enters the app

  2. 02

    Source retrieval (RAG)

    the app retrieves and reranks the right passages in your data

  3. 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 rather than 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

  1. Passage from your knowledge baseRAG
  2. Record from your systemRAG

Eval and guardrails

  • Guardrails, checking the answer against its source, fallback
  • Content from documents is data rather than commands; actions under conditions and minimum permissions
  • A log of input, sources and decision

Code, prompts and eval data belong to you

What the build includes.

  • Internal copilots and panels on your data
  • Answers from your documents with the source cited (RAG)
  • Extracting data from invoices, contracts and scans into an agreed format
  • Classification of cases and documents according to your rules
  • An eval set on your own examples: accuracy measured against evidence
  • 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), but 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: we'll recommend the SaaS

Entry price

from €6 000

Delivery window · 4–10 weeks

The price depends on the work, not on 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

  1. Implementation buildfrom €6 0004–10 weeks
  2. 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

30 minutes · written takeaway within 2 business days

Book a free process scan (30 min)

Times are shown in your own time zone. We work with clients across time zones.

Prefer to write? No-obligation form

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 instead of 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. Where personal data is involved, we sign a data processing agreement (GDPR), and only assigned engineers have access. Models: OpenAI, Anthropic or Google APIs under agreements that exclude training on your data, or open-weights models on your infrastructure when data cannot leave.

  • 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 €6 000 net. The price depends on the number of integrations, data sources and quality requirements, not on 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.

  • Is the pilot included in the price?

    Yes. A pilot on your data is the first implementation stage inside the fixed price. If it does not reach the written accuracy threshold, we improve it within the same price, and the decision on full deployment stays with you.

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.
€030 minutes · written takeaway within 2 business days
Book a free process scan (30 min)

Times are shown in your own time zone. We work with clients across time zones.

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

Full pricing