Taking new projects

Apps, automation and AI in one product that ships.

Apps, RPA and AI agents from one team. We add AI where it solves a problem — elsewhere, good code is enough. Since 2017.

Web
& mobile apps
RPA
Process automation
AI
LLM & agents, when needed
EU
AI Act & GDPR ready
Selected clients we've worked with
  • embraco
  • Nidec
  • SCHEIDT&BACHMANN SYSTEMS & SOLUTIONS
  • manuvia GROUP
  • express people
  • GWR
  • T Massachusetts Bay Transportation Authority
  • VALLEY METRO
  • cta
  • + more
What we do

Automation and AI. Software. And the rules that now apply to both.

Three things you would normally buy from three vendors. We deliver them separately, or as one.

Process automation

RPA & AI agents

An RPA bot where clicking instead of a human is enough. An LLM agent where the work needs to understand a document, an email, a context.

  • RPA bots (UiPath, Power Automate, Python)
  • LLM agents where plain RPA is not enough
  • Documents, OCR, email classification, integrations
Discuss your process →
RPA + AI UiPath · Power Automate · LLM
Apps as product

Web & mobile apps

Dashboards, B2B portals, mobile apps for field teams. Full-stack, built to still work in five years.

  • Web apps and B2B portals (React, Vue, Next.js)
  • Mobile apps (iOS/Android, cross-platform)
  • Legacy modernization and integrations
Discuss your project →
APP React · Native · Python · Cloud
AI compliance & regulation

EU AI Act, NIS2 and GDPR

Deploying AI now comes with rules. We get the systems, records and oversight into a state that survives an audit.

  • AI system classification and gap analysis
  • Human oversight under Art. 14 and 26, provable
  • NIS2, GDPR and audit-ready records
Discuss compliance →
EU AI Act · NIS2 · GDPR
Full stack, one team

Apps + Automation + AI. Mix as needed.

Three invoices, three timelines, no system. That's the usual route. We build one product — or just the layer you need.

Web Apps Mobile Apps RPA Bots LLM Agents Cloud Native EU Compliant
Our products

Two products we build and run ourselves.

Not portfolio samples. Software we develop, host and sell under our own name — and use ourselves.

In pilot · 2026

AI Capability Audit

How many people can still run a critical process without AI — shift by shift, with every number traceable and a forecast of when capacity drops below a safe line.

Demo plant · 24 capabilities · 3 shifts engine 0.4.0
Below required capacity
5 / 24
3 caused by automation
Crossing within 12 months
2
forecast at today's pace
Roadmap above safe ceiling
7
planned automation
Top risk
Paint defect diagnosis
CRITICAL
Fallback coverage 55%
0% required 100%
AI share 69% · safe automation ceiling 91%
Demo data from a fictional plant. Every figure comes from a deterministic model — no language model sits in the numeric path.
  • Fallback coverage per shift, not a plant-wide average
  • Deterministic calculation — no LLM anywhere in the numbers
  • Safe automation ceiling and a 36-month erosion forecast
  • Evidence for human oversight under AI Act Art. 14 and 26
  • Evidence against penalty exposure — the ceiling is €15M or 3% of turnover
Ask about the pilot hosted single-tenant · first result in 6 weeks
Live

YOUniversity

A realtime learning management system for companies that train their own people — and need to know who actually completed what.

Interface illustration SK EN CS DE HU
Module 3 of 5
Workplace safety
Presentation
Risks on the line
complete ✓
Video · 12:40
Stopping the machine safely
68%
SCORM
Interactive exercise
queued
Test and certificate
Final check
locked
Interface illustration. An admin sees learner progress live — realtime over WebSockets, not an overnight report.
  • Courses from presentations, video, text, tests and SCORM packages
  • Admins see learner progress live, not in an overnight report
  • Connects to what you already run — Google and Microsoft SSO, SCORM, data export
  • Interface in five languages, content in any — adding a language is configuration
  • An AI layer: course translation today, answer grading and reminders next
elearning.batacon.sk our hosting, or your own AWS
AI Capability Audit

The whole case — and where it is heading.

01Why this matters

Deploying AI without knowing how much human capability is left behind it has two consequences. You feel one in operations, the other under inspection.

Operations

When the AI stops, recovery outruns the SLA

Automation does not remove people all at once — it removes the ones who can still do the work unaided. Headcount looks unchanged while real coverage falls, often to a single person per shift. You find out during the outage.

Regulation

The AI Act asks for competence, not paperwork

Article 26(2) requires the people assigned oversight to have the competence, training and authority to actually exercise it. A nominal assignment does not satisfy it. Article 14(4) additionally names automation bias as a risk to be managed. The evidence has to show the person could intervene — not that they are on a list.

Deadline

One budget cycle is what is left

Regulation (EU) 2026/1744 deferred the high-risk obligations to 2 December 2027 for standalone Annex III systems and 2 August 2028 for AI embedded in regulated products. That is a deferral, not a repeal — and measurements cannot be produced retroactively.

02Statutory maximum fines

Statutory maximum fines Whichever is higher, measured on worldwide annual turnover
EU AI Act Art. 99
Deployer obligations, including the human oversight duty in Art. 26
€15M or 3% of turnover
EU AI Act Art. 99(3)
Prohibited practices under Art. 5
€35M or 7% of turnover
NIS2 Art. 34
Important entities, which include manufacturing, covering business continuity
€7M or 1.4% of turnover
GDPR Art. 83
Personal data processed in training and running the AI
€20M or 4% of turnover

Read this as a deadline, not a threat. AI Act penalties attach to obligations that only apply to high-risk systems from December 2027 and August 2028 — until then it is time to prepare, not an enforceable fine. NIS2 and GDPR duties apply today. For SMEs the lower of the two figures applies. The tool produces measurement, evidence and a checklist.

03What the audit measures

Eight quantities for every capability in scope. No language model computes any of them — the calculation is deterministic and every figure decomposes back to its inputs.

  • Fallback coverage Per capability and per shift, with the binding shift deciding — not the average.
  • Required capacity Derived from incident arrival, SLA, Erlang staffing and redundancy. The derivation is printed, not asserted.
  • Erosion forecast 36 months ahead, at both the current and the planned AI share.
  • Safe automation ceiling The highest AI share at which coverage still holds.
  • Takeover time vs SLA How long returning to manual operation takes after an AI outage.
  • Fallback concentration Effective number of people and key-person share — whether capacity hangs on one person.
  • Oversight gate Authority, monitoring, stop-to-safe, catch rate versus error rate — against AI Act Art. 14 and 26.
  • Confidence band Every input carries a provenance tag (measured / estimated / assumed) that propagates into the result.

04How the assessment runs

Six weeks from first scoping to the executive readout. We assume no particular MES, CMMS or type of AI — we map what you already have.

  • Week 1 Scoping, workshop, data request
  • Week 2–3 Extract, validation, identity mapping
  • Week 4 Calculation and input review with process owners
  • Week 5 Draft report
  • Week 6 Working session and executive readout

Effort on your side: roughly 5–7 person-days, mostly from whoever owns the records — not necessarily IT. We do not need names, an employee_id is enough. We do not take performance appraisals, disciplinary records, or the content of AI conversations.

05Where the product is going

Today it is an assessment you re-run whenever the inputs change. Connectors are what turn it into continuous measurement — that is the next phase, not today's state.

Running today

Phase 1 — the assessment

  • Manual data entry and file upload
  • Recalculation after every change, with no run button
  • Export to JSON, HTML and Markdown
  • Hosted single-tenant, one plant
Next up

Phase 2 — connectors and monitoring

  • HRIS and CMMS connectors feed the data on a schedule — the audit becomes continuous measurement
  • Comparison against the previous assessment
  • A wizard for all six data blocks, saved projects
  • Richer charts and PDF export
Later

Phase 3 — multi-plant

  • Multiple plants, sign-in, an audit log of data changes
  • Scheduled reassessment in the background
  • Custom adapters for your own systems

Manual entry stays in every phase. Some plants will never allow a connector onto their network, and they still need to be able to run the assessment. Integrations are a separate commercial and security conversation — network access, a data processing agreement, the works council.

YOUniversity

What it does — and what we can add to it.

01What YOUniversity does

We built it because most LMS platforms either cost like an ERP or cannot tell you whether a learner actually watched the video. It runs in production at elearning.batacon.sk.

  • Activity types Presentation, video, audio, text, test, certificate and SCORM packages in one lesson.
  • Realtime view An admin sees live who opened a course, where they are in a video, and how a test went — over WebSockets.
  • Completion and certificates Completion rules (all lessons, video percentage, minimum score), pass/fail, and numbered PDF certificates.
  • Multilingual SK, EN, CS, DE, HU. A course can be machine-translated and then corrected by hand.
  • Roles and enrolments Admin, instructor and student; enrolments tracked as active, completed, failed, expired or revoked.
  • Deployment Bun, Hono, PostgreSQL, Redis and SvelteKit. Runs on our hosting, or in your own AWS account.

02The AI layer

Course translation through a language model runs today, over an OpenAI-compatible API — so it works equally with AWS Bedrock or a model on your own hardware. The rest we add where it earns its place against your training volume.

  • Course translation · live A course is translated automatically, then corrected by hand. A translation never overwrites a manual version.
  • Grading open answers Tests grade multiple choice today. Open answers can be scored by a model against a rubric, with borderline cases routed to a human.
  • Reminders that are not spam The system already knows where each learner stopped. A reminder can follow real progress and an approaching deadline instead of going out to everyone each Monday.
  • A course from documents you already have From a directive, a manual or a deck, a model can draft the outline, lessons and questions. A draft for a trainer to edit — not a finished course.

In production today: course translation. The other three are custom work on top of the existing system — we will estimate them once we know your volume and languages.

03Integrations

An LMS earns nothing if people need another password for it and HR retypes the roster by hand.

  • Sign in with the company account Google and Microsoft OAuth — people sign in with the account they already use for email.
  • SCORM packages Courses bought or built elsewhere upload as SCORM and run beside your own lessons.
  • Export and reporting Overviews, an audit log, and data export for an HR system or an auditor.
  • Per-course notifications Email settings and notifications are configured per course, not globally.
  • Enrolments from the system you already run A link to HR or workforce systems so enrolments and groups fill themselves. Built to order — this is exactly the integration work we do for clients anyway.
What you get

Six questions that always come up.

The answer is the same whether we build an app, a bot, or the whole stack.

Yes, and it is not a side line. A large share of the work is classic apps and RPA bots with no AI layer at all. Sometimes AI is expensive, unpredictable, or simply complicates the product. We reach for AI when it solves the problem; otherwise you do not pay for it.

Yes. The usual route is a software house for the app, a consultant for automation and an agency for AI — three invoices, three timelines, and nobody owning the seams between them. We deliver all three layers as one system, or just the layer you need. EU AI Act and GDPR are part of the work from the start, not an add-on billed at the end.

The person on the call writes the code. No account manager in front of a junior team, no handoff offshore once the contract is signed. Senior people from kickoff.

The code, documentation, prompts, models and deployment scripts are yours, in your own Git repo from day one — no hosted-only models, no lock-in. We build on Python/FastAPI and Node.js, React/Next.js/Vue, React Native and Swift/Kotlin, UiPath and Power Automate, OpenAI and Anthropic Claude, plus open-source models via vLLM or Ollama when data cannot leave the building. We run on AWS, Azure and GCP.

One of our two own products, built for manufacturing plants. It measures how many people can still run a critical process without AI support, shift by shift, and forecasts when that capacity drops below a safe threshold. No LLM touches the numbers: the calculation is deterministic and every figure traces back to its input. It is currently in pilot with manufacturing partners.

By scope: a web app without AI is typically 30–50% cheaper than the same one with an LLM agent, and a standalone RPA bot is the cheapest way in. Discovery call within 5 business days, audit and roadmap within two weeks; you get a transparent range after the call. We work remote-first, with active projects across the EU, US, Canada and the UK.
Ready to start?

Tell us what's broken.

Thirty minutes on a call beats three months of internal debate. You do not need a finished spec or an approved budget — just a process that is not working.

info@batacon.eu +421 908 996 704
Sokolovská 178/10, 040 11 Košice · Slovakia · EU
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