Enterprise AI solutions from assessment to live system — with one team, iterative delivery, fitted to how you operate. We don't sell AI-flavored tools; we build systems that keep working after go-live.
30 min · online call · professional follow-up within 1–2 business days
enterprise data experience
data processed and managed
NAV, MBH, MVM, MÁV and more
They lack AI projects that survive beyond PoC. See where you stand now, and what the next sensible step would be.
Questions like: can AI solve this? What results can we expect? Where might we get stuck?
There was a chatbot, a ChatGPT experiment, a pilot — but none went live, or none delivered measurable value.
Copying documents, filling in forms, transferring data between systems — every day, with every person, over and over.
Too many inbound inquiries, slow response times, high turnover in the support team.
The pilot proposal is ready (written by us or someone else), the decision is made — now they need a working system.
You need to decide what to invest in, in what order, and at what risk — and it all goes before the board.
Doesn’t recognize any of them yet?
Let’s discuss your situation in a 30-minute call.
We don't do AI consulting — we build AI systems. In the following nine areas we have proven implementation experience — from frontend to backend, database, and business integration.
AI that recognizes, extracts, and processes information from documents — without manual labor.
Typical use cases
Contract management, invoice processing, automatic data extraction from forms, structuring financial and HR documents.Technical approach
LLM-based document analysis combined with OCR. Structured data extraction integrated with APIs, ERP, or CRM. In production systems, not as PoC.AI that understands meaning, not just keywords — and finds relevant knowledge in large data sets within seconds.
Typical use cases
Corporate knowledge bases, rapid customer support search, making internal document and mail archives searchable, navigating legal/regulatory documents.Technical approach
Vector database, RAG architecture, semantic search models. Verifiably accurate with source references — no "hallucinations."AI-powered assistants that solve tasks, answer questions, and carry processes through to completion — without human intervention.
Typical use cases
Customer support chatbot, internal HR/IT assistant, sales support, multi-system integrated workflow agents.Technical approach
LLM + tool use + integrations. Agents that work well in Hungarian-language environments. API connections to existing systems (CRM, ERP, ticketing).AI that recognizes objects, patterns, and anomalies in real time — improving process transparency.
Typical use cases
Production line monitoring, quality control, security camera analysis, visual tracking of logistics processes.Technical approach
Custom-trained computer vision models on your data. Edge deployment or cloud-based, adapted to your infrastructure.AI that reads handwritten text and filled-out forms, converting them into structured data — quickly and accurately.
Typical use cases
Medical forms, old archives, handwritten insurance/banking forms, filing declarations.Technical approach
Hungarian handwriting recognition with fine-tuning. Structured data integration with your existing management systems.AI-driven automation that takes over repetitive tasks — so the team can focus on higher-value work.
Typical use cases
Data flow between systems, replacing admin tasks, workflow automation, recurring email and reporting tasks.Technical approach
AI combined with classic workflow engines. AI where decisions or interpretation are needed, straightforward automation elsewhere — because not every step requires an LLM.AI models that forecast customer value, churn risk, and revenue — providing a foundation for growth.
Typical use cases
Customer churn prediction, cross-sell/up-sell opportunities, campaign effectiveness modeling.Technical approach
Classic ML (not just LLM). Feature engineering on your transactional data. Model monitoring and updates over the long term.AI that predicts performance and quality deviations — preventing errors, downtime, and losses.
Typical use cases
Early detection of production quality degradation, machine downtime prediction, maintenance optimization, service SLA monitoring.Technical approach
Anomaly detection, time series analysis, predictive maintenance models. With dashboard integration and real-time alerts.Strategy, training, and implementation support so AI delivers real business results and your team can use it with confidence.
Typical use cases
Executive workshops, AI use-case discussions, team training, AI strategy development in enterprise environments.Technical approach
Not a marketing-level AI talk. Concrete workshops tailored to your industry and systems. Implementable and measurable.30-minute free consultation, no obligation, honest opinion.
Most AI projects stall at PoC stage — they work in the demo but never go live. The reason is almost never the algorithm; it's system integration, data quality, or change management. This is exactly what the Omnit AI project methodology addresses.
Before writing any AI code, we clearly articulate the real business question. What should the company do differently? What is the concrete measure of success? We clarify this in a workshop format with your leadership — this happens during the Compass Audit or as part of finalizing the Pilot scope.
AI is only as good as the data it works on. Before development, we map what data is available, in what quality, how it flows between systems, and what the technical prerequisites of integration are. If we find gaps here, we address them with our Data-side services where needed.
In the first iteration, we prove the solution works on a smaller data set and narrower scope. But we don't treat the Pilot as the end product: we decide during the design phase how it will become a live system, with what architecture and scalability. Iterative delivery, two-week sprints, visible progress.
AI delivers value when it runs integrated into the client's daily systems (CRM, ERP, customer service platform, enterprise workflow). Frontend, backend, database level — our own development team carries this through; we don't outsource it.
Handing over the system isn't the end of the project. In the first months we monitor usage, train key users, and fine-tune models and the interface based on real use. Under a defined SLA and operational framework, we also take on long-term system maintenance.
Three entry points matched to your company’s maturity level. Each product has its own detailed page — here we just help you choose.
| AI Opportunity Check | AI Compass Audit | AI Pilot | |
|---|---|---|---|
| WHAT IT ANSWERS | Are your ideas feasible with AI? What can you expect? Where might you get stuck? | Where is the AI potential in the company and where is it worth starting? | Does what we're trying actually work? Is it worth scaling? |
| WHEN TO CHOOSE IT | I have 2–3 specific AI ideas and want an expert opinion | I have no specific ideas, I want to see the full picture | I already know what I want — build it |
| TURNAROUND TIME | 1 week | 4 weeks | 8–12 weeks |
| WORKSHOPS | 1 workshop (2 hrs, max 3 people) | 3 workshops (2×2 hrs, max 8–10 people) | 2–3 workshops + weekly status |
| WHAT YOU GET | 8–12 page expert evaluation • Feasibility signal per idea • Direction recommendation | 5-document package • Detailed pilot proposal • 90-min executive presentation | Working AI solution • Measurement report • Scaling recommendation |
| INTERNAL EFFORT | ~3 hrs / 2–3 people | ~8–10 hrs / 2–4 people (workshops + prep) | ~10–20 hrs / 3–5 people over 8–12 weeks |
| FEE | €1,450 + VAT (fixed) | From €4,950 + VAT (depending on company size) | From €7,950 + VAT (project-dependent) |
| CONNECTION | Work done in the Check can be credited toward the Compass or Pilot fee on a scope-dependent basis (within 60 days) | Work done in the Compass phase can be credited toward the Pilot price on a scope-dependent basis (typically 30–50%). The preparation phase is also faster, as we already understand the context. | Following an Opportunity Check (with 60–90% credited) or a Compass phase (with 30–50% credited), the preparation phase of the Pilot is significantly shortened. |
| AI Opportunity Check → | AI Compass Audit → | AI Pilot → |
Are your ideas feasible with AI? What can you expect? Where might you get stuck?
I have 2–3 specific AI ideas and want an expert opinion
1 week
1 workshop (2 hrs, max 3 people)
8–12 page expert evaluation • Feasibility signal per idea • Direction recommendation
~3 hrs / 2–3 people
€1,450 + VAT (fixed)
Work done in the Check can be credited toward the Compass or Pilot fee on a scope-dependent basis (within 60 days)
Where is the AI potential in the company and where is it worth starting?
I have no specific ideas, I want to see the full picture
4 weeks
3 workshops (2×2 hrs, max 8–10 people)
5-document package • Detailed pilot proposal • 90-min executive presentation
~8–10 hrs / 2–4 people (workshops + prep)
From €4,950 + VAT (depending on company size)
Work done in the Compass phase can be credited toward the Pilot price on a scope-dependent basis (typically 30–50%). The preparation phase is also faster, as we already understand the context.
Does what we're trying actually work? Is it worth scaling?
I already know what I want — build it
8–12 weeks
2–3 workshops + weekly status
Working AI solution • Measurement report • Scaling recommendation
~10–20 hrs / 3–5 people over 8–12 weeks
From €7,950 + VAT (project-dependent)
Following an Opportunity Check (with 60–90% credited) or a Compass phase (with 30–50% credited), the preparation phase of the Pilot is significantly shortened.
Not sure which step is right?
Start with a free consultation — in 30 minutes we’ll clarify where you stand and what the realistic next step is.
We are an implementation company, not consultants. Behind the assessment stands the knowledge of the team that actually builds the AI solutions.
The same team handles AI, data, and system integration. AI works in production when these three come together.
From frontend to backend, database, and business integration. We don’t just connect APIs — we build complete systems.
We’re not riding the AI wave. Before AI became fashionable, we were already building enterprise data systems and custom applications — AI is now a new tool in an existing toolkit.
We don’t stop at recommendations or architecture diagrams. Every engagement ends with a system that runs in production — tested, integrated, and handed over with documentation and support.
The question is never “what AI tool is interesting” — it’s “where does this make business sense for you.” We work backwards from there.
AI isn’t right for everyone at every moment. If our assessment suggests it’s better to first work on data, processes, or systems, we’ll say so honestly.
We never send a proposal sight unseen. We start with a free consultation or a fixed-price assessment so we can see the real situation.
Every project starts with a confidentiality agreement — automatically, not on request. AI models are trained on your data, in your environment, backed by ISO 27001 certification.
Honest answers, no marketing.
The Opportunity Check (1 week, €1,450) gives an expert answer to this question at the level of specific ideas. If you want the full picture, the Compass Audit (4 weeks, company size-dependent) is what examines the entire company.
No. An AI strategy is an outcome, not a prerequisite. One of the main outputs of the AI Compass Audit is exactly the decision-making basis from which a strategy emerges.
Our methodology. We decide during the design phase how the PoC will become a live system — including architecture, integration, and scalability. The PoC isn’t the end product for us; it’s a milestone. No vendor can guarantee going live (it also depends on the client’s decisions), but the methodology ensures there are no technical obstacles.
We don’t give average prices because it would be misleading. A document-processing AI project can run into 6 figures, and a full AI agent platform can reach 7–8 figures. We always provide pricing after a concrete assessment, when we know exactly what needs to be built. The pricing for our 3 AI products is detailed on their respective landing pages.
We sign a non-disclosure agreement (NDA) before the project. AI model training always happens on your own data and in your own environment — the data doesn’t leave the corporate perimeter unless that’s explicitly part of the design (e.g., a cloud-based architecture). We hold ISO certification for information security management.
Opportunity Check: gives an expert answer to your ideas (1 week). Compass Audit: we map your entire company and bring a proposal (4 weeks). AI Pilot: we build the developed proposal (8–12 weeks). Detailed comparison on each product’s own landing page.
Book a free 30-minute consultation. We'll look at where you stand now and what the realistic next step would be — whether that's AI or something else.
30 min · online · professional follow-up by email within 1–2 business days