From autonomous agent swarms to military-grade guardrails and strategic adoption, explore the three pillars of our engineering practice.
Custom GenAI pipelines, Multi-Agent Swarms, Semantic RAG integration, and model fine-tuning.
Prompt injection defense, PII data redaction, automated fact-checking, and Role-Based Access Control (RBAC).
High-ROI use case discovery, AI data readiness pipelines, rapid prototyping, and team adoption workshops.
Engagement Model
Enterprise AI requires engineering discipline, not just wrapper scripts. Our 4-phase methodology ensures your deployment is secure, scalable, and actually solves your business problem.
We analyze your current operational bottlenecks, map your data silos, and identify high-ROI opportunities for AI automation without disrupting existing workflows.
Before writing a single line of code, we design a zero-trust architecture blueprint, selecting the exact mix of LLMs, vector databases, and orchestration layers.
We build your custom agents and RAG pipelines in secure environments. We actively 'red team' the system to ensure it is resilient against prompt injection and hallucinations.
We deploy the architecture to your infrastructure or our managed servers. Your team receives full documentation, workflow training, and ongoing SLA-backed support.
Our Arsenal
We remain entirely tool-agnostic. We audit your existing infrastructure and select the exact combination of vector databases, LLMs, and orchestration layers that fit your specific enterprise requirements.
We use visual and code-based orchestration to connect systems reliably.
We route prompts dynamically to the best-in-class LLMs based on the task.
High-performance vector databases to give your agents perfect memory.
Frameworks designed to deploy multi-agent swarms that collaborate.
Enterprise-grade layers to prevent hallucinations and secure PII.
Scalable infrastructure to keep your AI endpoints running 24/7.
Methodology
We analyze your existing workflows, identify high-ROI automation opportunities, and assess your data readiness.
Before writing code, we design a zero-trust architecture, establishing RBAC and PII redaction guardrails.
We build functioning GenAI prototypes in weeks, allowing you to test interactions on your actual proprietary data.
Transitioning from PoC to production. We deploy scalable, multi-agent systems integrated directly into your stack.
Engagement tiers
Fixed prices. 100% IP yours. Most clients start with a 30-min scoping call so we agree on the right tier before any building.
Projects by us
AI systems built by us — same stack we ship to clients.
AI health coach powered by wearable data
Ingests wearable data (heart rate, sleep, steps, oxygen) from watches and fitness bands, correlates it with AI, and surfaces concrete lifestyle, medication, or 'see a doctor' recommendations. Web and mobile clients on the same API.
Stack
AI that turns flat specs into a walk-through 3D interior
Type a flat's specs and the AI generates its 3D floor plan + interior, which you can fine-tune by hand and walk through in FPV or drone view. Swap between ten interior styles in one click — no interior designer needed.
Stack
Fact-checked news aggregator with AI bias scoring
News aggregator that pulls from multiple sources, fact-checks with AI, summarises the piece, and scores how biased / opinionated it is plus what the article is trying to achieve. Editorial transparency is the whole point.
Stack
Four AI agents vote on every crypto trade
Four specialised AI agents — data, sentiment, quant model, verifier — vote together on trades across the top 50 crypto tokens, combining price with news and social signal. Runs 24/7, web UI + API, open-source.
Stack
30-minute discovery call, fixed-price quote, 4-week roadmap. The next project could be yours.
Real reviews
Quick feedback from people who worked with our team.
Overall
Based on real reviews
I stopped guessing. Now I follow clear steps and get solid results every time.
Now I know what to ask and how to get clean outputs for posts, emails, and offers.
They quickly identified what to automate, what to tighten up, and how to structure the system better.
New reviews get added regularly.
What AI engineering actually looks like in 2026
AI engineering in 2026 is no longer about picking a model — it's about wiring real systems against real data with audit-grade observability and compliance-grade security. UseAIEasily ships six service lines from Budapest covering the full production stack: AI agent development with LangGraph and CrewAI, retrieval-augmented generation (RAG) on Pinecone / Qdrant / pgvector, LLM fine-tuning and distillation, custom AI development, AI strategy consulting, and AI security audits against OWASP LLM Top-10 threats. Every line uses the same five-step delivery flow: scoping call, written SoW, build sprints with weekly demos, production hardening, 30-day warranty.
We chose this six-service surface because it's what an EU mid-market or enterprise team actually buys in a 12-month window. A typical engagement starts with a Maturity Assessment workshop (€2,500), follows with one or two concrete builds (€5,000–€25,000 each), and then either rolls into an Embedded retainer (€8,000+/month) or hands off entirely to your team. We don't do dashboards-as-a-service. We don't do retainers without code shipping. We don't do strategy decks that recommend hiring more strategists.
Behind every service is the same engineering bench: senior IC's with 6+ years building production systems before LLMs were viable, EITCA/AI-credentialed by the European IT Certification Institute, working in Budapest's CET timezone with overlap into UK / DACH / US-East workdays. We've shipped 30+ production AI systems for clients across Hungary (FinPálya, Dr. Horváth és Társai, NovaLeads, Medisync), DACH, the UK, and US, and every code repository, prompt file, eval dataset, and fine-tuned weight transfers to the client on day one of go-live. No reseller agreements, no model licensing tail, no offshore handover risk.
LangGraph, CrewAI, OpenAI Assistants, MCP. Tool-use, planner-executor, supervisor patterns. Cost limits + audit logs + human-in-the-loop gates baked in.
Hybrid retrieval (BM25 + dense), reranking, citation-grade answers, eval suites. Pinecone, Qdrant, pgvector, ChromaDB — chosen per data shape and scale.
Domain-specific SFT + DPO. Our customer benchmarks: +14.3% accuracy and -38% inference cost vs GPT-5 baselines on production traffic.
OWASP LLM Top-10 audit, prompt-injection defense, RBAC, output validation, EU AI Act alignment, GDPR-safe data flows.
Try it · 6 interactive demos
Five small things you can use right now in the browser. No signup, no email, no tracking. They run locally; the math and copy are real.
FAQ
Answers to the most critical security, timeline, and deployment questions regarding enterprise AI adoption.
Absolutely. We implement Enterprise Privacy Proxies and use zero-retention API agreements. Your data is stripped of PII before ever reaching a model, and it is strictly prohibited from being used to train public foundation models like ChatGPT or Claude.
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