AI engineering studio · Romania

We accelerate your company's core processes.

Multi-agent systems that turn raw data into operational decisions. Built for teams where every risk and opportunity needs an auditable trail.

Schedule a discovery call See the framework

Live projects in HR and Finance

The CyberApps Operating Model

Five layers. Each measurable.

Every project moves through five layers. Each layer is defined, measurable, and traceable.

We identify the relevant signals across your existing data flows. CVs, invoices, market orders — each domain has its own pattern.

We build the trust layer: data consolidated, cleaned, validated. Without it, every AI decision is a costly guess.

Specialized agents generate contextual recommendations, not generic answers. Every output is traceable back to source.

We integrate recommendations into your operational systems — ERP, CRM, ATS, trading platforms.

We automate repetitive actions and escalate decisions that actually matter.

Verticals

Where we operate today

Two verticals live in production. The framework holds for any domain with enough data and a repeatable process.

AI for HR

Live with clients
Problem
Recruiting teams lose hours filtering irrelevant CVs.
Solution
A RAG agent that classifies, scores, and explains each candidate's relevance to the job description.

AI for Finance

Live with clients
Problem
Accounting documents land in fragmented workflows, with manual classification that introduces errors.
Solution
Automated pipeline for ingestion, OCR, classification, and validation. Clean data before it lands in your ERP.

Have another vertical in mind? Let's talk.

Methodology

How we make processes AI-Ready

We don't start with the model. We start with the data. What enters the trust layer determines what exits the decision layer.

  1. 01

    Data Ingestion & Trust Layer

    We select, clean, and validate the data that matters. We define the standards before training anything.

  2. 02

    Decision Support Architecture

    We design the agents and decision flows. Every output is traceable back to source.

  3. 03

    Process Migration

    We migrate processes to AI without removing people. We restructure, we don't replace.

Engagement models

How we work together

Three distinct phases, each with a clear deliverable. You can enter at any phase — most clients start with Discovery.

  1. 01
    ~2 weeks

    Discovery

    We map your data flows, identify the relevant decision thresholds, and evaluate technical feasibility. We deliver a document with scope, effort estimate, and a go/no-go recommendation.

  2. 02
    2-week sprints

    Build

    We build incrementally, not big-bang. The data pipeline, agents, and integrations ship to production progressively. You see weekly progress and can adjust course.

  3. 03
    Optional monthly SLA

    Run

    AI systems aren't set-it-and-forget-it. We provide monitoring, periodic retraining, and threshold tuning based on operational feedback.

Why CyberApps

Domain-agnostic, method-consistent

We work in HR, Finance, Trading — and the framework stays the same. What changes is the data layer and the domain logic.

Specialized agents, not LLM wrappers

Each agent is designed for a narrow, measurable task. We avoid generalist systems built around a single LLM — they fail silently in production.

Explainability by default

Every AI decision we ship comes with traceability. If you can't audit the output, you can't use it in production.

FAQ

Frequently asked questions

How long does a typical project take?
Discovery takes about 2 weeks and produces a feasibility report with scope and effort estimate. Total project duration depends on integration complexity, data volume, and data quality. We set a realistic plan after Discovery — we don't sell numbers from slides before understanding the system.
Do you only work with large companies?
No. We work with teams that have a repeatable, well-documented process and enough data for a decision layer.
Do you use your own models or external APIs?
Both, depending on data sensitivity, latency requirements, and operational budget. For sensitive or regulated data, we run models in your infrastructure or in a dedicated isolated environment. For generic cases, external APIs are often the efficient choice.
Who's on the team for a project?
Every engagement has a lead AI engineer, a data engineer, and a product engineer. Depending on complexity, we add specialists for integrations, frontend, or ML ops. For long-running projects, we assign a dedicated point of contact throughout.
What happens to client data?
Data stays in your infrastructure or in an isolated environment. Never in public training flows.
Do you offer maintenance after delivery?
Yes. Post-go-live support contracts cover agent monitoring, periodic retraining, and decision threshold tuning.

What's next

Let's start with a conversation.

30 minutes. No pitch. We figure out whether there's a fit between your problem and what we do.

Schedule a discovery call