AI Business Process Automation — Built
for Production

Your team spends time on work that doesn’t require human judgment. We build AI-powered process automation systems that handle it — connected to your real data, deployed to your infrastructure.

Reduce manual effort. Eliminate errors. Lower operational costs

Official partner
Official ambassador

Key numbers

Years of iPaaS &
automation expertise
4 +
Complex integration workflows deployed
60 +
Manual hours
eliminated annually
10 K
Average reduction in operational costs
50 %
Reduction in manual data entry errors
60 %

Meet the n8n ambassador behind our automation practice

Roman Rodomansky, COO and Co-Founder at Ralabs, is an official n8n Ambassador – a recognition given to community leaders who help people build real products using automation and AI workflows.

Roman organized Valencia’s first n8n community meetup, with nearly 100 attendees, 160+ sign-ups, and conversations about automation, AI agents, and workflow design. That community knowledge goes directly into how we build for clients.

ROMAN RODOMANSKY

COO & Co-Founder

at Ralabs

Our services

Connect your systems. Automate the work. Optimize the process

01

Low-code and no-code system integration

Connect your business tools — ERP, CRM, HR systems, project management platforms — using modern middleware. No custom code from scratch. Prepackaged components, logically connected into pipelines that move data between your systems automatically.

02

Intelligent document processing

Extract, classify, and route structured and unstructured documents — claims, contracts, onboarding forms, medical records, invoices, KYC packets. AI reads what your team used to read and routes it where it needs to go.

03

Agentic AI workflows

Multi-step AI agents that plan, execute, and self-correct across your existing systems via APIs. Not a chatbot — a system that completes tasks, handles exceptions, and escalates only when it should.

04

ETL and data synchronization

Extract data from your source systems, transform it according to your business rules, and load it into the target — automatically, on a schedule or on demand. Consistent data across platforms without manual reconciliation.

Business process automation case studies

Problem areas

The work that’s costing you time and money

Systems that don't connect

Data lives in multiple tools — ERP, CRM, HR, and finance.
Nobody built the bridge. People fill the gap manually. We automate the handoffs. → Data flows automatically

Underestimated complexity

What looks like a simple connection often hides legacy systems, non-standard APIs, and security configurations that add weeks
to a project. We surface this during discovery — before it becomes
a delivery problem. → No hidden complexity at kickoff

You have automation, but it doesn't scale

Many teams have something automated — a legacy script, an old integration. It worked then. Now the volume is higher, the requirements have changed, or the connected system no longer behaves the same way. We assess what’s worth keeping and extend from there. → Scalability of business processes

Compliance in regulated industries

Healthcare and fintech teams need automation they can prove works. We build governance into the architecture from day one — audit trails, data masking, human review checkpoints. HIPAA, GDPR, and SOC-2 by default. → Audit-ready from day one

Ready to automate the work your team shouldn't be doing?

Tell us about your workflow. We’ll tell you what’s automatable, what isn’t, and what a realistic build looks like.

No commitment. Senior specialists on the call. Response within 1 business day.

Aleksandar Bogdanovski

Senior Automation Engineer at Ralabs

Our process​

01
Process Discovery

We map the actual workflow before configuring any platform — volume, edge cases, system handoffs, and exception handling. We find scope creep before it finds you.

02
Tech Stack Selection

We select the right platform for your security requirements and data volume. Enterprise clients: MuleSoft, Tibco, Boomi, Azure Logic Apps, Workato, Celigo. Smaller implementations: Make, n8n, Zapier.

03
Pilot Deployment

A focused build targeting one workflow in your real environment. Functional pilot target: 10–12 weeks.

04
Monitoring and Support

Documentation, error handling, and monitoring configured after go-live. Post-deployment support available on a contract basis.

Industry workflows we automate

Fintech

KYC and AML verification across systems, loan approval flows with multi-step validations, transaction monitoring with
real-time alerts, and compliance reporting with audit trails

Healthcare

Patient data coordination between clinical and administrative systems, prior authorization processing, document routing between departments, and staff scheduling workflows with fewer manual handoffs

Real Estate

Lease approval and contract routing, tenant onboarding across CRM and property management systems, maintenance request workflows, and centralized team-tenant communication

Media

Content distribution across publishing channels, automated metadata tagging, rights and licensing coordination, and asset management across production and editorial systems

Cybersecurity

Infrastructure monitoring with automated incident escalation, threat signal aggregation across security tools, audit-ready activity logging, and cross-team response coordination

Logistic

Carrier and warehouse synchronization, shipment exception handling, vendor communication workflows, cross-system inventory visibility, and delay escalation

Tech stack

Why Ralabs?

Human expertise, not AI-assisted guessing

AI accelerates our work. It does not replace the expertise behind it. When you hit a real blocker — a legacy system with
no API, a corporate firewall, an architectural call under pressure — experience is the only thing that helps. Our specialists use AI to move faster. Not to learn the job.

Quality by design

We compete on expertise. For enterprise clients in regulated industries, the cost of a failed integration — missed deadlines, compliance gaps, data errors — far exceeds the cost of doing
it right.

We refactor what works

We don’t rewrite everything. Most teams have existing automation in some form — legacy scripts, old integrations, manual workflows that have been running for years. We assess what’s worth keeping, what needs replacing, and what can
be extended.

No lock-in

We build to open standards and document everything. When
the project ends, you own it — the pipelines, the configuration,
the documentation.

Our clients say

Total reviews

45+

Average rating

4.9

Source

Let’s talk solutions

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    Roman Rodomansky

    CTO & Co-Founder at Ralabs

    Andrii Yasynyshyn

    CEO & Co-Founder at Ralabs

    FAQ​

    AI business process automation uses machine learning, natural language processing, and AI agents to handle repetitive or complex business workflows without fixed rules. Unlike traditional automation, it adapts to variability, processes unstructured data, and manages exceptions without manual intervention. Most implementations are built on low-code and no-code integration platforms — middleware that connects systems without writing custom code from scratch. It is used in document processing, approval workflows, fraud detection, and claims intake across fintech, healthcare, and logistics.

    The highest-impact use cases share two characteristics: high transaction volume and high manual effort. Document processing — claims, contracts, invoices, KYC packets — is the most common starting point. After that: fraud detection and transaction monitoring in financial services, patient triage and medical record classification in healthcare, and multi-step approval routing in operations-heavy workflows.

    A focused pilot targeting one workflow typically reaches production in 10–12 weeks. Smaller implementations — connecting two or three systems for a contained use case — can run two to four weeks. Enterprise projects involving multiple systems, high data volumes, and compliance requirements run six to nine months. We scope before we estimate — we won’t give you a timeline before we understand the problem.

    Governance is built into the system architecture from the first design decision — data masking at the pipeline level, role-based access controls, complete audit trails for every AI action, and human-in-the-loop checkpoints for high-risk outputs. We have production experience in both environments and know what auditors look for.

    The decision comes down to two things: security requirements and data volume. Enterprise platforms handle higher throughput, complex corporate security configurations, and regulated environments where auditability and compliance are non-negotiable. n8n, Make, and Zapier are better suited to contained implementations with modern APIs, lighter data loads, and faster build cycles. There is a point where a smaller tool reaches its limits — we identify that before you build into it.

    Yes, in most cases. We assess the existing architecture first — what’s working, what’s brittle, what can be extended. AI layers integrate into current stacks via APIs and standard interfaces. A full rewrite is rarely necessary and almost never the right starting point.

    Robotic process automation automates UI-level interactions — clicking buttons, filling forms — and works by mimicking human actions on a screen. What we do is system-level integration using low-code and no-code platforms: connecting applications through APIs and data layers, without scripting UI interactions. These approaches solve different problems.

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