Every project starts with a business problem. Here's how we solved them.
A Czech e-commerce company selling consumer goods across multiple channels replaced manual order processing with a unified automated platform. The system connects their web store, Heureka, Mall.cz, and wholesale partners into a single workflow that handles everything from order intake to shipping label creation. With over 150 orders per day flowing through the platform, the operations team shifted from repetitive data entry to focusing on supplier relationships and catalog growth.
A 30-person B2B services company replaced an expensive, underused Salesforce setup with a custom CRM that their sales team actually adopted. The new system was designed around their real workflow, not the other way around. Within weeks of launch, pipeline visibility went from zero to complete, and the average deal cycle dropped by 40%. The company also saved over $5,100 per year in licensing costs by moving off Salesforce entirely.
A mid-size B2B SaaS company was outgrowing its support queue. Repetitive questions arrived faster than a lean team could answer them, and adding AI through the helpdesk meant paying a fee on every AI-resolved conversation. We built a custom retrieval-augmented (RAG) support agent that answers from the company's own documentation and resolved tickets, escalates to a human the moment its confidence drops, and plugs into the helpdesk the team already uses. Repetitive tickets get deflected, answers arrive in seconds instead of hours, and the cost stays flat no matter the volume.
A Czech wholesale distributor was drowning in supplier invoices and paying per-document fees to a SaaS OCR platform. We replaced it with a custom pipeline built around a vision-capable LLM that reads any invoice format, extracts line items, categorizes costs, and posts straight into ABRA Flexi. Manual data entry that used to eat most of an accountant's week now runs with light human oversight, and every invoice stays on the client's own servers.
A mid-sized logistics operator ran five separate platforms (warehouse, transport, accounting, CRM, and an HR portal) with no shared data between them. Instead of a risky rip-and-replace migration, Bitvea built a custom integration layer that connects each system through its API, normalizes the data into one shared model, and drives a unified dashboard plus cross-system automation. This page walks through how that architecture works, and why an aggregation layer beats replacing tools you already depend on.
A fast-scaling fintech had made several costly senior mis-hires, and its engineering leads were losing a large part of every week to unstructured interviews. Bitvea took over technical screening. We calibrated a scoring rubric against the client's own strongest engineers, then ran every candidate through a structured interview, an architecture challenge built on the client's real system, and a live reasoning session designed to separate genuine signal from a polished, AI-assisted performance. Leadership only met candidates who cleared every stage. The details here are generalized and the client is anonymized.
A healthcare SaaS company preparing for ISO 27001 certification needed a thorough security audit of their platform, which processes sensitive patient data for over 200 clinics. Bitvea performed white-box penetration testing with full source code access, uncovering 23 vulnerabilities: 3 critical, 7 high severity, and 13 medium. One critical finding was an authentication bypass that could have exposed patient records. All issues were remediated before the certification audit, and the company passed on the first attempt. The entire engagement, from initial scoping to the final remediation report, took 3 weeks.
Before you buy a company, take on a partner, or sign a major supplier, the other side controls what gets disclosed. Open-source intelligence (OSINT) due diligence adds an independent layer. We check public records, corporate registries, sanctions and watchlists, litigation, and adverse media to verify what you were told and surface what you were not. This work is confidential, so everything below is anonymized and generalized. We describe how we run it, not who we ran it for.