Every project,
a lesson.

Context

A national retailer with 200,000+ active customers was facing a support crisis: inquiry volume had grown by 340%, the team couldn't keep up.

Approach

We started with a week of shadowing — we analyzed 10,000 previous tickets, identified that 78% of questions fell into 23 distinct patterns. We built an AI chatbot trained specifically on these patterns.

Technical challenge

The existing ticketing system was legacy and didn't expose modern APIs. We built custom middleware that transformed a 2009 system into a modern integration point.

Context

A top law firm managed over 500 contracts monthly — each requiring manual data extraction by junior lawyers. 2-3 hours per contract.

Approach

We built an AI pipeline specialized in Romanian law, trained on a corpus of 50,000+ anonymized contracts. The system automatically extracts: contracting parties, deadlines, critical clauses.

Technical challenge

Romanian legal language has particularities that didn't exist in standard models. We created a specific post-processing layer that increases accuracy by 23% vs off-the-shelf models.

Context

A SaaS startup with a complex product was receiving 800+ leads monthly, but the 3-person sales team could qualify at most 150. The rest were lost or waited too long.

Approach

We built an AI agent that picks up leads immediately after opt-in, conducts a personalized qualification conversation, answers technical questions, and automatically schedules demos.

Technical challenge

The product was technically complex. The agent needed to answer real technical questions without creating false expectations. We built an intelligent escalation system.