AI agent case studies in different verticals

First-line support

Chat, email, messengers, several languages. First line was drowning in routine questions, management couldn't see who was asking what, and conflicts surfaced as complaints.

• Routine answers. From internal documentation, with a source link; what's not in the base goes to a human instead of being made up.
• Actions on request. Check a status, update data, request a document — the request is closed, not forwarded.
• Queue triage. Every request tagged by topic and intent: routing plus a picture of who asks what.
• Tone control. A conflict is escalated before it becomes a complaint; the 20% that reach people arrive with topic and priority.

A lawyer's working platform

70 lawyers, two languages, regulated work. Research, contract review and standard documents were done by hand across 130,000 documents.

• Research. Search, reading, checking against rules — an answer with sources you can open and verify.
• Contract review. 31 clauses, each with a risk score and deviations from firm standards; edits go straight into Word.
• Standard documents. Work descriptions and drafts assembled from rough notes instead of written from scratch.
• Control before the client. Where unsure — refuses; deadlines computed by code; promising a case outcome is banned by code.

Client service operations pipeline

A B2B service runs a client portfolio on data from their platforms: collect, analyse, decide, track, confirm, report. Every link was held by a person; the portfolio grew only with headcount.

• Data collection and analysis. From heterogeneous sources, without manual exports.
• Action planning. What to do and in which order — by expected effect, not by arrival.
• Execution control. The system tracks what's done and confirms the result by calculation.
• Client reporting. Every value is checked against source data; nothing goes out without a human signature.

Financial reporting that assembles itself

An iGaming affiliate. Analytics was assembled by hand: exports from different systems, reconciliation, numbers that never matched. By the time they were reconciled, the picture was stale.

• Data collection. The agent goes into the systems itself — no exports drifting through email copies.
• Reconciliation. Different schemas and definitions of the same metric brought to one picture.
• Monitoring. A discrepancy is visible the day it happens: the earlier found, the cheaper.
• Report delivery. To the work chat, on schedule and on event, with the ability to ask where a number came from.