Every sector has its bottlenecks. Six examples, one per sector, of what we know how to build — from automatic invoice reading to demand forecasting and financial reconciliation.
An industrial company was entering supplier invoices and documents into the system by hand, which caused delays, errors, and hours spent on repetitive tasks.
We built a flow that reads, validates, and records everything on its own:
Document capture via optical scanning and layout analysis
Duplicate detection before the entry is recorded
Automatic classification by supplier and cost category
Validation against business rules and entry into the system
What used to take too long now happens in a fraction of the time. Errors no longer slip through unnoticed, and the team traded data entry for work that actually needs people.
A healthcare provider received hundreds of messages a day, from clinics and from patients, and sorting them all by hand was slow, inconsistent, and delayed urgent cases.
We built an engine that reads, classifies, and routes each message in real time:
Tagging by topic and by priority
Routing to the right team
Automatic closing of simple cases
Extraction and organization of the data that matters
Urgent cases now reach the people who resolve them first, simple messages close on their own, and the team moves from manual triage to work that adds real value.
A transport operator received many repeated requests, which led to long waits, inconsistent answers, and high support costs, especially whenever there were service disruptions.
We built a conversational agent that handles first-line support across multiple channels:
Automatic answers to the most common requests
Continuous learning from real conversations
A central knowledge base for consistent answers
A smooth handoff to a person for complex cases
Customers get an answer any time, the answers stay consistent across every channel, and the team is freed up for the cases that truly need human judgment.
A retail company planned purchasing and stock based on loose estimates, which led to waste during slow periods and stockouts during peak demand.
We connected demand forecasting to day-to-day planning:
Demand forecasting by product and by period
Classification of products by turnover
Purchase planning aligned with the forecast
Real-time tracking of deviation from plan
Purchasing now follows real demand, waste went down, and planning stopped reacting and started anticipating.
A professional services firm lost hours reading contracts and documents to find clauses, deadlines, and figures — manual, repetitive work at risk of missing something important.
We built a system that reads the documents and returns what matters:
Reading and understanding the document
Extraction of the relevant clauses, deadlines, and figures
Alerts for dates and obligations that need to be met
Everything organized in one searchable place
Information is now at hand instead of buried in the pages, deadlines stopped slipping through, and the team focuses on deciding instead of searching.
A financial services firm consolidated data from banks, platforms, and spreadsheets by hand to reconcile transactions and prepare client reports — a slow close, with errors that were hard to catch, always up against the deadline.
We automated the full circuit, from data collection to the final report:
Automatic collection of statements and transactions from multiple sources
Automatic reconciliation with discrepancies flagged
Per-client reports generated in the right format
Exception alerts for human review
A close that took days now takes hours, discrepancies surface first instead of slipping through, and the team spends its time analyzing instead of checking lines.
This page grows project by project. The next cases, with measured results, land here as soon as they're in production.
Thirty minutes on your process — and you'll leave knowing what would be worth building.