These are the problems ACE solves.

The Problem

Legal and notarial operations run on documents. Contracts, filings, certificates, and case files move through the office constantly, and the work of reading, cross-referencing, and extracting relevant information from them falls on highly trained professionals whose time is worth far more than manual document review.

What ACE Builds

An AI document intelligence system that ingests legal and notarial documents, extracts structured information, flags what requires attention, and surfaces relevant precedents or cross-references automatically. The system integrates into existing workflows and reduces the time professionals spend on document review by handling the routine extraction work end to end.

The Outcome

Legal and notarial teams spend their time on judgment and client work. The system handles the reading.

The Problem

Law firms take on cases based on experience and instinct. The data to make that decision more precisely already exists: case type, precedents, win rates, client profile, resource requirements, and expected return. Most firms never use it systematically. Partners spend time evaluating cases that analytics would have filtered out, and pass on cases that data would have flagged as high value.

What ACE Builds

An AI-powered recommender system that analyzes incoming cases against historical data, precedents, and firm-specific success patterns. The system scores each case across multiple dimensions and surfaces a ranked recommendation so partners spend their evaluation time on the cases most likely to succeed and most aligned with the firm's strategy.

The Outcome

The firm stops investing time and resources into cases that data would have flagged as low probability. Partners recover hours spent on intake evaluation. Operational costs tied to underperforming cases drop. The firm also captures revenue it was previously leaving on the table by passing on high-value cases that did not fit the intuitive profile but would have won.

The Problem

Most of what a company knows operationally lives inside people, not systems. When a senior person transitions out, that knowledge leaves with them. Job shadowing is unreliable. Documentation tools require a corpus that does not exist. The incoming person starts from scratch and the company pays for it in ramp-up time, errors, and lost institutional memory.

What ACE Builds

An AI-guided extraction system that captures operational knowledge from key people through structured async interviews before a transition occurs. The output is a set of documented SOPs, decision frameworks, and process maps that the incoming person can actually use. The system does not require existing documentation to function. It creates the corpus from scratch.

The Outcome

The company retains what it knows. Transitions take weeks instead of months. The knowledge base becomes a foundation for future automation.

The Problem

Fuel distributors receive weekly price updates and need to communicate personalized pricing to hundreds of clients based on each client's individual margin strategy. Salespeople do this manually, calculating each price, composing each message, and sending each update one by one. It consumes hours every week and the work adds no value beyond moving information from one place to another.

What ACE Builds

An automated system that ingests the weekly pricing email, calculates each client's personalized price based on their margin configuration, and sends individual pricing updates via WhatsApp automatically. Salespeople review and dispatch with a single click, or let the system run fully automated. Order intake is connected directly to the dispatch workflow so the path from price to purchase to fulfillment is a single continuous process.

The Outcome

What took hours takes minutes. Salespeople focus on selling. Pricing is accurate, consistent, and delivered on time to every client every week.