Measurable Impact

We deployed a Natural Language Processing (NLP) engine based on foundational models fine-tuned with legal jargon. The system ingests the entire Data Room in hours, automatically classifies documents, extracts key entities, and exclusively highlights high-risk clauses. The legal team receives an interactive dashboard where the algorithm has red-flagged documents requiring expert human review.

We developed a digital twin of the supply chain powered by predictive algorithms. The system analyzes not only sales history but also ingests dozens of exogenous variables in real-time: local weather forecasts, holiday calendars, raw material price fluctuations, and even social media sentiment. The model issues automated alerts recommending preventive purchases or stock redistribution.

We deployed an advanced semantic matching system that understands the real context of the candidate's experience, not just empty keywords. The model infers transferable skills and cross-references profiles with the company's current top-performers. The algorithm hides demographic information to guarantee a 100% blind and objective screening. Recruiters receive a ranked shortlist of the top 10 candidates.

Hexama implemented a neural OCR pipeline coupled with a Large Language Model (LLM). The system digitized and 'read' 50,000 historical contracts in a single weekend. It extracted expiration dates, SLAs, and penalties, structuring all this information into a relational database. Furthermore, it automatically audits every new incoming contract against the company's legal playbook, flagging deviations before signing.

A Propensity to Buy engine was developed directly integrated on top of Salesforce. The algorithm analyzes the client's entire digital footprint: email open rates, proposal reading time, corporate web interactions, and public financial data. With this data, it daily recalculates a 'Closing Score' from 0 to 100. The CRM now automatically sorts the rep's tasks, telling them exactly who to call today.

We installed high-frequency IoT sensors on critical machinery that stream ultrasonic vibration and temperature data. We applied Deep Learning algorithms that learn the 'acoustic footprint' of the machine in its optimal state. When the machine begins to wear out, the algorithm detects micro-anomalies imperceptible to humans and issues an early warning, allowing parts to be replaced without stopping production.

We developed a Reinforcement Learning engine that acts as an autonomous 'Price Trader'. The system monitors competitor prices in real-time via web scraping, analyzes warehouse stock levels, historical price elasticity, and even the time of day. From there, it automatically adjusts prices penny by penny within predefined business safety bands, ensuring total profit is always maximized without losing competitiveness.

We deployed a conversational assistant based on RAG architecture hosted on the client's private infrastructure (zero data leakage). We connected the AI to SharePoint, Confluence, and Drive. The employee asks the Copilot in natural language. The AI reads all documents, synthesizes the exact answer, and cites the original document, resolving the doubt in 3 seconds instead of 3 days.

We implemented Graph Neural Networks capable of understanding complex relationships between accounts, IPs, devices, and transaction frequencies. The model evaluates each payment or claim in under 50 milliseconds. Instead of static rules, the algorithm detects anomalous behavioral structures instantly. It blocks fraud in real-time and minimizes friction for legitimate customers.