We are elated to share some of our real-world wins! These case studies show how we turn promising AI into dependable outcomes. Each project began with clear measures of truthfulness, speed, and cost, then moved to targeted improvements and ongoing oversight. Explore the summaries below, and feel free to dive into the full stories.
Our Services
Intraday operators needed quick, trustworthy explanations from a RAG workflow pulling live and historical data. We separated retrieval from generation, tightened grounding, and set latency budgets for peak hours. The result was fewer unsupported claims, faster first-token times, and steadier performance under load. A lightweight “quality digest” kept stakeholders aligned week to week.
A multilingual knowledge assistant had to stay faithful to policy and source material while serving EN/FR/DE users. We introduced policy-to-behaviour mappings, multilingual evaluation packs, and canary checks on real traffic. Hallucinations fell, policy adherence improved, and answer clarity rose without slowing responses. The team now ships changes with confidence using release scorecards.
A campus chatbot needed to guide students on deadlines, courses, and campus services while citing authoritative sources. We evaluated the dialogue layer for accuracy, tone, and safety, and audited the RAG pipeline for context recall, document freshness, and groundedness. Targeted tuning of chunking, re-ranking, and prompts lifted grounded-answer rates and reduced “sounds right” mistakes. Ongoing monitors now flag policy or content drift before a new term begins.