Client wins: LLM platforms, translation engines, and financial intelligence
Consider the multinational technology corporation that needed to standardize its LLM development process across citizen data scientists, data scientists, and business stakeholders. Before Brillio stepped in, each persona operated in silos, creating release management headaches and inconsistent outputs. The solution was a centralized platform built on low-code and no-code foundations, with templatized pipelines for prompt engineering and fine-tuning baked in. LLM gateway integration using Azure OpenAI brought everything under one roof: faster model onboarding, better access management, and teams that could actually scale.
Then there’s the pharmaceutical story. Translating clinical research documents from French, Chinese, and other languages sounds manageable until you factor in file sizes, document structure, and the precision required for medical entities. Ninety to 95% improved translation accuracy isn’t an aspiration. It’s the measured result of building the right architecture for a very specific problem.
The British AI and ML software leader needed something different entirely: a GenAI-powered financial reporting tool that could scrutinize data, flag strengths and weaknesses, and deliver summaries fast enough to be useful. A caching mechanism alone cut Azure OpenAI costs and dropped latency from 12 seconds to two. That’s the kind of engineering decision that only shows up in the details, and only matters if your partner sweats them.
Client wins: Search, chatbots, and the operational case for GenAI
Search is where a lot of enterprise AI applications prove themselves or fall apart. For the global human capital management company, the challenge was extracting relevant information from dense ServiceNow and HR SharePoint knowledge bases. Users spent time they didn’t have sifting through documents when they needed answers in seconds. The GPT-powered chatbot Brillio built gave them focused user authorization, conversational capability, and latency under five seconds. Twenty-five-plus business users onboarded. Resolution time dropped.
The managed healthcare insurer’s story has similar bones but bigger scale. Fifty thousand-plus documents. Contextual search. Automated retrieval of customer benefits information that previously required manual effort. A 30% improvement in operational efficiency, a 10% reduction in claim settlement time, and 25% savings in penalties are the kinds of numbers that reframe the conversation about AI digital transformation from ‘interesting pilot’ to ‘we need this in production now.’
The North American building materials leader rounds this out. Branch managers and sales representatives needed a single source of truth across sales and inventory data with role-level security intact. The MS Teams bot Brillio developed used chain-of-thought prompting to handle complex, multi-intent queries and conversation memory to track follow-ups. Near-real-time responses, structured summaries, graphs that non-technical users can act on immediately. That’s what AI engineering services look like when they’re built for the people who actually use them, not just the people who commission them.
