The result: a regulatory team that accesses critical business information in moments, not hours. For organizations working through healthcare digital transformation, this story points to a principle Brillio brings to every enterprise AI engagement. Accuracy and explainability have to travel together. Speed without trust is noise. Speed with trust is something else entirely.
Revolutionizing healthcare data: boosting efficiency and security with GenAI solutions
Data trust is fragile. When teams stop believing the numbers in their dashboards, they stop using those dashboards. That’s not a technology failure; it’s an organizational one, and it’s surprisingly common in large healthcare companies operating across multiple geographies.
For a global healthcare company with a century of history, the problem ran deep. Inefficient ETL pipelines, inconsistent data quality, and a patchwork BI ecosystem across Qlik and Tableau had eroded confidence in the insights the data was supposed to generate. Field teams across Sales Planning, Patient Engagement, and Field Force Effectiveness were working with incomplete pictures.
Brillio modernized the ETL architecture to cloud-based infrastructure and deployed GenAI solutions targeting data management and quality use cases. Self-service BI dashboards replaced request-and-wait cycles. LLM applications gave field teams faster, more direct access to the insights they needed. SLA-driven support, ongoing automation, and tighter governance across the full data value chain meant the gains were durable, not just a launch-week spike.
Strengthened data security and compliance came with the territory. So did high platform availability with minimal downtime globally. For healthcare IT leaders working through data operations and compliance challenges, this story shows what it looks like when the data foundation is actually fixed rather than patched.
Simplifying LLM deployment: AI innovation with a Gen AI gateway
LLM proliferation is a real organizational problem. When every team builds its own AI models with its own tooling, governance disappears. Costs balloon in ways nobody predicted. Security gaps appear in places nobody checked. The central AI team becomes a bottleneck because it’s the only group with enough context to know what’s actually running in production.
A global healthcare company operating in over 160 countries was living this tension. More governance and slower approvals weren’t the answer. The fix had to make doing things correctly the path of least resistance.
Brillio built a cloud-agnostic, microservices-based Gen AI gateway giving business units self-service access to a curated set of Gen AI capabilities: LLMs, Retrieval-Augmented Generation, prompt libraries, translation, speech-to-text, and text-to-code. Trust, risk management, and security were embedded at the infrastructure level rather than left to individual teams to enforce. Cost tracking and usage visibility came standard.
AI development velocity increased measurably. Dependency on central teams dropped significantly. For enterprises thinking about how to scale enterprise AI solutions, this kind of LLMOps architecture bridges the gap between AI ambition and production reality. Agentic AI in healthcare only works when the infrastructure beneath it is trustworthy.
AI-powered automation: driving 30% efficiency gains for a global pharma giant
Deviation investigations in pharmaceutical manufacturing are high-stakes and high-volume. When something goes wrong in production, the investigation team traces the cause, links related incidents, and resolves the issue before it compounds. Manual extraction and categorization of deviation data is slow and inconsistently executed, which makes root-cause analysis harder than it needs to be.
For a Fortune 500 biopharma company, that manual dependency was creating real productivity drag. The investigations team needed to move faster and make fewer errors, without sacrificing rigor.
Brillio’s solution combined Vault, Amazon Redshift, and AWS Glue ETL with an LLM-powered analysis architecture. KeyBERT handled key issue extraction. Sentence-BERT generated semantic embeddings for each deviation report. Cosine similarity then identified links between related reports, surfacing patterns that manual review would routinely miss. The output was an interactive, centralized dashboard giving the investigations team actionable insights rather than raw data.
Keyword extraction accuracy improved by 90%. Deviation resolution time fell by roughly 30%. Similarity detection improved by 80%, and overall operational efficiency increased 25-30%. For life science consulting engagements focused on AI digital transformation, those numbers make a clear case: replacing manual categorization with LLM-powered analysis isn’t just an efficiency play. It’s a quality play.
From hours to seconds: transforming document translation with AI for a Fortune 500 pharma leader
Clinical documents don’t follow language borders. A single R&D workflow might require researchers to extract specific content from Chinese regulatory submissions, French clinical summaries, and English study protocols, all within the same day. The manual process was consuming time and people who could be doing higher-value work, and translation errors in a regulated context carry real consequences.
Translation speed was only part of the challenge. Clinical documents carry complex structure: tables, headers, embedded figures, formatted sections. A translation that loses structure loses meaning. Key medical entities, including drug names, dosing details, and safety signals, had to be preserved with precision that general-purpose tools couldn’t reliably guarantee.
Brillio built a machine translation engine on Azure AI Translator Service with document structure preservation built in from the start. Text analytics extracted medical entities and relationships from the translated output and stored them in a searchable knowledge repository. The platform supported multilingual document searches end to end.
More than 200 clinical documents were translated. Content search time dropped by approximately 95%. Translation accuracy reached 90-95%. For organizations focused on digital health and smart clinical trials for life sciences, this kind of capability changes what research teams can realistically accomplish in a given sprint. Work that once took days now takes seconds.
