Driving AI & Machine Learning Excellence, Our Portfolio

Challenge: Domains with high skilled processionals struggle to trust AI when handling complex documents and citations are vague. We built a pipeline that structurally decomposes complex medical prescription data, recognizes safety policies, and returns traceable answers with inline citations. The system optimizes retrieval granularity (from a 10 page document to a 4 sentence section) and filters hallucinations via guardrails and confidence thresholds.

Solution: A lift in answer precision and dramatic improvement in user satisfaction. Verus Data delivered robust parsing of rich documents, modeling, and deployment with data safeguards.

Challenge: Product teams spend weeks gathering scattered competitive intelligence and feature comparisons across fragmented sources. We developed an agentic research system that takes initial product concepts and systematically explores market landscapes, competitor features, and user sentiment patterns. Multi-agent workflows coordinate web research, feature extraction, and trend analysis across social and public data sources.

Solution: Instant market research cycles (~5 minutes) and more comprehensive feature gap identification. Verus Data designed the agent orchestration, data synthesis, and competitive analysis frameworks to give objective ratings.

Challenge: As families age, they struggle to recognize gradual cognitive changes and preserve fleeting stories before they’re lost. We designed an ambient listening system that passively monitors speech patterns for early decline indicators while capturing and organizing personal narratives. The proposed system listens and provides discrete alerts to caregivers and engages in gentle conversations and prompts story-sharing.

Solution: Individual and their care providers perceive high life quality and increased security in additional oversight from system interactions.

Challenge: Marketing teams waited almost a month for developers to translate campaign analysis requests into complex SQL queries. We built a text-to-SQL system that maps database schemas into conversational tokens, enabling natural language queries that generate expert-level SQL. We evaluated multi-pass validation, agentic flows, and layered queries to enable non-experts to achieve database precision previously requiring senior developers.

Solution: Campaign analysis cycles reduced from 3+ weeks to same-day delivery, with SQL experts shifting from repetitive query writing to system enhancement. Verus Data led designs of the schema mapping and query optimization strategies.