Results That Speak for Themselves
Real-world deployments across industries—each built to solve a specific problem with measurable outcomes.
Clinical Trials Intelligence
Unlocking structured insight from dense, complex medical research at scale.
Extracting structured clinical data from dense medical research papers—manually intensive, error-prone, and slow.
ParseQa extracts quantitative & qualitative data from unstructured text, tables, and images with natural language querying.
Faster insights, reduced manual effort, and improved decision-making across life sciences R&D workflows.
Intelligent FAQ Bot
Moving beyond keyword matching to true document-level semantic understanding.
Traditional FAQ bots rely on keyword matching and lack document semantics—leading to missed answers and user frustration.
ParseQa enables semantic indexing of unstructured content across formats for intelligent, contextual question answering.
A dynamic FAQBot that’s intuitive, scalable, and quickly customizable across legal, compliance, and fintech domains.
AI-Powered Production Planning
A conversational LLM copilot with specialized agents for demand, inventory, resource, and logistics planning.
LLM-based Copilot + specialized agents for Demand, Inventory, Resource & Planning — seamlessly integrated with ML engines, OR optimizers, and knowledge-search tools.
ML forecasts for demand & prices · OR algorithms for packing, scheduling & line optimization · Instant what-if scenario evaluation.
Parked Car Detection System
Computer vision for parking meters—handling real-world noise, variable lighting, and arbitrary sensor orientation.
Detection from parking meter images with no fixed sensor orientation, varied lighting, and noise from rain and smudge.
Automatic area-of-interest detection with robust handling of orientation variance, lighting, and weather-induced noise.
Production-deployed system achieving 95%+ accuracy across all real-world conditions, enabling automated parking enforcement at scale.
