Case Studies

Visual recognition models in safety-critical industries fail most often on the cases that matter most: rare events, unusual objects, and edge-case scenarios. The root cause is not algorithmic , it is a lack of training data, and collecting such data through conventional means is prohibitively expensive, dangerous, or impossible.

The client needed automated visual inspection on densely packed production lines and high-speed robotic arms. Traditional cameras were too bulky, overheated when miniaturized, and captured plaintext video feeds that posed security risks in classified facilities.

Client-facing technical teams needed AI-powered decision support during live meetings — not after them. Existing alternatives forced a tradeoff: either manually query a chatbot mid-conversation (high cognitive load, broken context) or rely on post-meeting transcription (useful for records, useless for in-meeting decisions).

A seed-stage venture capital firm needed to evaluate hundreds of inbound deals per year with a small investment team. Manual research across fragmented data sources consumed analyst hours that could be spent on higher-value judgment work, and many promising deals were passed over before receiving adequate review.

Recruiting and headhunting workflows involve large amounts of browser-based, repetitive work , navigating multiple web tools, extracting structured information, and performing standardized updates. AspiraTech needed reliable automation on real web UIs with secure isolation and auditable outputs.

The client needed to leverage LLMs to automate complex financial workflows and analyze proprietary data, but internal security policies and GDPR-related governance constraints made the use of commercial cloud AI APIs unacceptable.