The test of a lab is what leaves it.
Our AI Lab brings together applied research and the engineering behind intelligent products—from computer vision and document intelligence to language models and agentic systems that interact with real business applications. We develop, test, and integrate these capabilities into software designed to work beyond the demonstration.
Provides technical depth: exploring emerging techniques, publishing findings and developing intellectual property.
Turns techniques and delivery experience into dependable systems.
Shows what has been built, tested against real product and business requirements.
Capabilities come from both research and practical delivery.
Six areas of engineering depth.
Each capability page says where the technique breaks and when conventional engineering is the better choice.
Agentic AI & Workflow Orchestration
Agents that interpret goals, plan multi-step activities, and coordinate actions through applications and connected tools. We engineer the capabilities, permissions, validation, and execution controls that allow AI to participate meaningfully in business workflows.
Language Models & Knowledge Systems
Language models powering conversational experiences, contextual retrieval, structured generation, and intelligent interfaces. We engineer how models work with application context, data, and validation so their outputs can become useful product functionality.
Read the capability →Intelligent Document Processing
Complex technical documents converted into structured data, with hierarchy, warnings and cross-references preserved.
Read the capability →Computer Vision
Reliable information from images and video, with the model’s confidence visible.
Read the capability →Edge & On-Premises AI
Inference where the data is, when latency, privacy or bandwidth require it.
Read the capability →AI Engineering & MLOps
What keeps AI working after launch: testing, deployment, monitoring and clear ownership.
Read the capability →AI in real-world products
Agentic finance operations platform.
A production finance operations platform used by Medpanel, spanning six integrated business domains. Natural-language requests become structured plans over 35 registered application capabilities. The application’s authorization, validation and services govern execution, with human confirmation where appropriate.
Read the case study →Medpanel reports at least a fourfold reduction in manual document follow-up and related coordination through the integrated platform and automated workflows.
Technical document extraction.
Read the case study →Computer vision and natural language processing combined to extract, classify and structure complex technical documents with hierarchy preserved. Over 10,000 documents processed, roughly ten times faster than the manual workflow.
Aircraft turnaround monitoring.
Read the case study →Computer vision observes an airport’s existing camera feeds and records turnaround events with timestamps. It observes and records; operational decisions stay with the ground crew.
Edge AI for workplace security.
Read the case study →Proktor runs detection on the endpoint and uses privacy-conscious image handling, so it looks for defined security events instead of capturing everything. Proktor is a JIFFY.ai product that Founding Minds engineered.
Real-time sports analytics.
Read the case study →BullpenAI combines high-speed capture and computer vision to measure pitch trajectory, location and mechanics in real time, for use in training.
Our AI engineering extends into conversational interface generation, complex agent orchestration, and automation of legacy applications that conventional tools struggle to reach.
Research behind the engineering.
Patents and publications
Four US patent filings, two granted, and five peer-reviewed papers, covering object tracking, medical imaging and computational photography.
Research that makes better products possible
When a product calls for AI, we draw on this research to find promising approaches, understand their limits, and move faster where that makes sense. It also tells us when a technique isn’t reliable enough for a customer’s data, environment or requirements, which is as useful as knowing when it is.
Engineering depth behind both offerings.
Application Modernization
Introducing intelligence and agents into established systems, selectively, while preserving the business logic and data they depend on.
Application Modernization →MVP in a Box
Building new products with AI-native interfaces, generative functionality and agentic architectures where they fit.
MVP in a Box →Have an AI problem worth solving?
Whether you’re exploring AI for an established product or building something new, we’d like to understand the problem and discuss what’s practical.