TSX Sports AI
Computer vision pipelines for pitch registration, continuous player tracking, and automated tactical event reconstruction.
Published project record · implementation context includedAI product & systems engineering
From possibility
to production.
Alector Lab builds AI products for product and operations teams: business agents, computer vision, document intelligence and spatial applications, from a scoped pilot to production integration.
01 Perceive vision + multimodal data02 Reason models + decision systems03 Act products + automated workflows
Alector Lab project workflow reel
Reel description
A silent sequence of conceptual scenes representing sports analysis, AI-assisted products and operational workflows. The visuals do not show client data or measured outcomes.
Alector Lab, plainly
What we do—and how we work.
- What we do
- Design and engineer production AI products across agentic workflows, computer vision, multimodal systems and spatial computing.
- Who we work with
- Product and operations teams with a defined workflow, representative data and a need for reliable deployment.
- How engagements work
- Discover the constraints, validate against real data, engineer the full product, deploy in stages and operate with measurement.
- How evidence is handled
- Project records distinguish published scope, implementation context and measured outcomes so teams can assess fit before an engagement.
Selected work
Useful intelligence.
Real applications.
Nepean Air Conditioning
Autonomous inbound dispatch, technician routing, and structured ERP synchronization with human review escalation.
Published project record · implementation context included
Dandy AI
Multimodal property intelligence synthesizing deed records, GIS zoning layers, and automated valuation models.
Published project record · implementation context included
Timebrews
A centralized advertising CRM where users can schedule different campaigns across Android-powered LED displays in multiple locations and use AI Signage insights to understand audience demographics.
Published project record · implementation context includedWhat we do
Specialist thinking.
End-to-end engineering.
From a focused AI feature to a complete product, we work across the layers that make a system useful.
Agentic AI
Stateful agent execution graphs with formal schema verification, sandboxed tool execution, and human escalation gates.
02Computer Vision
Multi-camera tracking, 3D coordinate projection, and real-time edge tensor inference.
03Multimodal AI
Joint visual-language models, structured document parsing, and grounding across text, image, and video.
04AI Product Engineering
Scalable streaming runtimes, latency optimization, deterministic fallbacks, and reliable human-facing interfaces.
05Spatial Computing
Spatial computing, neural radiance fields (NeRF/Gaussian splats), and immersive WebXR / VisionOS interfaces.
Where it makes a difference
Built around your world.
The right technology starts with the people, decisions and constraints in your industry.
Explore all solutions
AI for Sports
Computer vision, multimodal match analysis and decision tools built around the realities of your sport, footage and coaching workflow.
AI for Software & SaaS
Turn raw model APIs into durable product features with evaluation benchmarks, streaming state graphs, and deterministic tooling.
AI for Media & Content
Automated video segmentation, multimodal asset tagging, semantic clip indexing, and generative production workflows.
AI for Logistics & Operations
Route optimization, field technician dispatching, computer-vision inventory audits, and resilient agentic orchestration.
How we work
Clarity at every stage.
01Discover
Define scope, data, and constraints.
Explore this stage
Understand the workflow being automated, the data available, and any latency or compliance constraints before writing a spec.
02Validate
Prototype against real data first.
Explore this stage
Build a scoped prototype and test it against real client data or transcripts before committing to a full build.
03Engineer
Build the full system, not just the model.
Explore this stage
Model orchestration, API integrations, and the interfaces the client's team actually uses day to day.
04Deploy
Staged rollout with monitoring.
Explore this stage
Phased rollout with logging and alerting in place before the system takes over the full workflow.
05Operate
Ongoing monitoring & iteration.
Explore this stage
Drift monitoring, cost tuning, and iteration as the client's data and requirements change.
From the lab
Notes on what matters.
Computer Vision & Sports AI · 10 min read
Inside 2026 Football Telemetry: Fusing 500Hz Ball Sensors, 50Hz Skeletal Vision, and Multimodal LLMs
2026-09-21
Computer Vision & Sports AI · 9 min read
The Tripartite Pitch: How Computer Vision, VLMs, and LLMs Are Modernizing Football Analytics for Elite Clubs
2026-09-18
Enterprise AI & Security · 8 min read
How to Secure Enterprise AI Agents: Identity, Permissions and Prompt Injection
2026-09-17