Full-stack ai engineering, from first conversation to production deployment, all under one team.
CTai LABS, a department of Connect Tech · Published June 2026 · Updated June 2026
Answer first
CTai LABS delivers Edge ai engineering services across the full project lifecycle: consulting and feasibility, ai integration, full-stack engineering, proof of concept, platform migration, model optimization, sensor integration, and dedicated team engagement. As a department of Connect Tech, an NVIDIA Elite Partner that designs and manufactures Edge compute hardware, the team brings hardware, BSP, software, and ai expertise under one team. Every service is scoped to produce a Deployment-ready outcome, not a deliverable that requires another team to finish.
The eight services below cover the work from initial feasibility through to a supported production deployment. Some engagements start at one service and expand into others as the project develops. If you are not sure where your project fits, the Book a Demo call is where that gets sorted out.
Edge ai Consulting
For teams that need expert input before committing to a build: architecture review, platform selection, feasibility assessment, and a clear path forward. Edge ai consulting is where projects get scoped correctly before engineering starts, which is the stage most failed deployments skip.
ai Integration Services
The full-stack work of connecting an ai model to real hardware, real sensors, and a Real-world environment so it runs reliably in production. Covers architecture, board Bring-up, sensor integration, model optimization, and a validated handoff. The entry point for most CTai LABS engagements.
Full-Stack ai Engineering
One team owns every layer simultaneously: Connect Tech carrier board, BSP, NVIDIA software stack, sensor pipeline, model optimization, and application interface. Built for teams that need the full stack delivered without assembling it from separate vendors and managing the handoffs between them.
See Full-Stack ai Engineering →
Edge ai Proof of Concept
A structured feasibility build on real Connect Tech hardware, with a defined success metric and a clear go/no-go output. Not an open-ended research project: a scoped proof of concept that answers the production question, not just whether the model works in a lab.
See Edge ai Proof of Concept →
x86 to Jetson Migration
Managed migration from x86 architecture to NVIDIA® Jetson™, covering workload assessment, platform selection, ARM64 software porting, BSP bring-up, model optimization with TensorRT, and full integration validation. For teams moving to Jetson Orin or Jetson Thor and needing the migration done without mid-project stalls.
ai Model Optimization
TensorRT conversion, INT8/FP16 quantization, and power-mode tuning for models that need to run within a real Edge power and thermal envelope. Optimization is performed against the actual target hardware, not a desktop proxy, and validated on the sensor pipeline the model will see in production.
System and Sensor Integration
Bring-up and integration of cameras across MIPI CSI-2, GMSL2/3, and FPD-Link III, plus LiDAR, IMUs, radar, and CAN, performed by the same team that will optimize the model against the data those sensors produce. Covers SIL, HIL, and Digital Twin validation before the system leaves the lab.
See System and Sensor Integration →
Hire an Edge ai Team
For organizations that need the full stack delivered without building an embedded ai team from scratch. CTai LABS operates as your Edge ai engineering team: one contract, one point of accountability, from architecture to a production-supported deployment. Suited to startups, OEMs adding ai to existing products, and programs with fixed delivery timelines.
Book a Demo
Not sure which service fits your project? Talk to a CTai LABS engineer. Bring your use case, your constraints, and your timeline.
Edge ai Services FAQ
What is the difference between ai integration services and full-stack ai engineering?
ai Integration Services covers the full-stack work of connecting an ai model to hardware and sensors, and is the most common engagement type. Full-Stack ai Engineering describes the specific capability that makes CTai LABS different: one team owning every layer simultaneously, including the hardware itself, rather than coordinating across vendors. In practice, most full-stack engagements include ai integration as a component.
Where should a new project start?
Most projects start with either Edge ai Consulting (if the feasibility or architecture is still open) or ai Integration Services (if the use case is defined and the work is ready to begin). The Book a Demo call is the fastest way to determine the right starting point for a specific project.
Can CTai LABS work on a project that already has a trained model?
Yes. ai Model Optimization and System and Sensor Integration can both be scoped as standalone engagements for teams that have a working model and need it optimized for the target Jetson platform, or need sensor bring-up and integration work without redoing the model development.
Does CTai LABS only work with Connect Tech hardware?
CTai LABS is strongest when the ai runs on Connect Tech carriers and NVIDIA Jetson modules, because the same team owns the hardware, the BSP, and the software stack. That is the structural difference that removes the most common integration failure points. The team will give a direct answer during scoping if the project requires a different hardware approach.
What does a typical engagement produce at the end?
A Deployment-ready validated system: documented hardware and software configuration, an optimized model running on the target platform, SIL/HIL/Digital Twin validation records, a RESTful API layer where the system connects to external infrastructure, and a production support path backed by Connect Tech’s BSP and software teams.
Implementation notes for the web team
Page role: This is the Services hub. It routes readers to eight sub-pages. Every sub-page should link back to this hub via a breadcrumb or contextual link.
JSON-LD schema: CollectionPage + BreadcrumbList + FAQPage + Organization. Consider adding ItemList schema for the eight service cards to support AI Overview extraction of the service directory.
Sub-page URLs: All URLs in this document use the pattern ctailabs.ai/services/[slug]/. Confirm final slugs with the web team. Pages already produced: ai Integration Services, Full-Stack ai Engineering, x86 to Jetson Migration, Hire an Edge ai Team. Pages not yet produced: Edge ai Consulting, Edge ai Proof of Concept, ai Model Optimization, System and Sensor Integration.
Internal links: Link this hub page from the site navigation under Services. Cross-link from the homepage to this hub. Once sub-pages are live, confirm all eight card links resolve correctly.
CTA URL: https://ctailabs.ai/book-a-demo/ — confirm live with web team before publication.
Updated date: Refresh when new service pages are added or existing ones are renamed.
