Edge ai Services
We close the gap between a working ai model and a production Edge deployment, from first conversation through to a system that deploys.Â
CTai LABS, a department of Connect Tech Inc.
Published June 2026Â |Â Â Updated June 2026Â
Key Takeaways
Most Edge ai projects don’t survive the handoff between separate hardware, software, and integration vendors: hardware chosen after the model is built, sensor data that doesn’t match what the model was trained on, and no one owning the point where those pieces meetÂ
CTai LABS closes that gap by keeping every layer under one team: hardware selection, board support package, sensor integration, model optimization, and validation, on Connect Tech-designed computeÂ
Eight services cover the full project lifecycle, consulting, ai integration, full-stack engineering, proof of concept, platform migration, model optimization, sensor integration, and dedicated team engagement, so a project can enter and stay with one team rather than restarting at each stageÂ
Every engagement produces a deployment-ready outcome:Â validated, documented, and supported in production, not a deliverable that requires another team to finish
Edge ai Services
One team. Every layer. Deployment-ready outcomes.
Most Edge ai projects don’t fail because the model doesn’t work. They fail in the space between the model and the hardware it has to run on: a board selected too late, a board support package (BSP) written by a team with no visibility into the hardware, sensor data that doesn’t match what the model expects. CTai LABS exists to close that space. The services below cover the work from initial feasibility through to a supported production deployment, all under one team, so a project doesn’t restart every time it changes hands. Some engagements start at one service and expand into others as the project develops. If you’re not sure where yours fits, the Book a Demo call is where that gets sorted out.Â
Plan Your Project
Edge ai Consulting
Most Edge ai pilots never reach production. According to IDC and Lenovo’s joint research, of every 33 proofs of concept a company starts, only about 4 make it live (IDC & Lenovo, 2025), because the hardware, thermal, and integration reality gets confirmed after the model is already built. CTai LABS runs Edge ai Consulting as the stage that fixes that order: architecture review, platform selection, and feasibility assessment before a single line of production code is written.Â
Edge ai Proof of Concept​
A model that hits 94 percent accuracy on a clean validation set can degrade badly against a live camera feed, vibration, heat, and a single-digit-watt power budget, exactly the gap CTai LABS’ Edge ai Proof of Concept is built to expose early. A structured feasibility build on real Connect Tech hardware, with a defined success metric and a clear go or no-go outcome, run on the same architecture the project will scale on, so nothing has to be rebuilt between proof and production.Â
Build & Integrate
ai Integration Services
A model that works in a lab and a model that works in production are not the same claim, and the gap between them is where CTai LABS’ most common engagement starts. ai Integration Services is the full-stack path: architecture, board bring-up, sensor integration, camera integration, model optimization, and a validated handoff, run by the same team from first sensor to shipped system, with no vendor boundary in between for a bring-up issue to get lost in.Â
Full-Stack ai Engineering
When hardware, BSP, sensors, and model optimization sit with different vendors, the gaps between them are where Edge ai projects stall. CTai LABS owns all layers, Connect Tech carrier board, BSP, NVIDIA software stack, sensor pipeline, model optimization, and application interface, under one team, so there’s no seam for an integration issue to disappear into.Â
Optimize & Migrate
x86 to Jetson Migration
Porting an x86 codebase to Jetson looks straightforward until unified memory, ARM64 dependencies, and BSP bring-up start consuming the schedule. CTai LABS manages that migration end to end, workload assessment, platform selection, ARM64 software porting, BSP bring-up, TensorRT optimization, and full integration validation, so a team moving to Jetson Orin or Jetson Thor gets the migration done without the mid-project stall that usually comes from doing it piecemeal in-house.Â
ai Model Optimization
Teams that already have a trained model don’t need the full integration path, they need it optimized for the exact hardware it’s about to run on: TensorRTÂ conversion, INT8/FP16 quantization, and power-mode tuning, performed on the actual target hardware, not a desktop proxy, and validated against the real production sensor data. Scoped as a standalone engagement for when the model is already built and the only gap left is making it perform.Â
Memory Optimization
Memory, not raw compute, is usually what decides whether a deployment holds its performance once it’s shipped: unified memory shared with the OS, a KV-cache that grows with every token generated, and a footprint that has to leave room for the rest of the pipeline running alongside it. CTai LABS optimizes trained models against that real constraint: INT8 and FP16 quantization tuned to the target module, unified memory configuration, and KV-cache budgeting sized to the actual context length a deployment needs, validated on the hardware it will ship on. The result holds its performance in production instead of only working in a benchmark with more memory to spare.Â
Complete the System
System and Sensor Integration
Sensor bring-up is the stage most Edge schedules quietly lose to board flashing, timing validation, and driver conflicts. CTai LABS’ Sensor Integration service handles it across cameras (MIPI CSI-2, GMSL2/3, FPD-Link III), LiDAR, IMUs, radar, and CAN, performed by the same team that optimizes the model against the data those sensors actually produce, and validated through SIL, HIL, and Digital Twin testing before the system ever leaves the lab.Â
Hire an Edge ai Team
Building an embedded ai team from scratch means hiring for BSP development, NVIDIA platform expertise, and multi-sensor integration separately, then hoping they work as one unit under deadline. CTai LABS operates as that team instead: 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.Â
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Resources and Frequently Asked Questions
Related
Services. Explore the full range of CTai LABS engagements, from proof of concept through consulting and full-stack integration.
Edge AI Consulting. The broader engagement model a PoC often feeds into: architecture, integration, and a deployment-ready handoff.
About CTai LABS. D.A.V.E., the team, and Connect Tech’s NVIDIA Elite Partner standing.
Jetson Orin vs. Thor. The platform decision framework for sizing a PoC correctly from the start.
Read: Jetson Orin vs. Thor →
x86 to Jetson Migration. For teams whose PoC points to a platform change, the technical path from x86 hardware to the NVIDIA Jetson line.
Sources
CIO. (2025, March 25). 88% of AI pilots fail to reach production — but that's not all on IT.
https://www.cio.com/article/3850763/88-of-ai-pilots-fail-to-reach-production-but-thats-not-all-on-it.htmlRyseff, J., De Bruhl, B. F., & Newberry, S. J. (2024). The root causes of failure for artificial intelligence projects and how they can succeed: Avoiding the anti-patterns of AI. RAND Corporation.
https://www.rand.org/pubs/research_reports/RRA2680-1.htmlFrequently Asked Questions
Why do most Edge ai projects fail to reach production?
The research is consistent. IDC found that for every 33 proofs of concept a company runs, only about 4 reach production. RAND put the overall ai project failure rate at over 80 percent (IDC & Lenovo, 2025; Ryseff et al., 2024). The common thread isn’t the model, it’s the handoff: hardware selected after the model is built, sensor data that doesn’t match training conditions, and no team owning the point where those pieces meet. CTai LABS’ services are built around removing that handoff, not adding another vendor to it.Â
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.Â
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.Â
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.Â
Does CTai LABS only work with Connect Tech hardware?
CTai LABS is strongest when the ai runs on Connect Tech solutions, because the same team owns the hardware, the BSP, and the software stack. We support many module platforms such as NVIDIA Jetson, Toradex, and COM Express®. 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.Â
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.Â
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