Services
ai Integration Services
From a use case and a timeline to a production-ready Edge ai system. One team. One contract.
No handoff in between.
By Ceri Nelmes
Tech Marketing Leader & Journalist, Connect Tech Inc.
Technical review: Rob Callaghan, Chief Product Officer, Connect Tech Inc.
Key Takeaways
- AI integration services take a working model and connect it to real hardware, real sensors, and a real operating environment so it runs in production, not only in a lab.
- CTai LABS delivers that path as a managed service across eight industries. The engagement covers architecture review, hardware and platform selection, sensor integration, model optimization, and a validated handoff.
- CTai LABS is a department of Connect Tech, which has designed and manufactured edge hardware for more than forty years. Buying the stack from one place avoids the gaps that show up when separate vendors each own only one layer.
- One team owns the carrier board, the board support package, the sensor pipeline, and the model. That shared ownership is what keeps projects from stalling at the handoff into deployment.
CTai LABS is Your Physical ai Integration Partner, working across the CTI EdgeAI Stack to connect Edge compute, BSPs and software, sensors, models, optimization, validation, and deployment. Instead of handing these layers between separate vendors, we help bring them together as one deployable system.
Why full integration services matter
The research on ai project failure is consistent. The IDC findings show for every 33 proofs of concept a company runs, only 4 reach production, which is an 88 percent attrition rate (IDC 2025). RAND Corporation put the share of ai projects that fail to deliver promised business value at 80 percent (RAND, 2024). Gartner forecast that 30 percent of generative ai projects would be abandoned after proof of concept by end of 2025 (Gartner, 2024). S&P Global reported that 42 percent of companies scrapped most of their ai initiatives in 2025, up from 17 percent the year before (S&P Global, 2025).
The failure typically happens when the model meets the real deployment environment and the team that built the model is not the team that owns the hardware, the sensors, or the thermal design. At the Edge specifically, the causes are predictable:
The hardware was selected too late
The model architecture is decided or built before anyone has confirmed the power envelope, hardware achitecture, or thermal headroom of the target board. Rework follows.
The data does not survive production
A model tuned on clean datasets meets the sensor pipeline that was integrated by a different team. Accuracy falls apart in the field.
Nobody owns the handoff
The engineer who built the proof of concept is gone by demo day. The production team inherits artefacts without context (Netguru, 2026).
Complexity is cited as the biggest barrier.
Nearly 60 percent of organizations report cost and complexity as the top obstacles to Edge ai deployment, and fewer than one-third have fully deployed Edge ai today (STL Partners, 2025).
Why having everything in one place matters
PoC to production
88%
attrition rate
4 of 33 PoCs
reach production
IDC, 2025
Fail to deliver value
80%
of ai projects
do not deliver
promised value
RAND Corporation, 2024
Initiatives scrapped
42%
of companies scrapped
most ai initiatives
in 2025
S&P Global, 2025
Fully deployed Edge ai
<⅓
of organizations
have fully deployed
Edge ai today
STL Partners, 2025
Figure 1. The pilot-to-production gap: four independent research findings on why ai projects fail to reach production.
What ai integration services actually include
The phrase “ai integration” for CTai LABS means the full engineering path from a use case definition to a validated, production-supported system running on Connect Tech hardware. That path has six stages, and skipping any one of them is how projects stall between pilot and production.
1.
Discovery and feasibility
Define the use case, the data, and the production success metric before anything is built. This means agreeing on what ‘working’ means in the field, not just on a developer’s laptop. Most pilots that fail skip this step entirely.
2.
Hardware and platform selection
Match the workload to the right NVIDIA® Jetson™ module, Connect Tech carrier, and thermal solution. With the cost of memory rising, ensuring you have the right hardware matters. Power envelope, camera bus type, ingress rating, and long-term module availability are all criteria. This decision sets the ceiling for every layer above it. Getting it right the first time is the most cost-effective thing a program can do.
3.
Sensor integration and bring-up
Most teams underestimate the integration stage in both time and complexity. CTai LABS draws on Connect Tech’s network of vision partners, built over 40+ years as a department of Connect Tech. Camera, LiDAR, IMU, and CAN integration across MIPI CSI-2, GMSL2/3, and FPD-Link III, with sensor timing validated before the model sees production data.
4.
Model optimization
TensorRT™ conversion and quantization run on the actual target hardware, tuned to its real power mode and thermal envelope. A model optimized on a desktop workstation and then moved to Jetson is not the same as a model optimized on Jetson.
5.
Deployment-ready handoff
A validated platform with documented hardware and software configuration, SIL/HIL/Digital Twin validation records, an optimized model running on the target, and a API layer where the system connects to external infrastructure.
6.
Production support and scale
OTA update architecture and a production support path backed by Connect Tech’s BSP and software teams. The same team that built the system supports it in production.
Data
Success metric
Carrier
Thermal
LiDAR
IMU
CAN
Power tuning
Documented
Deployment-ready
Fleet path
CTI BSP teams
Figure 2. The CTai LABS ai integration engagement: six stages from discovery through production support.
Why the hardware must be in-house
Most ai integration partners source hardware from third-party manufacturers and write software against BSPs they did not author. When a bring-up issue appears at integration, it travels between the software team and the hardware vendor before it gets resolved.
CTai LABS is a department of Connect Tech, which designs and manufactures carrier boards that modules run on. When an integration issue appears, it gets resolved by the team that built the board, not escalated to a third party. That structural position is the core differentiator.
One example is: D.A.V.E. (Data. Analytics. Vision. Execution.), Connect Tech’s in-house autonomous mobile robot, built and operated on Connect Tech Edge hardware by the CTai LABS team. It exists to demonstrate the full integration stack in a real physical system, not a lab rack.
ai Integration across eight verticals
The same integration discipline applies across every industry where real-world Edge ai matters. Explore each industry’s page for its integration challenges, hardware options, and deployment requirements.
| Vertical | What CTai LABS Solves |
|---|---|
| Robotics and Logistics See Robotics and Logistics → | AMRs and humanoid robots that need to perceive, navigate, and act in real time. The integration challenge is not the ai model: it is multi-camera bring-up, LiDAR fusion timing, and vision-language-action model deployment on hardware that moves. One failed sensor timing assumption stops the robot. |
| Industrial Automation See Industrial Automation → | Defect detection and process monitoring running at production-line speed, where missing a flaw has a measurable cost per unit. Multiple high-resolution camera streams, parallel inference pipelines, and on-prem data processing are the requirements. Cloud latency does not fit in the inspection window. |
| Aerospace and Defense See Aerospace and Defense → | Unmanned systems and vehicle-mounted compute where rugged connectors, wide-input power, and field-validated reliability are non-negotiable from the first architecture review. The integration cannot be patched after a field deployment. |
| Smart Cities and Transportation See Smart Cities and Transportation → | Distributed traffic and infrastructure monitoring where data must stay on-prem, the network cannot be trusted, and camera-dense nodes run unattended across wide geographic deployments. The scale is the integration challenge. |
| Healthcare See Healthcare → | Real-time sensor processing for medical imaging and patient monitoring, where deterministic sensor timing and validated software configurations are requirements, not preferences. NVIDIA Holoscan integration for high-frequency data ingestion is one option. |
| Retail See Retail → | Deployed ai for in-store analytics and loss prevention that processes locally, without sending customer data externally. The ai runs on compact, unattended hardware built for low-latency inference and a long operational life. |
| Autonomous Vehicles See Autonomous Vehicles → | Builds the ai for multi-sensor fusion across cameras, LiDAR, radar, and IMUs, powering ADAS and vehicle intelligence. The ai demands high compute density, wide input voltage, and thermal performance in a constrained form factor, with no margin for sensor-pipeline errors. |
| Construction, Agriculture and Mining See Construction, Agriculture and Mining → | Remote and unattended Edge ai where connectivity is intermittent, dust and vibration are constants, and the hardware runs without human intervention for extended periods. The deployment environment defines the hardware selection. |
Sensor fusion
Defect detection
Rugged deployments
On-prem analytics
Holoscan integration
On-prem vision
ADAS · LiDAR
Unattended deployments
Figure 3. CTai LABS ai integration services across eight focus verticals and more.
What the deliverable is
A CTai LABS engagement gets you to production faster, reduces the risk of joining the 80 percent of ai projects that never reach production, and costs less than assembling the same capability from separate hardware, software, sensor, and integration vendors. The output is a working Physical Edge platform, built to go into production and be supported over its operational life. Specifically:
- A validated hardware and software configuration, documented and reproducible.
- An ai model optimized with TensorRT for the target platform, running within the real power and thermal envelope.
- Full sensor integration across cameras (MIPI CSI-2, GMSL2/3, FPD-Link III), LiDAR, IMUs, and CAN.
- SIL/HIL/Digital Twin validation records against the agreed production success metric.
- A RESTful API layer connecting the Edge system to external infrastructure.
- OTA update architecture and a production support path backed by Connect Tech’s well-known BSP and software teams.
For the business, that translates to a shorter time to market, a lower risk of joining the 80 percent of ai projects that do not reach production, and a lower total cost than assembling the same capability from separate hardware, software, sensor, and integration vendors.
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ABOUT THE AUTHOR
Ceri Nelmes
Tech Marketing Leader & Journalist | Building Brands at the Edge of What’s Next |
Connect Tech & CTai Labs
Ceri Nelmes is Head of Marketing at Connect Tech and an experienced technology journalist and digital strategist. She covers the technologies and market shifts shaping embedded computing, Edge AI, Physical ai, robotics, autonomous systems, and the NVIDIA® ecosystem. Working with Connect Tech and CTai LABS subject-matter experts, she turns engineering developments into accurate, useful reporting for developers, technical buyers, business leaders, and the media.
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Resources and Frequently Asked Questions
Related
Sources
Connect Tech. (2025). Carrier board and thermal solutions product documentation.
https://connecttech.comCTai LABS. (2025). Your Physical ai integration partner.
https://ctailabs.aiGartner. (2024, July 29). Gartner predicts 30% of generative AI projects will be abandoned after proof of concept by end of 2025.
https://www.gartner.comGartner. (2025, February 26). Lack of AI-ready data puts AI projects at risk.
https://www.gartner.comCIO. (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.htmlNetguru. (2026). Why enterprise AI projects fail: Root causes and fixes.
https://www.netguru.com/blog/enterprise-ai-projects-fail-root-causesNVIDIA. (2025). Jetson Orin and Jetson Thor product documentation.
https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/RAND Corporation. (2024). The root causes of failure for artificial intelligence projects and how they can succeed.
https://www.rand.orgS&P Global Market Intelligence. (2025). AI adoption and abandonment survey. S&P Global.
STL Partners. (2025). Edge computing market: 2025 as the tipping point.
https://www.edgeir.com/2025-marks-a-shift-data-sovereignty-and-ai-drive-the-next-phase-of-edge-deployment-20251116ai Integration Services FAQ
What do ai integration services include?
For CTai LABS, the full scope: architecture review, hardware and platform selection, BSP bring-up, sensor integration, model optimization with TensorRT, SIL/HIL/Digital Twin validation, a deployment-ready handoff, and ongoing production support. Skip any one stage and the project may stall between pilot and production.
How is this different from hiring a software development firm?
A software development firm writes code. CTai LABS’s ai architects design ai systems against the hardware, not just the software layer. That means owning the solutions, the thermal design, the sensor pipeline, and the BSP that ties them together. CTai LABS is a department of Connect Tech, the company that designs and manufactures Edge hardware. It gives CTai LABS’s ai architects a level of control a software firm simply does not have: they design the ai to the constraints of the physical system, instead of writing code and hoping it fits whatever hardware shows up.
What does CTai LABS need from us to start?
A defined use case, the data available, the environment the system will deploy in, and a rough timeline. The production success metric is the most important input: agreeing on what ‘working’ means in the field is the first thing the discovery stage does.
Can CTai LABS optimize a model we have already built?
Yes. The team takes existing models through TensorRT conversion and INT8/FP16 quantization on the actual target hardware, integrates them with Isaac™ ROS where perception and navigation are involved, and validates them against the production sensor pipeline. The starting point can be a trained model, a dataset, or a use-case definition.
Which industries does CTai LABS serve?
Robotics and logistics, industrial automation, aerospace and defense, smart cities and transportation, healthcare, retail, autonomous vehicles, and construction, agriculture, and mining. Each has a dedicated vertical page with integration specifics.
What is the difference between ai Integration Services and Full-Stack ai Engineering?
ai Integration Services is the commercial service: what CTai LABS delivers, the business case for using it, and how it applies across verticals. Full-Stack ai Engineering is the technical depth behind how CTai LABS does it: the six-layer stack, the hardware specifications, and the engineering decisions that make the integration work. They are companion pages. Integration Services is for the buyer.
How long does an engagement take?
Timeline depends on workload complexity, sensor count, and validation depth. CTai LABS scopes the timeline during the discovery stage. Projects with mature models and well-defined hardware requirements move faster than greenfield builds. Proof of Concept engagements are typically scoped to run in weeks.
What happens after the deployment-ready handoff?
CTai LABS provides production support, OTA update architecture, and a route to fleet deployment backed by Connect Tech’s BSP and software teams. The team that built the system supports it in production and beyond, depending on the engagement.
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