For regulated and IP-sensitive teams
If your data can't go to a public AI API, the AI has to come to your data. We build custom language and vision models, then deploy them air-gapped on your servers, inside your private cloud, or on edge devices. You own the models, and your data stays where it is.
Deployment options
The right option depends on your regulations, latency needs and who will operate the system. Many teams mix them, for example training in a private cloud and running inference on-premises or at the edge.
Models run on servers inside your facility with no internet connection. Nothing is sent to a third-party API, and inference keeps working when the outside network is unavailable.
Good fit: Regulated data, secure facilities, hospitals
We deploy into your own AWS, GCP or Azure account. Your cloud team keeps control of networking, identity and keys; the model endpoints are never exposed publicly.
Good fit: Teams already standardized on one cloud
A dedicated, isolated environment that we operate for you, including monitoring, drift detection and retraining through our managed services team.
Good fit: Teams without in-house MLOps capacity
Computer vision models optimized with TensorRT for NVIDIA Jetson Orin and Xavier NX, built for offline-first operation, with secure over-the-air model updates.
Good fit: Cameras, factory lines, remote sites
Weighing the trade-offs? Read our guides on on-premises vs cloud computer vision deployment and edge AI vs cloud AI for computer vision.
What you keep
We build custom models that you own, not a seat license to our platform. No recurring per-seat SaaS fees, and competitors never get access to your workflow.
When a model is deployed locally, it has no reliance on third-party services, so your data never leaves your own infrastructure.
We sign non-disclosure agreements with all of our clients, and we can do it before the scoping call if you need it.
Your documents, images and logs are used only for your project. We don't use them to train anything else unless you explicitly agree.
Your security team will have questions. Our security, data handling and IP page covers NDAs, IP ownership, code and model handover, and the subprocessors this website uses.
Book a private AI assessmentProof
Case study · Atacana
The constraint. Atacana needed a chatbot that could answer complex questions over thousands of proprietary medical news reports and drug discovery updates added every day. Cloud-based LLMs were not an option because of confidentiality requirements, so the system had to run completely offline on local servers.
What we built. A retrieval-augmented generation (RAG) system on a Mixtral 8x7B model with 8-bit quantization, which reduced the memory footprint and processing time. LangChain orchestrates retrieval from a vector database that ingests the new reports daily.
The result. Cited answers in 5 to 7 seconds on local hardware, with zero data exposure to the public internet.
“[Because of AxcelerateAI we] experienced favourable results in both precision and recall metrics, along with improved response times of the [LLM-based] system.”
Edge deployment
How an engagement works
We review your data sources, security constraints and the workflow you want to automate, then agree on target metrics and what “good enough” means.
We design the ingestion flow, model serving and network boundaries for your chosen environment: air-gapped servers, your VPC, a managed private cloud or edge devices.
We pick and fine-tune open models that fit your hardware, quantize where needed, and connect them to your document stores, cameras or internal systems.
We measure the system against your own benchmark data and test the private endpoints before anything reaches production.
We deploy into your environment and hand over the code, model artifacts and runbooks your team needs to operate it.
After launch, we can monitor performance metrics and logs for drift and retrain as your data changes, without raw data leaving your environment.
FAQ
In the environment you choose. For air-gapped and on-premises deployments, the models run on your servers and your data never leaves them. For private VPC deployments, everything stays inside your own cloud account and region. When we provide ongoing monitoring, we only look at performance metrics and system logs; raw data stays in your environment.
We choose the model per use case, based on accuracy, license terms and the hardware you have. PharmaBrain, for example, runs a Mixtral 8x7B model with 8-bit quantization entirely on local servers. We also work with other open-weight model families such as Llama, Mistral, Qwen and DeepSeek, and with custom computer vision models (for example YOLO-based detectors) for image and video workloads.
It depends on the model size, the number of users or video streams, and your latency target. Quantization lowers memory needs: 8-bit quantization is what let PharmaBrain answer in 5 to 7 seconds on local hardware. For edge vision, we typically deploy on NVIDIA Jetson Orin modules and can also optimize for Jetson Xavier NX. We size the hardware with you during the assessment, before you buy anything.
Yes. Our managed services cover monitoring, drift detection, retraining and scaling. Edge devices receive new models through secure over-the-air update pipelines. For air-gapped sites, we agree on an update path that follows your change-control and network rules.
You do. We build custom models that you own outright, so there are no per-seat licenses and no lock-in to a hosted platform. We also sign an NDA with every client. See our security, data handling and IP page for details.
Book a strategy session
Tell us what you want to run privately, where your data lives and any regulations you work under. An engineer will reply within one business day, under NDA if you need it, and recommend a deployment option and hardware approach.
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