Create. Automate. Restore.
AI design agents, private machine learning and industrial 3D printing. Neural Deep Network connects automotive and product-design experience with practical tools for studios, founders and people who want to create, automate or restore. Start with a real task, build a working prototype, and develop it into a useful system.
Local AI agents on NVIDIA DGX and PCs
Bring AI into your studio without sending every brief, reference or document to an external service. We scope and configure local models on suitable NVIDIA DGX systems, including DGX Spark for compact development, or GPU-equipped PCs and workstations. The right setup depends on model size, document volume, response speed, concurrent users and the tools your team needs.
A local deployment can include a conversational interface, document search, image understanding and agents that prepare drafts or call approved tools. We help choose models and inference software, configure storage and retrieval, connect permitted folders, and establish access rules. We test the system against representative tasks before recommending a wider rollout.
For an automotive studio, an agent might locate an approved design reference and assemble a brief. For a product studio, it might compare requirements or organise supplier documentation. For an architectural practice, it might search project notes, summarise meeting decisions and prepare a review checklist. These are workflows we can scope around your own material and approval process.
Deliverables can include the configured environment, model and hardware recommendations, a working agent, a documented data-flow map, evaluation examples, a handover guide and team onboarding. Hardware procurement, model licences and ongoing support are agreed in the project scope. We also assess existing PCs before recommending new equipment.
Cloud agents on AWS and Google Cloud
When your team needs shared access, remote collaboration or more compute, we build cloud-based agent workflows on AWS and Google Cloud Platform (GCP). We can evaluate Amazon Bedrock and AgentCore or Google Vertex AI and Agent Engine, alongside other appropriate services, based on your operational needs and budget.
A cloud project starts with a small, useful workflow: retrieving internal knowledge, preparing a structured report, organising a design brief, or connecting an approved business tool. We define the agent's boundaries, the actions it may perform and the points where a person must review the result. We then configure identity, storage, secrets, monitoring and cost controls for the agreed environment.
Cloud deployment does not automatically make a system private. We agree where information is processed, which providers and models receive it, how access works and what is retained. Depending on the service and architecture, the scope can include private networking, controlled service accounts, encryption and audit logs. We explain the trade-offs before implementation.
Choose local, cloud or hybrid. A hybrid workflow can keep selected files on your infrastructure while using approved cloud services for other tasks. We assess that data flow explicitly; we do not assume an agent can safely access every studio file. A pilot gives you evidence about usefulness, latency and operating cost before expanding.
AI design agents for creative studios
Design work needs judgment, context and iteration. Our agents support the process from research and brief preparation to concept exploration, review and presentation. We build around the way designers actually work, with people retaining decisions about form, function, feasibility and the final result.
Possible workflows include collecting references into a structured brief, turning meeting notes into design tasks, developing visual directions, preparing alternative concepts, organising feedback and assembling a presentation draft. For teams with approved internal knowledge, retrieval can help an agent answer with relevant project material rather than relying only on general model knowledge.
SketchMonkey supports visual exploration, while 3D DJ sits within our browser-based 3D direction. Our wider app and agent work includes Agent, Shelter, Mind and Dreamscape. Availability and integration are discussed individually: a prototype, an app and a production deployment have different requirements. We demonstrate the relevant tools and agree which are suitable for your project.
We can also scope Hermes or OpenClaw workflows for personal and studio automation. Tool permissions, credentials, approval steps and test tasks are part of the setup. Start with a defined outcome such as organising research or preparing a brief, then add integrations only where they improve the work.
Industrial 3D printing with Prusa
Turn a sketch, CAD file or reference component into a physical prototype. Our Prusa-based workflow combines design review, CAD preparation, PrusaSlicer setup, material selection and print iteration. We work on the geometry and intended application before treating a printed object as a finished solution.
Applications include product mock-ups, equipment housings, covers, brackets, assembly aids, jigs, fixtures and small-batch parts. A prototype can reveal a clearance issue, test an assembly sequence or make a design review more concrete. For an industrial client, we define the dimensional requirements, expected loads, operating environment and acceptance checks at the beginning.
We review wall thickness, tolerances, orientation, supports and assembly. Material choice depends on temperature, moisture, UV exposure, chemicals and mechanical demand, as well as the available printer and process. PLA, PETG or another printable material is selected for the particular task rather than used as a universal answer.
The process can include an initial feasibility review, CAD development or repair, a small fit coupon, a first article, revisions and a documented handover. For repeat parts, we can retain an agreed model revision and print specification so future production starts from a consistent digital reference. Quantities, finish, lead time and inspection are quoted after the requirements are clear.
Discontinued marine and automotive parts
A missing or discontinued component can leave a larger product unusable. We assess whether a replacement can be reverse engineered from an original part, photographs, measurements or the mating assembly. The aim is to recover useful function through a measured design and prototype process.
Bring us the broken part and explain how it fits, what it carries and where it operates. We identify mounting points, interfaces and constraints, build a CAD model, and iterate on fit. Marine environments add salt, moisture and sunlight; vehicle applications may add heat, vibration and chemical exposure. Those conditions shape the material and validation plan.
Our published AutoAnchor cradle project with Malta Yachting shows this approach: a marine replacement-part brief developed through CAD, printed fit checks and documentation. It is a concrete example of design and fabrication working together. The project record explains the actual work rather than claiming long-term environmental testing that has not been completed.
See the AutoAnchor marine case study and project PDF →
Modular panels, product prototypes and interactive objects
We also develop modular wall-panel concepts with Malta-inspired and cyber forms: geometric relief, flowing surfaces and repeating tiles that can be arranged into a larger composition. We review printer size, connections, wall mounting and finish before moving from a visual concept to a physical test.
Recent development includes modular panel exploration and interactive-object prototypes. Spark and Spark Orb work explores the connection between browser-based 3D, printable geometry and electronics. These examples are development work; physical testing and manufacturing readiness are assessed separately. We can use the same approach to scope a housing, display object or custom interactive prototype for your studio.
Design experience behind the technology
Our founder's background spans automotive and industrial design, including contributions to Toyota robotic forklift and Traigo-range work and projects involving Toyota RAV4, Harrier, Highlander and Corolla. That experience informs how we approach proportions, interfaces, materials and real-world constraints. Founder design history is distinct from a new AI or manufacturing contract with those companies.
Today the focus is transformation: connecting design expertise with agents, digital tools and physical prototyping. Older portfolio work remains part of the story, while the latest development and practical use cases lead our services. We review the relevant work with you and explain the role, stage and outcome of each example.
Prusa collaboration and startup support
Our work with Prusa supports the connection between digital design and desktop fabrication. We use Prusa hardware and the PrusaSlicer workflow in our prototyping practice, and share the development process through our project and blog updates.
Neural Deep Network is supported through Google for Startups, AWS for startups and Cloudvisor sponsorship. These programmes support our infrastructure and development; they are not a certification of every service or a promise of credits for every customer.
If you are building a startup, our Cloudvisor referral provides a route to discuss AWS infrastructure and available startup-credit programmes. Eligibility, previous credit awards, current programme terms and provider approval determine the offer. We recommend checking the current terms directly before planning your budget around an award.
Neural Deep Network on the AWS startup showcase →
Discuss AWS credit eligibility with Cloudvisor →
From your brief to a working result
Tell us the problem, the people who will use the result and the constraints that matter. For an agent, bring representative tasks and explain your data requirements. For a printed part, bring photographs, dimensions or a sample and describe its environment. We review feasibility, propose a pilot and agree the scope, deliverables and acceptance checks.
The first stage should produce something you can assess: an agent completing a defined task, a model searching approved knowledge, or a printed prototype you can inspect and fit. We review what works, what needs revision and what is required for the next stage. Training, documentation and ongoing support can be included in the scope.