Advancing Hardware Development & Discoveries

Engineering the Tools for the Next Era of Physical Engineering

DeepLabs builds advanced engineering reasoning models, physical data ingestion pipelines, and software processes designed to accelerate physical hardware development and edge deployment for the benefit of humanity.

Explore Tooling Our Mission
Core Ecosystem

Active Tooling & Core Systems

Intelligent design environments and pipeline processes built to respect physical rules, protect intellectual property, and enable rapid edge execution.

Data Ingestion Tools

Automated pipelines and desktop directory watchers that capture, parse, and structure high-quality metadata from design files (CAD, EDA, simulation logs) to secure clean datasets for engineering logic training.

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Domain Reasoning Models

Developing domain-specific engineering reasoning language models, starting with comprehensive fine-tuning optimized for physical equations, structural constraints, and mechanical logic on the edge.

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Active Focus

Engineering & Research Initiatives

Pioneering standard data pipelines, domain model fine-tuning, and robust systems orchestration for hardware design memory.

Data Pipelines Active

Physical Data Ingestion

Developing robust desktop parsers that read local project directories (EDA schemas, solid geometry) to extract high-quality, structured system parameters without exposing raw files.

Ingestion Metadata Extractor Data Ingestion
Reasoning Models Active

Domain Model Fine-Tuning

Training and fine-tuning engineering reasoning models on spatial mechanics, mathematical constraints, and physics proofs to execute complex physical reasoning tasks.

Fine-Tuning Engineering LLM Edge Inference
Operating Layer Active

ProtoLM Workspace

Integrating local desktop watching workflows, requirements matrices, and rigorous ECR/ECO revision structures into a single workspace for engineering team coordination.

Traceability Change Management Local-First Sync
Blueprints

Technical Blueprints & Directions

Deep dives into the systems architectures, pipeline processes, and training directions guiding our engineering efforts.

Ingesting High-Quality Data for Physical Engineering

Exploring why physical engineering AI requires highly structured local ingestion tooling. How we parser geometric and board parameters directly from desktop folders.

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Fine-Tuning Reasoning LLMs on Physical Constraints

Our starting methodologies for training compact models to understand boundary properties, mechanical rules, and materials specifications on local hardware.

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Local-First Synchronization for Proprietary IP

Why copying entire design models to cloud servers exposes critical company IP. How the local-first metadata synchronization in ProtoLM guarantees design security.

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Operational Scope

Core Operational Pillars

DeepLabs' technical capabilities are organized into core disciplines designed to bridge advanced computing with real-world deployment.

Data & Pipelines

Data Capture & Ingestion

Building ingestion tools to secure high-quality data from desktop-level projects, CAD parameters, and mechanical definitions.

Reasoning Models

Fine-Tuning & Constraints

Developing fine-tuned, domain-specific engineering reasoners optimized to understand spatial, mechanical, and mathematical requirements.

Operating Layers

System Orchestration

Integrating requirements, modular system definitions, and design parameters into unified workspaces like the ProtoLM platform.

Edge Deployment

Edge-First Compilation

Compiling, squeezing, and optimization of language models and pipeline routines to run locally in secure offline industrial edge stations.

Mission Blueprint

Advancing Physical Engineering for Humanity

DeepLabs builds engineering tools, processes, hardware, and software which help advance hardware development, physical engineering, new innovations, and scientific discoveries.

We focus intensely on developing for the edge and streamlined deployment. This ensures that novel research breakthroughs and physical engineering innovations move rapidly from our compilers and pipelines directly into the physical world, where they benefit humanity.

High-Quality Data Ingestion

Building automated extraction processes to capture structural board metadata, Solid CAD variables, and simulation outputs into clean datasets.

Domain Model Fine-Tuning

Developing domain-specific models tailored to solve advanced engineering, physics-based, and geometry-constrained reasoning tasks.

Edge Deployment Tools

Compiling logic and inference engines specifically to execute on-device and locally, protecting IP and reducing hardware round-trip latencies.

Technical Roadmap

Our Development Phases

A multi-phase timeline for mapping advanced intelligence to physical engineering constraints and edge execution environments.

Phase 1

Workspace & Data Foundations

Q2 - Q3 2026
  • ProtoLM Workspace: Active core development of the system-centered engineering workspace, enabling structural requirements traceability, and central ECR/ECO change milestones.
  • Agentic Collaboration: Establishing robust orchestration layers within ProtoLM to facilitate secure, turn-bounded multi-agent execution, deployment, and human-in-the-loop verification.
  • High-Quality Data Ingestion: Designing desktop watchers to parse schematic boards (ECAD) and Solid CAD parts to gather structured engineering datasets.
Phase 2

Domain Logic & Workflows

Q4 2026 - Q1 2027
  • Domain-Specific LLMs: Fine-tuning compact 7B/8B language reasoners exclusively on mathematical proofs, physical constraints, and materials specifications.
  • Agentic Workflow Loops: Constructing closed-loop agentic reasoning networks designed for automated design compilation, schematic checking, and hardware verification scripts.
  • Ingestion Pipeline Scaling: Deploying structured data ingestion formats for thermodynamic CAE boundaries and practical grounding constraints.
Phase 3

Edge Hardware Co-Design

Q2 2027 and Beyond
  • Edge Deployment Hardware: Constructing optimized physical computing modules tailored specifically to execute dense engineering models locally and securely.
  • Deep HW/SW Co-Development: Tight hardware-software compilation integration to run engineering reasoners in offline industrial sites.
  • Physical Grounding & Testing: Accelerating deployment loops by testing generated designs on physical, hardware-in-the-loop automated testing stations.
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Deploy the Future of Engineering

Interested in integrating ProtoLM workspaces, local-first data ingestion pipelines, or fine-tuned reasoning models into your hardware development workflows?

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