Shrubnet Labs
Research & development for complex systems, agentic automation, and hard technical problems.
We explore problems that don't fit neatly into products, roadmaps, or sprint cycles.
What We Are
Shrubnet Labs is an independent R&D organization focused on designing, testing, and evolving complex technical systems.
We work in the space between infrastructure, automation, and intelligence — where problems are asynchronous, multi-agent, and resistant to simple solutions.
Shrubnet Labs is not a product company or an app repository. It is a place for experimentation, architecture, and long-term thinking.
Areas of Exploration
Agentic Systems & Automation
Designing systems where autonomous components collaborate, adapt, and self-correct.
Asynchronous & Distributed Architectures
Solving problems that span time, machines, networks, and uncertain states.
Applied AI Infrastructure
Practical foundations for AI-enabled systems, not demos or wrappers.
Tooling for Non-Linear Workflows
Supporting workflows that don't move in straight lines or predictable sequences.
Research-Driven Prototyping
Building to understand, not just to ship.
Data Architecture & ETL
Data lakes, pipelines, and orchestration at scale. Queue systems, time-series, and schema mapping.
Legacy Systems Integration
Connecting legacy platforms with modern AI. Cross-system automation where APIs do not exist.
Multi-Modal AI Systems
RAG, Graph RAG, embeddings, and agents. Vision, audio, and generative pipelines.
Technical Capabilities
Deep expertise across the full stack.
From legacy mainframes to frontier AI models. Decades of hands-on experience building systems that move data, process intelligence, and connect the old world to the new.
Current Focus
Integrating AI with legacy platforms that require special tooling aligned with our unique stack. Building multi-faceted systems with physical devices, cloud integration, extensive AI automation, and ML algorithms to reduce LLM token consumption.
- Queue-based systems: RabbitMQ, Kafka, SQS
- Data lake architecture: Databricks, Apache Spark, Airbyte
- ETL task orchestration and pipeline design
- High-velocity time-series data management
- SQL & NoSQL: Postgres, MongoDB, ACID compliance
- Schema-driven GraphQL APIs with Hasura
- Data Concept Code Mapping: OMOP, SnoMed, Common Data Model
- Data Feeds As A Service: real-time and adaptive refresh
- Financial Services APIs: Plaid, SILA, Lithic, Stripe
- Payment rails: custom banking integrations
- Supabase: on-premise Docker, cloud deployments
- Edge Functions, S3-compatible Storage, Auth
- Database and Vector Database solutions
- On-premise AI, RAG and Graph RAG
- Custom AI Agents: LangChain, Tool Calling, Function Calling
- Embeddings and Vector Stores: PGVector, Supabase
- LLMs: Open Source, Anthropic, OpenAI, MiniMax, Kimi, Manus
- Image & Video generation: ComfyUI, Stable Diffusion, Flux
- Qwen, WAN, VEO, SORA pipelines
- Pre-LLM ML: NLP, OCR, YOLO, Object Detection, Depth Perception
- Audio: Text-to-Speech, Speech-to-Text
- Autoencoder training, Sentiment Analysis, Log Analysis
- Industrial-grade data extraction at scale
- Playwright, Selenium with Chromium and Firefox drivers
- AI-assisted scraping for complex data structures
- Structured and unstructured data extraction
- Anti-bot technology and cybersecurity
- Detection bypassing: HUMAN, The Dread Clock
- Legacy platforms: AIX, AS-400, XENIX, UNIX
- Extensive Linux and Windows experience
- DOS, NT 4.0, Domain Controllers, Exchange
- SQL Servers, File and Application Servers
- Routers, Switches, Firewalls
- Datacenter and telco to the DMARC
- SMB and SaaS operational backgrounds
- DevOps, MLOps, high-compliance environments
- Managing distributed development teams
- Motion Analysis and PHI management
- De-identification protocols
- AI-driven medical integration
- EMR/EHR platforms, LIMS, PMS, Registries
- Agentic AI working with clinical systems
How We Work
Shrubnet Labs operates deliberately and quietly.
We favor:
- Thoughtful system design over rapid exposure
- Internal rigor over external validation
- Understanding failure modes before scaling success
Some work remains exploratory.
Some work never becomes a product.
All work is treated seriously.
Supporting Builders
Alongside research, Shrubnet Labs provides mentorship and guidance to founders working on technically complex ideas.
This includes:
- Architectural review and system thinking
- Early-stage technical strategy
- Navigating hard trade-offs in automation and AI design
- Helping founders reason through complexity before it becomes technical debt
Mentorship is selective and relationship-driven, not programmatic.
Origins
Shrubnet began as a practical response to a recurring problem: modern systems are increasingly asynchronous, automated, and difficult to reason about — yet most tooling and advice assumes linearity.
Shrubnet Labs was formed to explore these gaps through research, experimentation, and hands-on system design.
The name reflects the original inspiration: resilient, distributed growth — small components working together to support complex outcomes.
Shrubnet collaborates selectively.
Have a complex project in mind? Submit your requirements and we'll get back to you within 2-3 business days.
Submit Your Project
Fill out our project intake form with scope, budget, and requirements.
We Review & Respond
Our team evaluates fit and responds within 2-3 business days.
Scope & Proposal
If it's a fit, we'll schedule a call and provide a detailed proposal.