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AI Developers & Systems Integrators

Obsidian Research Group

We build AI native applications for the next era of work.

Check out our latest applications and learn about our services below.

Trusted partners & technologies

OpenAI
Anthropic
Groq
xAI
Dell
Google Cloud
AWS
What We Do

Three pillars of AI excellence

We operate through three distinct pillars, each focused on a critical stage of making AI practical, scalable, and valuable.

01

Obsidian Arrow

AI Data Structuring

The foundation layer that prepares enterprise data for AI. We structure, encode, and optimise complex datasets so they are ready for analytics, machine learning, and AI applications.

  • Data structuring and feature engineering for AI readiness
  • Spatial, temporal, and vector encoding for AI workflows
  • Scalable data pipelines supporting analytics and inference
02

Obsidian Development

AI Software & Systems

Custom AI software and systems built for real-world deployment. We design, engineer, and integrate AI solutions tailored to enterprise, infrastructure, and complex operational environments.

  • Bespoke AI applications and decision-support tools
  • AI-enabled platforms, dashboards, and workflows
  • Secure, scalable systems from prototype to production
03

Obsidian Research

Advanced Analytics & Intelligence

Advanced analytics and multi-domain data science delivering insight, foresight, and strategic intelligence across spatial, economic, and behavioural systems.

  • Advanced data science across multiple domains
  • Geospatial intelligence and custom analytics
  • Scenario modelling, forecasting, and machine learing
DEVELOPER EXPERIENCE

Your data, wired into AI

We transform enterprise data into AI-ready knowledge. We design and deploy vector stores, RAG pipelines, and MCP servers that connect your data directly to LLMs, agents, and applications.

From raw data to production-grade AI systems — securely and at scale.

Supports:
Structured dataGeospatial dataDocumentsAPIsStreaming data
Build with your data
rag_agent.py
1# rag_agent.py
2from obsidian import VectorStore, RAGAgent, MCPServer
3
4# Build a vector store from client data
5vector_store = VectorStore.from_sources(
6 sources=["data_lake/", "documents/", "geospatial/"],
7 embedding_model="obsidian-embed-v1"
8)
9
10# Expose data via an MCP server
11mcp = MCPServer(
12 vector_store=vector_store,
13 permissions="read_only"
14)
15
16# Create an AI agent trained on your data
17agent = RAGAgent(
18 llm="gpt-4.1",
19 knowledge_source=mcp,
20 tools=["search", "analysis", "reasoning"]
21)
22
23# Embed the agent directly into your application
24response = agent.query(
25 "Summarise key spatial and behavioural patterns for this site"
26)
Our Products

Tools we're building

Beyond client work, we invest in building our own AI-powered products that push the boundaries of what's possible.

Testimonials

Industry leaders trust Obsidian

Obsidian's AI infrastructure allowed us to deploy models 10x faster than our previous solution. Their team truly understands enterprise scale.

Head of Engineering

Fortune 500 Tech Company

The custom AI agents they built transformed how we process data. What took days now happens in real-time.

CTO

Leading FinTech Startup

Working with Obsidian felt like having an elite AI research team in-house. They delivered exactly what we needed.

VP of Product

Global Analytics Firm

Get Started

Ready to build something?

Whether you need custom AI solutions, data infrastructure, or strategic AI consulting—we're here to help.

Schedule

Location

Melbourne • Los Angeles • Berlin

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