Give AI agents all your data.

Plug Claude, ChatGPT + Gemini into every system you run - SaaS, IT, OT, IoT, files - anything. Get AI agents that can finally see all your business.

Custom AI built on your very own data fabric.

Predictive ML, Gen AI, Agentic AI, MCP, conversational analytics + AI-led execution.

Built into every layer of your operations. Governed end-to-end. Live in weeks.

DIYBuild it yourselfDONE-FOR-YOU We build itHYBRIDBuild it together

95% OF AI PROJECTS NEVER SHIP · AUSTRALIA-BASED TEAM

Trusted by 240+ teams across Australia + globally

AI POCS NEVER SHIP

Industry average (Gartner).

RAYVEN AI IN PRODUCTION

Via The Rayven AI Pilot.

AI CAPABILITIES

Native to the fabric. Not bolted on.

100%

YOUR DATA, YOUR CONTROL

Sovereignty + governance built in.

AI is only as good as the data + architecture underneath it.

Most AI projects fail because the data layer is not ready: generic LLMs hallucinate, predictive models score against stale exports + agents have no context.

The Rayven Platform can create an AI Data Fabric that solves the prerequisite - unified, real-time, multi-source data - then uses that fabric to give you the custom AI capabilities you need - including agentic AI - embedded across every layer of your business.

Just 5 of the custom AI solutions we can build for you on Rayvens.

Not vague capabilities. Specific products you can take to a client next week - built by Rayven, delivered under your brand.

[03 · AI ASSISTANT ACCESS Rayven MCP \Connect Claude, ChatGPT + Gemini directly to your live data via the Model Context Protocol. Your AI assistants get governed, real-time context - not guesses based on stale exports. \

  • →Claude, ChatGPT + Gemini connected to your live data.
  • →Governed access - you define what each assistant can see.
  • →Full audit log of every query made. \
    Find out more](/content/solutions/custom-software/rayven-mcp/index.html)

[02 · AUTONOMOUS AI Agentic AI \AI agents that don’t just surface insights - they act. Goal-seeking agents that monitor conditions, make decisions + execute across your systems without manual intervention. \

  • →Write-back to OT systems, ERP + operational software.
  • →Custom decision logic, thresholds + escalation rules.
  • →Human-in-the-loop controls with a full audit trail. \
    Find out more](/content/solutions/custom-ai-solutions/agentic-ai/index.html)

[01 · FOUNDATION AI Data Fabric \The prerequisite for every AI project that actually ships. Connect every system and data source into a unified, real-time layer - structured, governed and ready for AI to use. \

  • →600+ connectors spanning IT, OT + IoT systems.
  • →Real-time processing - no batch delays, no stale data.
  • →AI-ready and deployed in 2-6 weeks. \
    Find out more](/content/solutions/custom-ai-solutions/ai-data-fabric/index.html)

Predictive AI + ML

Train Python-based machine learning models on your data in real-time to forecast outcomes, detect anomalies + prevent failures before they cost you. Deliver real-time insights.

  • →Custom models trained on your data, not generic benchmarks.
  • →Real-time scoring against live feeds - sub-second detection.
  • →Continuously retrains as conditions change.

Conversational Analytics

Ask operational questions in plain language via custom UIs or embedded in existing dashboards. Get accurate, real-time answers from your actual data - not a generic LLM’s.

  • →Natural language queries across all your connected data.
  • →Answers traceable to source data - no hallucination risk.
  • →Built for operations teams, not data scientists.

AI in every layer. Not bolted-on.

Most AI platforms sit beside your operational software. Rayven puts AI inside the platform - across every one of the five layers.

11 AI capabilities. One platform.

01 · AI AGENTS

Custom AI agents

Goal-seeking agents that read your data, run workflows + take real actions - governed by your rules.

02 · PREDICTIVE AI + ML

Predictive AI + ML

Trained models deployed into live workflows - scoring, forecasting, classifying against real-time data.

03 · CONVERSATIONAL

Ask-anything analytics

NL interface over your data - ask questions in plain language; get charts, summaries + actions.

04 · REAL-TIME TRAINING

Continuous + real-time training

Online learning, scheduled retraining + live deployment. Models stay current as operations change.

05 · AI-LED EXECUTION

AI-led automation

AI decisions wired directly into workflows - triggering actions, calling APIs, routing approvals.

06 · MULTIMODAL

Document + media AI

Extract from PDFs, forms, contracts, CSVs, video, images + audio. Generate reports + summaries.

07 · ANOMALY

Anomaly + risk detection

Models that watch your operations + flag the unusual - auto-routed to the right person.

08 · FORECASTING

Forecasting + optimisation

Demand, capacity, energy, scheduling, pricing - AI-driven forecasting embedded in the workflow.

09 · VISION + EDGE

Vision + edge AI

Vision-based defect detection + edge-inference outputs - ingested into the fabric as another data source.

10 · GEN AI SUMMARIES

Generative summaries

AI-written reports, updates + summaries generated automatically - on schedule, on demand, or triggered by events.

11 · RAYVEN MCP

AI assistant access

Give Claude, ChatGPT + Gemini governed, real-time access to your data via the Model Context Protocol.

Why AI FAILS

Shadow AI is already happening

Your teams are pasting customer data into ChatGPT. Marketing is on Copilot. Engineering uses Cursor. It is the wild west - ungoverned, untracked, undefended.

AI without a data foundation

You cannot put AI on fragmented data and expect production results. Without a unified, real-time data fabric underneath, the model is guessing.

Pilots never reach production

95% of AI PoCs die at 'interesting demo'. Beautiful Jupyter notebooks, zero operational impact, executive deck slides.

Generic LLMs don’t know your business

ChatGPT + Copilot are great until they hallucinate about your products, processes + customers. Trust collapses and the rollout stalls.

AI lives in a separate platform

Your AI sits in one tool. Your operations live in another. Insights never become actions. Predictions don’t trigger anything.

No governance, no audit

You cannot see who asked, what model answered, where data went, or why. Risk + compliance teams say no. Project stops.