Redditech Labs is a Sydney-based research and product lab for AI agents, infrastructure, identity, and real-time software. We read the research, build the thing, measure what happens, and publish what we learn.
Built on experience from: Microsoft·Auth0/Okta·Antler·UQ Ventures Innovate
The work shows up as shipped products, open-source tools, benchmark results, operating playbooks, and technical writing. The lab is designed to produce artifacts, not just recommendations.
Shipped systems
10+
Products, prototypes, courses, evals, and infrastructure projects moved from idea to working artifact.
Operating model
Agentic
Research, build, QA, publishing, memory, and ops run through the lab's own agent workflow.
Core domains
AI · Identity · Infra
Practical experience across real-time AI, autonomous agents, OAuth/OIDC, blockchain, and enterprise architecture.
Redditech Labs uses an integrated agent operating stack to run research, development, QA, publishing, and operations. Human judgement stays in the loop, but the day-to-day machine is agentic.
Specialist workers for research, build, QA, writing, operations, and synthesis.
Project status, daily logs, decisions, and searchable context survive across sessions.
Model/task fit, routing decisions, quality gates, and regression checks inform what ships.
Telemetry, spend tracking, health checks, watchdogs, and postmortems keep the machine honest.
External actions, publishing, credentials, spend, and sensitive claims stay approval-bound.
Four lanes. All shipping.
Early-stage. Real users. No promises. We get things in front of people as fast as possible and iterate based on what the data shows. Some products find a market. Some don't survive contact with users. That's the point.
Tools we built because we needed them. They work well enough that other builders can use them too. That's usually how it starts: scratch your own itch, then realise the itch is universal.
Skills, libraries, tools, given away. We build community by giving builders something real to work with, not something to aspire to.
We read papers, form hypotheses, run experiments, and publish findings. The research informs the products. The products inform the research. Neither works without the other.
We retrofitted SPDD onto three real OpenClaw-built MVPs to see whether lightweight prompt contracts improve reviewability
We studied Portkey's open-source LLM gateway, implemented the patterns natively in OpenClaw, and never ran a single line of their code. Here's why that's the point.
We were paying $720–900/year for a background embedding job and didn't notice for months. Migrated to nomic-embed-text via Ollama in an afternoon. Cost: $0/month. Quality: identical.
Projects are tracked as evidence: what was hard, what was tested, what shipped, and what the lab learned.
Problem: AI agents are impressive in demos but brittle in real operations.
Shipped: A persistent operating stack with agents, memory, routing, QA, observability, and human gates.
Learning: Agents become useful when they inherit process, memory, and accountability — not just prompts.
Problem: Cloud models are powerful but expensive; local models are cheap but uneven.
Shipped: A routing and evaluation layer that measures task fit, quality, and cost before promoting models.
Learning: Model choice should be empirical and task-specific, not brand-specific.
Problem: Generative AI demos rarely become real family-facing products.
Shipped: A multimodal bedtime-story pipeline spanning story generation, narration, and illustrations.
Learning: The product challenge is orchestration, safety, consistency, and taste — not just generation.
An open source AI agent course platform, live on Fly.io and built to be forked, extended, and taught from.
Open SourceMulti-modal AI bedtime stories for kids, combining ElevenLabs voices and Google Imagen 3 illustrations into a working parent-facing production pipeline.
ProductA prototype 26-agent AI research platform exploring multi-agent orchestration, blockchain protocol research, knowledge graphs, and contributor economics.
ResearchIntelligent routing between local and cloud AI models, with an LLM-as-judge evaluation layer that picks the best output for each request.
InfrastructureA published benchmark comparing free local TTS against a paid cloud baseline, with methodology and results available for inspection.
ResearchInteractive developer education for OAuth2, OIDC, and SAML, built to replace the spec-reading that kills learning and the tutorials that skip the hard parts.
Product