Capabilities & technology

What we build with, and why it matters to you

The short version: we build agentic systems on mature, observable, open tooling - and we connect them to the software your team already opens every morning.

Book a free 30-minute call 1800 557 179

Why this page exists

Most of our clients do not care what a vector store is, and they should not have to. This page is here for the ones who do - the technical buyer, the IT manager doing due diligence, the developer who will inherit what we build.

The short answer to the question behind it: we build on tooling that is mature, widely adopted, observable and open enough that you are not locked into us. If you replace us in two years, what we built should still be maintainable by whoever comes next.

Capabilities

Agentic frameworks

Systems where an AI holds tools and permissions and completes a task, rather than only producing text. This is the difference between software that answers and software that acts.

Multi-agent systems

Several specialised agents cooperating under a supervisor - one retrieves, one drafts, one checks, one executes. More reliable than a single agent asked to do everything, and far easier to debug when it goes wrong.

Intelligent automation

Deterministic workflow automation with AI in the places that genuinely need judgement, and plain code everywhere else. Using a model where an if-statement would do is expensive and less reliable.

LLM function calling

How an agent invokes your real systems - fetch the order, create the invoice, book the slot - with typed, validated arguments rather than guessed text. The safety boundary between a language model and your database.

Retrieval-augmented generation (RAG)

Grounding answers in your own documents and records so the agent cites your reality rather than a training-set memory. The single most effective defence against confident, plausible, wrong answers.

Model Context Protocol (MCP)

An open standard for exposing tools and data to AI systems. Building to it means the integrations we write are portable across models and clients rather than welded to one vendor.

Planning systems

Agents that decompose a goal into steps, execute them, and recover when a step fails - the machinery that turns a request into completed work instead of a single response.

Prompt engineering

The unglamorous discipline of making behaviour repeatable: constrained instructions, evaluated against real cases, versioned like code. It is testing, not wordsmithing.

AI copilots

Assistants embedded where your team already works, helping rather than replacing - drafting the reply, finding the record, summarising the thread, with a human still deciding.

UX and UI for agentic AI

Interfaces that make it obvious what the agent is doing, what it is about to do, and how to stop it. Trust in an AI product is a design problem at least as much as a model problem.

Go-to-market for agentic AI

For businesses building AI into their own product: positioning, pricing and packaging something whose value is measured in work removed rather than seats filled.

Ethics and transparency

Disclosure that a customer is talking to an AI, logs of what it did, a route to a human, and honesty about what the system cannot do. Aligned with OAIC guidance on automated decisions, and simply the right way to build.

The stack

Grouped by what each part does. Not everything appears in every project - the right architecture for a single FAQ agent is not the right architecture for a multi-agent finance workflow, and we do not add components for the sake of the list.

Agent frameworks and orchestration

Where agent behaviour is defined, sequenced and supervised.

LangChain
LangGraph
CrewAI
AutoGen
MetaGPT
AutoGPT
OpenClaw
Emergent

Models and interfaces

The reasoning layer, and the protocol standards that keep integrations portable.

ChatGPT
MCP
FastMCP
GitHub Copilot

Data, memory and retrieval

Where an agent's grounding knowledge and your operational records live.

Pinecone
PostgreSQL
MongoDB

Observability and evaluation

How we tell whether an agent is behaving, before your customers tell us it is not.

LangSmith
Phoenix
Jupyter
Google Colab

Automation and integration

The pipes between systems, and the containers they run in.

n8n
Docker
GitHub

Design and build

Where interfaces are designed and software is written.

Figma
Miro
Visual Studio Code
Lovable

Business tools we connect to

The software your team already opens every morning. These are integration targets, not things we sell you.

Slack
Gmail
Google Docs
Asana

Common questions

Are we locked into these tools?

No, and that is deliberate. We favour open frameworks and open standards such as MCP precisely so the work is portable. You own the code and the accounts, and another developer can pick it up.

Which model do you use?

Whichever suits the task, the budget and the data-handling constraints - that decision changes faster than any website can keep up with, so we make it per project rather than pledging allegiance to a vendor. Where data residency matters, that constrains the choice and we will say so up front.

Can you work with our existing development team?

Yes. We are often brought in for the agentic and integration layer alongside an in-house team that owns the product. We document what we build and hand it over properly.

Do you connect to Australian industry-specific software?

Frequently. Anything with a documented API is straightforward; older systems sometimes need a scheduled export or a direct database connection. If there is no safe route in, we will tell you rather than build something fragile.

Want to know if your stack fits?

Bring the list of what you run - CRM, accounting, project tool, database - and we will tell you on a free call what could be connected and what would be a fight.

Pick a time that suits you 1800 557 179

Free call, Australia-wide. No pitch, no pressure.