Applied AI / service hub
Put AI to work inside a real business system.
Consulting, agents and custom AI implementation with an accountable operating model.
We identify a valuable workflow, set its operating boundary, and build the knowledge, interfaces, tools, review states and evidence needed to run it safely.
- Delivery
- Consulting + implementation
- Scope
- Australia-wide
- Control
- Human-owned
One operating system
Useful work, visible ownership- 01Workflow
- 02Knowledge
- 03Agents
- 04Tools
- 05Review
- 06Evidence
The operating problem
Most AI projects begin with a model or demo. The commercial work begins when the system must know its limits, use real tools and return an inspectable result.
System mechanism
The Applied AI operating model
A governed path from business input to evidence, with permissions and human review around the model.
The Applied AI operating model: a business workflow from request and approved context through controlled action, human review and a measurable result.
- 01Business triggerBusiness input
A real request, event or decision enters through a controlled interface.
- 02Business contextApproved context
The system retrieves only the knowledge and state the task is allowed to use.
- 03Decision supportBounded reasoning
Rules, model behaviour and workflow state determine the next permitted step.
- 04Useful actionBusiness tools
Typed tools read or prepare changes in the systems where work already happens.
- 05Human gateHuman checkpoint
Uncertain or consequential work returns to an accountable person.
- 06Business valueMeasurable business result
Useful work reaches the team with ownership, evidence and a clear next action.
Choose the operating problem
Capabilities underneath. Business scenarios up front.
Start with a real workflow when the business problem is already clear. Use a capability page when the technical operating model still needs to be chosen.
We convert repeatable enterprise work into bounded tools that Codex or Claude Code can operate with clear permissions, human approval and evidence.
LangChain + LangGraph agentsBuild stateful agents that can pause, recover and ask for judgment.We build agent systems around explicit state, typed tools, durable checkpoints and review paths rather than hiding a business workflow inside one model call.
Mastra agent developmentBuild AI agents inside a TypeScript operating model.We use Mastra when a TypeScript-native agent and workflow layer reduces integration friction without weakening state, permission or review boundaries.
Retrieval Engine + RAGGive every agent a trustworthy way to find the information it is allowed to use.We build retrieval engines that combine structured search, semantic retrieval, metadata, permissions and evidence checks so agents can use company information without treating every document as equally reliable.
AI sales chatbotTurn a chatbot into a guided sales journey.We build sales chatbots that use approved commercial knowledge, ask useful questions, recommend the next action and hand a structured opportunity to sales.
Customer service AIResolve repeatable service questions without hiding the handoff.We build customer-service AI that answers from approved sources, gathers useful context, routes exceptions and keeps customer-impacting actions accountable.
Knowledge work
Review-ready tender pack · Earlier bid decisions · Reusable evidence
Business reporting automationProduce management reports without rebuilding the evidence every month.Approved management report · Shorter reporting cycle · Visible data exceptions
Document processing automationTurn incoming documents into checked, structured business records.Structured records · Visible exceptions · Reduced manual entry
Internal knowledge assistantGive teams evidence-backed answers from the information they are allowed to use.Evidence-backed answer · Faster knowledge access · Visible content gaps
Sales
Qualified opportunity · Faster sales response · Better nurture routing
Product recommendation and quote assistantHelp buyers find a suitable option and prepare the right commercial next step.Relevant recommendation · Quote-ready request · Reduced sales discovery
Customer service
Correctly routed case · Faster first response · More useful escalation
Order, returns and warranty assistantResolve repeatable order and policy questions without hiding exceptions.Resolved repeatable request · Accountable escalation · Shorter handling time
Fatbunny responsibility
Fatbunny owns the system boundary: problem definition, workflow design, retrieval and state, tool contracts, interface behaviour, human handoff and measurable release criteria.
Capabilities
- 01
AI opportunity and workflow diagnosis
- 02
Knowledge, retrieval and data-boundary design
- 03
Agent and automation implementation
- 04
Interfaces, integrations and human review
- 05
Evaluation, observability and controlled release
Delivery decisions
- 01
Choose the workflow before choosing the model
- 02
Separate read access, draft actions and consequential actions
- 03
Design failure and escalation before adding autonomy
- 04
Measure task outcomes rather than model confidence
Expected outputs
- 01
An agreed workflow and operating boundary
- 02
A working system connected to approved knowledge and tools
- 03
Evaluation evidence, handover controls and a measured next backlog
Honest limits
- !
A model is not a source of business authority; permissions and policy remain explicit.
- !
Sensitive or consequential actions require scoped tools, review states and a tested fallback.
- !
A convincing demonstration is not treated as production evidence.
Start with the operating constraint
AI is useful when a specific part of the business is slow, inconsistent, difficult to search or expensive to coordinate. It is not useful simply because a new model exists.
The first decision is therefore commercial: which outcome matters, who owns it, what evidence is available and what must remain a human decision.
Build a capability, not a prompt
A production capability has inputs, state, permissions, tools, evidence, failure behaviour and a place for human judgment. Prompt design is one small part of that system.
Fatbunny connects those layers as one implementation so that the interface does not promise behaviour the underlying workflow cannot safely deliver.
Choose the delivery path
Codex automation supports repository-aware engineering work. LangChain and LangGraph support stateful tool-using agents. Mastra provides a TypeScript-native agent and workflow model. Sales and service AI require customer-journey, handoff and measurement design around the agent itself.
Direct answers
Common questions
Do we need to know which AI framework we want?
No. The workflow, data boundary and operating requirements should decide whether a framework is useful.
Can you work with our existing systems?
Yes, when those systems expose safe integration points and the required access can be scoped.
Can AI operate without human approval?
Only for bounded, reversible work where permissions, failure handling and measurement justify it.