AI Implementation Expert
Budget: $25 – $50 AUD
AI Implementation Expert — Business Knowledge Capture & AI Agent Deployment
We are looking for an experienced AI implementation specialist to help modernise a 40-year-old established business. This is a multi-phase project and we need someone who has done this before — not someone who is learning on the job.
THE PROJECT
This business has decades of operational knowledge sitting in the heads of key employees, email threads, legacy non-cloud systems and Xero. Our goal is to extract, organise and operationalise that knowledge — and then layer AI on top of it.
Think of it in three phases:
Phase 1 — Knowledge Audit
Audit the business to understand where information currently lives. Map all systems, processes, tools and key person dependencies. Identify gaps and risks.
Phase 2 — Operational Knowledge Layer
Capture all extracted knowledge and organise it into a structured, searchable system (Notion or equivalent). This becomes the foundation — SOPs, decision frameworks, process documentation, supplier relationships — everything the business needs to run independently of any one person.
Phase 3 — AI Agent Implementation
Once the knowledge layer is in place, implement AI agents that can work across it. This could include agents for customer communication, internal Q&A, operations, reporting and more.
IMPORTANT — MODEL AGNOSTIC APPROACH
We do not want to be locked into any single AI model or provider. The architecture must be designed so that the underlying model (Claude, OpenAI, Gemini etc) can be swapped out without rebuilding from scratch. Please only apply if you have experience building model-agnostic AI systems.
WHAT WE ARE LOOKING FOR
- Proven experience auditing business operations for AI readiness
- Experience building knowledge management systems in Notion or similar (Confluence, Coda)
- Hands-on experience deploying AI agents in a business context — not just theory
- Strong communication skills — you will be interviewing staff and translating what you learn into structured documentation
- Model-agnostic architecture experience (LangChain, LlamaIndex or similar orchestration frameworks preferred)
- Ability to recommend tools and integrations without pushing a particular vendor agenda
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WHAT SUCCESS LOOKS LIKE
At the end of this engagement the business has:
- A complete map of how it operates and where knowledge lives
- A structured knowledge base any team member or AI agent can reference
- AI agents deployed and running that reduce manual workload
- A system that can evolve — new models, new agents, new integrations — without starting over
TO APPLY
Please answer the following in your proposal:
1. Describe a similar project you have completed — what was the business, what did you build and what was the outcome?
2. How do you approach extracting knowledge from non-technical staff who have never documented their processes?
3. What is your preferred stack for building model-agnostic AI agents and why?
4. What do you see as the biggest risk in a project like this and how would you mitigate it?
Generic proposals will not be considered. We want to understand how you think.
We are looking for an experienced AI implementation specialist to help modernise a 40-year-old established business. This is a multi-phase project and we need someone who has done this before — not someone who is learning on the job.
THE PROJECT
This business has decades of operational knowledge sitting in the heads of key employees, email threads, legacy non-cloud systems and Xero. Our goal is to extract, organise and operationalise that knowledge — and then layer AI on top of it.
Think of it in three phases:
Phase 1 — Knowledge Audit
Audit the business to understand where information currently lives. Map all systems, processes, tools and key person dependencies. Identify gaps and risks.
Phase 2 — Operational Knowledge Layer
Capture all extracted knowledge and organise it into a structured, searchable system (Notion or equivalent). This becomes the foundation — SOPs, decision frameworks, process documentation, supplier relationships — everything the business needs to run independently of any one person.
Phase 3 — AI Agent Implementation
Once the knowledge layer is in place, implement AI agents that can work across it. This could include agents for customer communication, internal Q&A, operations, reporting and more.
IMPORTANT — MODEL AGNOSTIC APPROACH
We do not want to be locked into any single AI model or provider. The architecture must be designed so that the underlying model (Claude, OpenAI, Gemini etc) can be swapped out without rebuilding from scratch. Please only apply if you have experience building model-agnostic AI systems.
WHAT WE ARE LOOKING FOR
- Proven experience auditing business operations for AI readiness
- Experience building knowledge management systems in Notion or similar (Confluence, Coda)
- Hands-on experience deploying AI agents in a business context — not just theory
- Strong communication skills — you will be interviewing staff and translating what you learn into structured documentation
- Model-agnostic architecture experience (LangChain, LlamaIndex or similar orchestration frameworks preferred)
- Ability to recommend tools and integrations without pushing a particular vendor agenda
---
WHAT SUCCESS LOOKS LIKE
At the end of this engagement the business has:
- A complete map of how it operates and where knowledge lives
- A structured knowledge base any team member or AI agent can reference
- AI agents deployed and running that reduce manual workload
- A system that can evolve — new models, new agents, new integrations — without starting over
TO APPLY
Please answer the following in your proposal:
1. Describe a similar project you have completed — what was the business, what did you build and what was the outcome?
2. How do you approach extracting knowledge from non-technical staff who have never documented their processes?
3. What is your preferred stack for building model-agnostic AI agents and why?
4. What do you see as the biggest risk in a project like this and how would you mitigate it?
Generic proposals will not be considered. We want to understand how you think.