Internal knowledge bot
Question → permissioned retrieval → cited answer → review.
Useful AI assistants.
Inside real workflows.
Identify sets up and trains AI agents and assistants around your business knowledge, workflows and rules. We test how they retrieve information, prepare tasks and route exceptions, with the right human approvals for your department and industry. The AI transformation company for Australian small and medium-sized businesses.
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AI TRANSFORMATION · AUSTRALIAN SMALL & MEDIUM BUSINESSESTeams lose time finding information, transferring context and manually preparing repeatable work. AI assistants should reduce that friction without acting outside agreed boundaries.
Buyer: COO · Head of Systems · AI Lead
01 / WHERE TO START
Three workflows worth testing first.
These are not universal prescriptions. They are practical starting patterns with a clear owner, observable work and a measurable outcome.
Enquiry triage assistant
Incoming request → classification → context → responsible team.
Operations reporting assistant
System data → exceptions → draft summary → human approval.
02 / 12 WORKFLOWS
Go deeper than a list of AI tools.
Every workflow is framed as work moving from input to action. That is what lets us map systems, context, human review and the business measure behind it.
Internal Q&A bot
Question → approved knowledge → sourced responseDocument intake
Files → extraction → completeness check → ownerEnquiry classification
Message → intent → team assignmentPolicy retrieval
Permissioned knowledge → answer → citationQuality checks
Draft → rules → exceptions → reviewerOperating summaries
System data → signal → commentary → actionCRM record assistance
Customer context → structured update → reviewFollow-up assistant
Trigger → context → draft → approvalException monitoring
Sources → changes → escalationService request bot
Request → information → routing → responseResearch assistant
Approved sources → summary → human decisionLearning from corrections
Reviewer feedback → tested improvement → reusable patternSet up and train your agents.
We configure agents with your department's approved knowledge, instructions and examples. Agree permissions, human approvals and exception handling, then test before connecting live systems. Your industry shapes the rules.
TRIGGER→CONTEXT→AI ACTION→HUMAN OWNERSHIP→EXCEPTION→MEASURE
04 / WHAT TO MEASURE
AI usage is not the outcome.
Use operating measures that already matter to the function, then compare the new workflow against a baseline.
01Hours returned
02Resolution time
03Human escalation rate
04Accuracy
05Response time
06Time to insight
07Adoption
08Exceptions
Keep accountability visible.
Automation should make the operating boundary clearer, not hide it.
Human approval for consequential actionsUnverified answersSensitive data outside approved accessUncontrolled autonomous actions
06 / FIRST 90 DAYS
Map. Build. Activate.
Audit
Review your actual processes and agree five practical improvements with clear owners, first steps and measures of success.
OUTPUT: FIVE-PRIORITY PLANTrain & Build
Train your people, set up and train AI agents, and test their work against approved examples before connecting live systems.
OUTPUT: TRAINING + REVIEWED PILOTSImplement
Activate what is ready, review real results and agree how the team will maintain and improve the work. Timescales are indicative, not a guarantee.
OUTPUT: VERIFIED PROGRESSShow me what AI could change in my department.
Select your team, size and pain point. Identify will generate five workflow opportunities, the impact dimensions to test and a recommended starting point.
RUN THE AI ASSISTANTS FINDERCan Identify set up and train agents for our department?
We configure agents with approved knowledge, clear instructions, examples and permission limits, then test and refine them with your team. This does not mean training a new AI model from scratch or letting agents learn without review. Your department's workflow, industry requirements and existing systems determine what is in scope.
Where should AI Assistants start with AI?
Start with a repeated workflow that creates visible friction, has a clear owner and can be measured before and after. The strongest first move is usually not the most ambitious use case. It is the one that proves the organisation can Map, Build and Activate a useful change.
Do we need to replace our existing systems?
Usually not. Identify designs around the systems, data, permissions and workflow already in the business. The first question is where AI should sit in the operating model, not which platform should replace everything.
What should we measure?
Use the measures already used to run the function: time, cycle time, throughput, service, conversion, quality, exceptions, cost to serve or another agreed business outcome. AI usage on its own is not the result.
What should AI not own?
Material decisions, sensitive judgement and exceptions need clear human accountability. The exact boundary depends on the workflow, risk and industry.