Operating context
Signals, knowledge and decisions move through a controlled journey built around Agent Assist. Each point represents a person, trusted information, a business rule or a connected system contributing current context.

Support people during live interactions with relevant knowledge, guided actions, concise notes and quality prompts.
Discuss Agent Assist

Signals, knowledge and decisions move through a controlled journey built around Agent Assist. Each point represents a person, trusted information, a business rule or a connected system contributing current context.
AI assistance and human judgement stay connected as each interaction progresses. Intelligence supports understanding and action while explicit controls preserve confirmation, authority and escalation.
The platform layer brings channels, knowledge, workflow logic and human support into one operating model. That matters because a useful AI experience must do more than answer a question: it must understand the stage of the journey, work with approved information and know when a person should take over.
For Agent Assist, the first step is to define the journey in operational terms. AIIT identifies the questions people ask, the information required, the systems involved and the moments where confidence or authority changes. This creates a bounded design that teams can review before automation is introduced.
The experience can then combine Real-time knowledge suggestions, Guided processes and next-best actions, Automated notes and interaction summaries. Approved knowledge provides grounding, workflow rules preserve required steps, and integration points connect the conversation to current business context. If the request becomes uncertain, sensitive or exceptional, the interaction can pass to a person with the relevant history attached.
Implementation should progress in observable stages. A focused first journey gives stakeholders space to test language, decision boundaries, handovers and operational ownership. Feedback from real use can inform measured refinement without turning the system into an uncontrolled collection of disconnected responses.
The potential value is practical: Reduce time spent searching; Support more consistent decisions; Shorten after-call administration. The exact result depends on the organisation, its source information and the process being improved, so AIIT keeps discovery centred on the work itself rather than making assumptions about outcomes.
Operating ownership continues after launch. Source information needs responsible owners, journey changes need review and service teams need a clear way to report unexpected behaviour. These routines help the capability stay connected to current business practice rather than becoming a static layer around a changing process.
AIIT brings operational and technical perspectives into the same conversation. This helps teams evaluate not only whether a journey can be automated, but whether it can be supported, governed and improved with confidence over time.


Follow the live conversation in real time.
Surface knowledge and next steps.
Prompt on policy and quality moments.
Draft notes and follow-up tasks.
Help new team members become confident faster with live guidance.
Surface detailed product and policy information during the conversation.
Prompt at the right moment for required statements and confirmations.
Highlight indicators and suggest appropriate support pathways.
Draft notes, outcomes and tasks so people move on quickly.
Give team leaders specific examples for fair, useful feedback.
Suggest suitable products, eligibility checks and the information a customer needs before deciding, while the team member stays in control of the recommendation.
Offer translated knowledge and phrasing support where configured, helping teams serve customers who prefer another language with more confidence.
Agent Assist listens to live conversations and chats and surfaces relevant knowledge as the customer speaks. Instead of searching several systems while the customer waits, your team member sees the policy excerpt, product detail or procedure that applies, drawn from approved sources.
Suggestions are ranked by relevance and show where the information came from, so people can judge its fit and open the full source when needed. As the conversation changes direction, so do the suggestions.
This support is especially valuable for new starters, complex products and infrequent processes. It shortens the time it takes to become confident and reduces the pressure of handling unfamiliar situations live.
Guided processes walk team members through multi-step tasks such as hardship conversations, claims intake or account changes. Each step prompts for the information required, checks it against rules and highlights what must be said or confirmed.
Next-best-action suggestions help people choose the most suitable option for the customer, while leaving the decision with the person. Policy and quality prompts appear at key moments, for example when a required disclosure has not yet been given or when a vulnerable-customer indicator is detected.
The balance matters: guidance supports judgement rather than replacing it, so conversations remain human while outcomes become more consistent.
At the end of an interaction, Agent Assist drafts a concise summary, captures structured outcomes and proposes follow-up tasks. The team member reviews and confirms, then moves to the next customer with far less after-call work.
Consistent summaries improve handovers between shifts and teams, and give managers reliable information about why customers contact the organisation.
For coaching, interaction insights highlight strengths and specific opportunities. Feedback becomes grounded in real conversations, which is fairer for staff and more useful for improving service.
Every AIIT deployment of Agent Assist begins with a clear definition of what the AI may do, what it must never do and when it must hand over to a person. Those boundaries are written as configuration rather than left to chance, so they can be reviewed by operations, risk and compliance teams before anything reaches a customer. Access to systems is granted through scoped connections, and each connection is limited to the fields and actions the journey genuinely needs.
Conversation records, decisions and actions are logged so teams can see what happened and why. Sensitive information can be masked, retained for agreed periods or excluded from storage altogether, depending on your policies and the obligations that apply to your organisation. Client data is kept separate, and AIIT works with your team to align the deployment with New Zealand privacy expectations and your own security standards rather than asking you to accept a generic template.
Governance is also practical. Changes move through testing and approval before release, previous versions can be restored, and performance is reviewed against agreed measures. The aim is simple: a capability your people trust enough to use every day, because they understand how it behaves and can correct it when the business changes.
Large enterprises use Agent Assist to bring consistency to high-volume service, where thousands of similar requests arrive every week across several channels and teams. In that setting, the value comes from dependable handling of routine work, clean handover of complex cases and reliable records that support reporting, compliance and continuous improvement. Integration with existing contact-centre, CRM and core platforms means the capability strengthens current investments rather than replacing them.
Medium-sized companies often face a different challenge: growing demand without a matching increase in headcount. Here Agent Assist helps a small service team cover more hours, respond faster and keep information organised, while the people on the team focus on the conversations that need experience, empathy and judgement. Deployments can start with one department and extend as the benefits become clear.
Small New Zealand businesses benefit from the same foundations in a simpler form. Owners and small teams use AIIT capabilities to answer enquiries when they are busy on the job, capture complete details for quotes and bookings, and present a professional first impression every time. Whatever the size of the organisation, the principle is identical: a Digital AI Employee handles the repeatable work within clear rules, and people remain in charge of the relationships and decisions that matter most.
AIIT does not ask you to rely on generic industry figures. Before a pilot begins, we agree the measures that reflect your goals, which might include response times, completion rates, handover quality, after-hours coverage, customer effort, administrative time saved or the share of enquiries resolved without follow-up. A baseline is captured from your current operation so the comparison is fair.
During and after the pilot, dashboards and regular reviews show how Agent Assist is performing against those measures. Where results fall short, the conversation data usually shows why, whether that is unclear knowledge, a missing integration or a rule that needs refining, and the next improvement can be planned with confidence. This evidence-led approach helps leaders make sound investment decisions and gives teams a clear view of how the capability is helping them.
An AI capability is only as helpful as the information and actions available to it. Agent Assist connects to the systems that already run your organisation, such as CRM platforms, booking and scheduling tools, contact-centre software, ecommerce and order systems, policy or account platforms, knowledge bases and document stores. Those connections allow the AI to answer with current information and to complete tasks rather than simply telling customers to call back later.
AIIT designs each integration around the minimum access required. Read and write permissions are separated, sensitive fields can be excluded, and every action taken through a connection is logged. Where a system does not offer a modern interface, AIIT can work with secure file exchange, email workflows or intermediate services so the journey still flows smoothly. When systems are unavailable, the AI explains the situation honestly and captures what is needed for follow-up.
Connected data also improves the experience for your people. Summaries, outcomes and structured fields are written back to the records your team already uses, so nothing needs to be copied between screens. Reporting draws on the same information, giving leaders an accurate view of demand, resolution and handover across every channel.
Introducing a Digital AI Employee changes how work is shared, and AIIT treats that change with care. We involve frontline staff early, because they know where customers struggle and which tasks drain their time. Their input shapes the journeys, the handover rules and the wording customers hear, which leads to better results and stronger support for the new way of working.
As routine work moves to Agent Assist, team members can spend more time on complex cases, relationship building and service improvement. Training, clear escalation paths and regular reviews help everyone understand what the AI handles and how to work alongside it. The outcome AIIT aims for is not fewer good people, but good people doing more meaningful work with better tools.
AIIT starts with your operation, not a product demonstration. We map the journeys that create the most repetitive effort or the most customer frustration, identify the systems and knowledge involved, and agree what a successful outcome would look like. That shared baseline matters, because it lets everyone judge the pilot on evidence from your organisation rather than on assumptions.
A focused pilot then puts Agent Assist to work on a small number of well-defined journeys. Your team reviews real interactions, refines wording and rules, and decides when coverage should expand. As confidence grows, the same foundations extend to new channels, departments and use cases without rebuilding from scratch. AIIT remains involved throughout, providing configuration, monitoring and ongoing improvement from Auckland, so you have a local partner who understands how New Zealand customers speak and what they expect.
Browse 81 Typical SAM Roles — from receptionist and claims specialist to compliance, sales and operations — each a Digital Employee powered by ANNA and managed by AIM.
Tell us what you want to improve. Your enquiry will go directly to the AIIT team in Auckland.