Frequently Asked Questions

Start with a business problem you can describe and measure. Examples include repeated data entry, slow document handling or skilled people buried in administration.

We map that process and establish its current cost. Then we assess the available options. This keeps the decision tied to a real operating result.

Because a requested solution often reflects the visible problem, rather than its cause, and building immediately can lock that misunderstanding into software.

Discovery gives us enough evidence to make a sound recommendation. We map the relevant workflow, systems, data and hand-offs. The work goes into detail where mistakes or delays cost the business.

A clear view of the problem and its cause, the practical options, likely costs and expected benefits. The analysis also shows what your existing systems can already deliver.

The work belongs to you. Use it internally or ask us to implement it.

Clear rules suit conventional automation: moving files, updating records, sending reminders and applying approval rules. One national furniture retailer's automated fulfilment cut its order cycle from 12 days to 7.

AI suits work involving language, interpretation or varied documents. It can extract invoice fields, summarise correspondence or draft a first response. We map the full process before deciding where each belongs.

Progentic's first step is checking what your current systems can do. Many businesses already pay for useful functions they haven't configured or connected.

We then consider add-ons, integrations and data improvements. A new product or custom build comes later in the assessment. You see the options before making that decision.

For some tasks, yes, and discovery will tell you so. Part of our assessment is what your team can do itself with the tools you already licence, and our leadership capability training exists to build exactly that self-sufficiency.

The gap is usually not the tool but the process around it: where it fits, what it must never do, and how the work changes. That is the part we engineer.

Progentic's discovery phase runs under an agreed cap. We set the budget, focus and limits before work begins, and the cap cannot be exceeded without your sign-off. If the work reveals more than expected, extending is your decision, made with the evidence in front of you.

The findings determine the implementation cost. Each selected piece is then scoped, sized and quoted separately. This gives you a decision point before committing further budget.

No. The discovery work belongs to you. Take the recommendations and run them internally, use another provider, or engage us to implement. Either way, implementation is scoped and priced separately.

Less than you might fear. Discovery needs time from the people who do the work, usually in short, scheduled sessions rather than long workshops, and we fit around operational demands.

Most of the analysis happens on our side. Your team's time commitment is agreed as part of the discovery scope, so it is a known cost, not a surprise.

It depends on the process, available data and number of systems involved. Discovery is deliberately bounded, with its scope agreed at the outset.

After discovery, the implementation proposal includes a timetable. Larger programmes are divided into phases. The first phase targets the clearest operational gain, with results typically visible within weeks of go-live.

We agree the baseline before changing the process. Measures may include processing time, error rates, rework, payment speed or staff hours released.

We also agree how the released capacity will be used. One pricing process fell from 25 hours a month to four. Those 21 hours only create further value when the business uses them deliberately.

No. We document what we build and train your team to run the process and handle exceptions. Where you want your internal IT team or MSP to take over, we train them too.

Ownership of anything custom-built is agreed in the scope. If you want the IP locked down to your business, that can be agreed before the build starts. Ongoing support is available where wanted; it is a choice, not a dependency.

Progentic's work usually targets administration performed by skilled people. The role remains; its mix of work changes.

For example, a recruitment consultant may spend less time processing reference notes. That time can return to candidate conversations and business development. Redeployment should be planned before the automation goes live.

We involve the people doing the work during analysis and design. They know where exceptions, workarounds and delays occur.

Training forms part of implementation. We also define ownership, measures and follow-up support. A technically sound workflow returns nothing when people work around it.

Most failed projects skip either the diagnosis or the adoption work. A tool arrives; the process and the people around it never change.

We start with the process, involve the people who run it, and stay through training and embedding. Success is measured on the operating result, not the go-live date.

Progentic is led by its two directors, who stay directly involved and accountable from discovery through delivery. Behind them is a New Zealand-based team of workflow engineers, developers, project managers, change managers and analysts.

The people you meet at the start remain accountable at the end.

Alongside them, not around them. Your provider usually stays responsible for infrastructure and support, while Progentic works on process, workflow and the solution layer.

Where changes touch their environment, we involve them early and agree responsibilities. We can also train your IT provider to run what we build.

No. Progentic does not use client data to train public AI models, and contractual data controls are in place from the start of the engagement.

Data location, retention and access are set during solution design and documented before anything goes live.

We consider these requirements during discovery and solution design. The controls depend on the data, industry and consequence of an error.

Formal privacy, legal and governance advice sits with qualified specialists. We can bring trusted specialists into the engagement when needed. This may include a Privacy Impact Assessment or AI risk assessment.

Progentic's strongest fit is a New Zealand SME where experienced people lose time to repetitive operational work. Common signs include spreadsheets between systems, repeated data entry and document-heavy workflows.

If skilled people are losing hours to repetitive work, the size of the payroll matters less than the size of the leak. We work across professional services, engineering, manufacturing, retail, healthcare and financial services. The operating problem matters more than the industry label.

A code-to-order brief is unlikely to suit us. We need to understand the process before building anything.

Sustainable results also require a client willing to change how work gets done. If nothing about the process can move, no tool will fix it.

Yes. Progentic works with clients throughout New Zealand. Discovery and delivery both work well remotely, and we come onsite where it helps. Some process observation is best done standing next to the work.

It is a conversation, not a pitch. We ask about the process that hurts, what it costs you, and what you have already tried.

You leave with our honest view on whether discovery is worth doing. If your problem is not one we should solve, we will say so.

Yes, when the business needs something its existing systems or a mature product cannot provide. The build follows process analysis and option assessment.

Sometimes a configuration or integration solves most of the problem. A custom application should cover the business-specific gap, rather than recreate standard functions.

No. The process determines the platform, not the other way around. We work across business systems and specialist products, and the choice follows your requirements, existing licences and integration needs.

That said, most New Zealand SMEs already run on Microsoft, and we have strong capability across Microsoft 365, Power Platform, Copilot and Azure. Often the fastest gains come from switching on what that stack already includes.

Focused, practical sessions for leaders: the tools, approaches and vocabulary to apply automation and AI to their own work first. Leaders adopt first; teams follow.

Format and depth are shaped to your leadership team. The aim is leaders who can sponsor initiatives with confidence and judge proposals on operating merit.

With a revenue goal, not a technology demo. We link AI adoption to the operational constraints holding back the revenue-generating parts of the business.

The roadmap sets out where AI genuinely helps, in what order, and what each step should return. It is a plan you can execute with or without us.

An agent makes sense when a step needs interpretation, research or judgement. Examples include reviewing correspondence, comparing documents or drafting a structured summary. One executive-search workflow cut reference-check processing time by 62%.

We give the agent a bounded task inside a controlled workflow. Predictable steps remain conventional automation. Human review stays where the consequence of an error warrants it.

We keep each AI task narrow and define its permitted sources. Outputs can include source references, confidence indicators and validation checks.

Higher-risk decisions receive human review and an escalation path. We also test edge cases and monitor exceptions. The required controls depend on the cost of a wrong answer.

It can help inspect and classify messy records during discovery. That can reveal duplicates, gaps and inconsistent naming quickly.

Repeated operational work usually needs better structure underneath. Otherwise, the system pays for repeated inference and exception handling. We assess whether cleaning the data now will cost less over the process lifetime.

Yes. The solution design must reflect the information involved and the applicable controls.

One recruitment workflow used recorded calls only after candidate consent. AI produced a draft summary, the firm validated it, and unnecessary transcript data was removed. Formal legal and privacy advice is provided by the appropriate specialists.

Discovery is interviews and observation; it does not interrupt the work. Implementation is staged, with changeover planned around your operating calendar.

The old process keeps running until the new one is proven.

We right-size the method to the work. Process analysis draws on Lean Six Sigma. Project controls draw on PMI and PRINCE2. Technology delivery may use Agile practices.

Change planning uses Prosci principles. Clients see defined scope, ownership, risks, decisions and acceptance criteria throughout the engagement.

We support training, handover and early use. We check the process works in normal operations, that staff can handle exceptions, and post-implementation measures show whether the expected gain is being realised.

Ongoing support is scoped by agreement, from a support retainer with us to training your internal IT team or MSP to run the solution themselves.