AI agents that do real work.Not demos. Deployments.
Agents that answer your customers, chase your invoices, draft your quotes and process your paperwork — supervised, measured, and honest about their limits. Built by the NZ team behind LangParse, for businesses that want outcomes, not AI theatre.
Human in the loop
Agents propose; people approve — until trust is earned.
Measured, not vibes
Accuracy and cost tracked from day one.
Weeks to value
A working pilot on your data, fast.
AI in production for businesses you know
Agents for the work nobody wants to do.
The best automation targets are boring: high-volume, rule-adjacent tasks where a supervised agent saves hours every single day.
Customer Support Agents
First-response agents grounded in your docs and policies — answering the 70% of tickets that repeat, escalating the rest to humans with full context.
Quoting & Estimating
Agents that read the enquiry, look up your pricing rules and draft the quote — your team reviews and sends. Hours to minutes.
Document Workflows
Invoices, claims, contracts and onboarding packs processed by our LangParse platform — extracted, validated, delivered into your systems.
Back-Office Automation
The copy-paste between systems, the chasing, the reconciliation — agents that work your workflows overnight and flag the exceptions.
Research & Monitoring Agents
Competitors, tenders, prices, compliance changes — watched continuously, summarised daily, delivered where your team already works.
AI Visibility (AEO)
When customers ask ChatGPT who to buy from, is it you? We make your business legible to AI assistants — structure, schema and the content they cite.
What agents can actually do in 2026 — honestly
The gap between AI-agent marketing and AI-agent reality is wide, and businesses get burned in it. Here's our honest read: agents are genuinely excellent at high-volume tasks with clear inputs and checkable outputs — support triage, document processing, drafting quotes against known rules, moving data between systems. They are not ready to run unsupervised through ambiguous, high-stakes decisions, and anyone selling you a fully autonomous employee is selling ahead of the technology.
The pattern that works is supervised autonomy: the agent does the work, a human approves the output, and the approval rate is measured. As accuracy proves out on your real data, the supervision narrows to exceptions. That's how you get the savings without betting the customer relationship on a language model's good day — and it's how every agent we ship goes to production.
The right first step is small and concrete: pick the one task that most obviously eats your team's week, and pilot an agent on it with real data in a few weeks. Our AI Discovery Workshop finds and ranks those tasks — or if you already know yours, tell us about it.
From idea to production, without the drama.
A predictable process, working software every fortnight, and no invoice surprises. Here's how a project runs.
Scope
A free scoping call, then a written proposal with honest costs, timeline and the trade-offs that matter.
Design
UX flows and interface design validated with real users — before a line of production code is written.
Build
Fortnightly releases you can click, test and steer. You always know exactly where the project is.
Launch & grow
Production launch with monitoring and support — then a roadmap of improvements driven by real usage.
Built on the grown-up AI stack.
We expected a chatbot; we got a colleague. The agent drafts every quote against our pricing rules and our estimator just reviews them — what took a morning now takes twenty minutes, and the accuracy is better than ours was.
AI Agents & Automation — Frequently Asked Questions
A focused pilot — one task, your real data, measured accuracy — typically runs NZ$15k–40k and ships in 3–6 weeks. Production deployments with integrations, guardrails and monitoring generally land NZ$40k–150k depending on scope. Ongoing costs are mostly model usage, which we track and optimise from day one. You get real numbers after one scoping call.
Left unsupervised and ungrounded — yes, eventually, which is why we don't build them that way. Our agents are grounded in your documents and rules, constrained in what they're allowed to say and do, measured against accuracy thresholds, and escalate to humans whenever confidence drops. The failure mode is 'asked a human', not 'made something up to your customer'.
The boring ones: high-volume, repetitive, with clear inputs and a checkable output. Support triage, document data entry, quote drafting and inter-system copy-paste are the classic winners. The wrong first project is the ambitious, high-stakes one — start where mistakes are cheap and volume is high, prove it, then expand.
In our experience they replace the worst hours of people's jobs, not the people. The quote-drafting agent doesn't remove your estimator — it removes the re-typing so they estimate more, faster. Teams that frame it that way get enthusiastic adoption; teams that frame it as headcount reduction get quiet sabotage. We'll tell you that on day one.
That's usually the actual project. Agents earn their keep when they can read from and write to your CRM, ERP, inbox, and databases — and two decades of systems integration is exactly our home ground. If it has an API (or even if it only has a database), we can probably wire an agent to it.
That's AI visibility — making your business legible to AI assistants. It overlaps with good SEO (structure, schema, authoritative content) but has its own layer: being citable, consistent across sources, and machine-readable. We audit where you stand and fix the gaps; it's an emerging discipline and frankly most of your competitors haven't started.