AI development.Practical. Production. Proven.
LLM features, knowledge assistants, agents and document automation — designed, evaluated and shipped into production by a team that builds its own AI products, not just demos.
Production-grade
Evals, guardrails and monitoring built in.
Grounded in your data
RAG done properly — accurate and current.
Safe & private
Your data stays yours, with governance intact.
AI shipped into production for real businesses
AI development that ships, not just demos.
The gap between an impressive demo and a production system is where most AI projects die. We live in that gap.
LLM Product Features
AI woven into your product — drafting, summarising, classifying — with the UX and cost model thought through.
RAG & Knowledge Assistants
Assistants grounded in your documents and data that answer accurately and cite their sources.
AI Agents & Automation
Multi-step agents that act in your systems — with approvals, guardrails and audit trails.
Document Intelligence
Extraction, validation and workflow powered by LangParse — our own document AI platform.
Evaluation & Model Strategy
Structured evals across Claude, GPT, Gemini and open models — evidence-based model choice per task.
Support & Chat Automation
Customer-facing assistants that deflect the repetitive work and hand off gracefully to humans.
Why do most AI projects stall — and how do you avoid it?
Most stalled AI projects fail the same way: they start with the technology instead of the workflow. A model that's 90% accurate is a triumph in a demo and a liability in accounts payable — unless the system around it knows which 10% to route to a human. Production AI is mostly engineering around the model: evaluation, confidence handling, guardrails, cost control and monitoring.
We build with that discipline because we ship our own AI products — LangParse processes real documents for real businesses every day, and the AI scan on our homepage runs the same stack we sell. If you want to find where AI genuinely pays off in your business, start with a Discovery Workshop or tell us what's eating your team's time.
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.
Model-agnostic, on purpose.
Other vendors showed us demos. Flipmind showed us an eval harness, a cost model and a rollout plan — then shipped a system our compliance team actually approved.
AI Development — Frequently Asked Questions
Start where repetitive knowledge work meets high volume: support responses, document processing, data entry, report drafting. These have measurable ROI and bounded risk. Our AI Discovery Workshop maps your specific opportunities in half a day and ranks them by value and feasibility — it's the lowest-risk first step we offer.
Focused AI features — an assistant, a document pipeline, a classification system — typically run NZ$30k–$100k to production. A proof-of-value pilot on your real data usually lands in the NZ$10k–$25k range and takes a few weeks. Ongoing model costs are part of every estimate we give — no surprise API bills.
We architect for privacy by default: enterprise API tiers where prompts aren't used for training, cloud-hosted models inside your own AWS or Azure boundary, or self-hosted open models where residency demands it. Data governance is part of the design conversation from day one, not a retrofit.
It genuinely depends on the task, and the honest answer changes every quarter. That's why we run structured evaluations on your actual workload rather than following benchmarks or fashion. Often the right architecture uses several: a frontier model for hard reasoning, a cheap fast model for volume work.
Confidence thresholds, human review queues, guardrails on actions, and continuous evaluation against real cases — the same architecture we built into LangParse. AI systems should know what they don't know and escalate accordingly. That's the difference between production AI and a chatbot with access to your database.