We build applied AI that survives contact with real users, real data and real regulators. Three practices — RAG & Knowledge, Agentic AI and Generative AI — delivered by the same team that stays on to evaluate, harden and operate what we ship.
Not a slide deck of buzzwords — the specific, repeatable engineering that moves an AI idea from a promising demo to a system your team depends on.
Grounded, cited answers over your documents, tickets and databases — with hybrid retrieval, reranking and access control so responses stay accurate and permissioned.
See the practiceMulti-step agents that plan, call tools and take action — with human-in-the-loop checkpoints, observability and guardrails against runaway behaviour.
See the practiceContent, code and creative generation — prompt-engineered, fine-tuned where it pays, and wrapped in brand, safety and structured-output controls.
See the practiceOffline and online evals, golden datasets and regression suites so every prompt, model and retrieval change is measured before it reaches users.
Tracing, cost and latency budgets, drift alerts and CI/CD for prompts and pipelines — the operational spine that keeps AI dependable at scale.
PII handling, red-teaming, audit trails and policy guardrails — engineered for regulated, high-throughput teams, not bolted on afterward.
Retrieval-Augmented Generation that answers from your ground truth — with citations, not confident guesses. We treat retrieval as an engineering problem, because that's where most enterprise AI quietly fails.
We design ingestion pipelines that parse, chunk and embed messy real-world documents; run hybrid search that combines vector similarity with keyword precision; and rerank candidates so the model reasons over the right context, not the merely similar. Every answer is grounded and cited, hallucinations are contained with abstention and confidence thresholds, and knowledge respects the same access controls as your source systems.
Agents that plan, use tools and take real action across your systems — designed by people who have run them in production and know exactly where autonomy goes wrong.
We build multi-step agents with explicit planning, typed tool and function calling, and orchestration patterns that keep long-running tasks reliable. High-stakes actions pause for human-in-the-loop approval; every step is traced and observable; and guardrails cap spend, loops and blast radius so an agent can never quietly run away. We ship them with evals that score task success and safety, not just vibes from a demo.
Content, code and creative generation that stays on-brand, on-policy and structured enough to plug straight into your systems — at a volume humans alone can't reach.
We engineer generation pipelines for copy, code, images and multimodal outputs, then wrap them in the controls production demands: prompt engineering treated as versioned software, fine-tuning where it measurably beats prompting, structured JSON output your services can rely on, and brand and safety filters that keep every generation compliant. Human review sits where it adds value, so quality scales without losing your voice.
A significant part of our work is dedicated to the automotive sector throughout the Asia-Pacific region — and New Zealand in particular, where we partner with dealer groups, distributors, fleet operators and aftermarket businesses to put AI to work on the problems that actually move the needle.
RAG assistants over vehicle inventory, spec sheets, finance products and OEM manuals — so sales and service teams answer buyer questions instantly, in-market and on-brand.
Agentic workflows that triage service bookings, look up parts compatibility, and draft repair estimates across DMS, parts catalogues and warranty systems.
Generative and retrieval pipelines tuned to NZ and APAC regulations — vehicle imports, WoF/CoF, emissions and Clean Car obligations, and consumer-finance disclosure.
Automotive AI that ships in Auckland is not the same as automotive AI that ships in Detroit. Right-hand-drive fleets, used-import supply chains from Japan, local lending and insurance products, te reo Māori and multilingual APAC customer bases, and country-specific compliance all change the data, the prompts and the guardrails. We build for those realities from day one.
Start small and prove value, or bring us in to build and run the whole thing. Every engagement is fixed-scope, honestly estimated and built to ship.
A fixed-scope sprint to de-risk an idea before you commit budget to a build.
End-to-end delivery of a production AI system, from prototype to launch.
We run, monitor and improve your AI systems so your team can focus on the business.
The same four-stage method behind every system we've put into production. Nothing is left as a lab experiment on a shelf.
We map your workflows, data and risk profile to find the highest-leverage AI use cases — and rule out the ones that won't pay off.
A working proof-of-value in weeks, evaluated against your real data with honest, quantitative metrics.
Hardening, evals, observability, governance and CI/CD — the unglamorous engineering that makes AI dependable.
We monitor, tune and extend your systems as models, data and business needs evolve.
Bring us your data, your workflow and your goal. We'll tell you honestly what AI can — and can't — do for you, and what it would take to ship it.