AI-Native Development · New York
New York's AI-native build partner — software designed around agents, not bolted onto them.
We build products where AI is the architecture, not a feature flag: agentic workflows, retrieval, evals and cost controls, shipped to production for New York teams. Not a demo. A system your engineers own.
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Why New York teams call us
The work you can't afford to get wrong.
New York is fintech, media and Wall Street-adjacent tech — AI-native work here is regulated, ROI-driven, and judged on a hard security and risk bar.
We bolted a chatbot onto our product and it works in the demo and falls over with real users.
Everyone says 'add AI'. Nobody can tell us what to build, what it costs to run, or how we'd know it's working.
Our prototype has no evals, no guardrails and no observability — we can't ship it to customers and sleep.
Our token bill is climbing and we have no idea which features or models are driving it.
We need an agent that does real work across our systems, not a wrapper around a single prompt.
What we do
What an AI-native build actually includes.
Every engagement starts with a 30-minute call. If we can't tell you something useful in that window, we don't take it on.
AI-native product architecture
We design the product around the model: where AI sits, where deterministic code sits, and where a human stays in the loop. The boring decisions that decide whether it survives contact with real users.
Agentic workflows
Agents that take real action across your systems — tools, function calling, multi-step plans — with the safeguards that stop them doing something expensive or irreversible.
RAG & retrieval
Retrieval that returns the right context, not the nearest vector. Chunking, hybrid search, re-ranking and the evals that prove it actually grounds answers.
Evals & observability
You can't ship what you can't measure. We stand up evals, tracing and quality monitoring so regressions are caught before your customers catch them.
Model routing & cost control
Opus where it earns its keep, Sonnet or Haiku where it doesn't, nothing where a function call is cheaper. Cost guardrails and budgets wired in from day one.
AI UX that builds trust
Streaming, citations, confidence and graceful failure — the interface patterns that make users trust an AI feature instead of abandoning it.
Why New York
In your timezone. Onsite when it counts.
NYC teams build AI-native products like document and research agents for finance, ad-tech personalisation, and RAG over regulated data — all needing evals, guardrails and observability before they touch a customer or a trade.
We ship in your repo and run our own AI products, designing for SOC 2, the NIST AI RMF and regulated-data controls from day one.
What "good" looks like.
We measure ourselves on what your team is doing differently — not on hours billed.
From idea to an evaluated, working prototype your team can pressure-test with real data.
Every AI feature ships with evals and tracing, so quality is a number you watch — not a vibe.
Typical inference-cost reduction once model routing and caching replace 'Opus for everything'.
Code lands in your repository with your engineers, not locked in a vendor sandbox.
How we engage
Three ways to start. All begin with a call.
Engagement
AI-Native Sprint
2-week build sprint: a working, evaluated prototype of the highest-value AI feature, with the architecture documented. Fixed scope, fixed fee.
from US$10K
Engagement
AI-Native Build
6–10 week engagement: production build of an AI-native feature or product — agents, retrieval, evals, observability and cost controls, in your repo.
from US$50K
Engagement
Embedded AI team
An embedded squad that builds alongside your engineers and leaves them able to run it. 2–4 days a week, quarterly outcomes.
from US$12K/mo
FAQ
Direct answers to what New York teams ask before they book.
Are you based in New York?
We're an Australian-headquartered firm and work with New York teams remote-first in your timezone, with onsite visits for kickoff and key milestones. We work with teams across Manhattan, the Flatiron District and Brooklyn.
How fast can you start?
A 2-week AI-Native Sprint can start the Monday after the scoping call. Larger engagements typically begin 1–2 weeks out, depending on team availability.
Do you work in USD and to US compliance?
Yes — we contract and price in USD. We design for SOC 2, HIPAA where health data is in scope, the NIST AI Risk Management Framework, and state privacy law (CCPA/CPRA). We're an official Vanta partner for the evidence side.
Which models do you build on?
We're model-agnostic — Claude, GPT, Llama and open-weight models — and we route between them on cost and capability. We're not reselling one vendor's stack.
Do you hand it over, or keep us dependent?
Hand it over. Code lives in your repo, the architecture is documented, and the point of an embedded engagement is that your team can run and extend it without us.
Can you build AI that clears a financial vendor security review?
Yes — SOC 2-aligned controls, evals, guardrails and audit logging, mapped to the NIST AI RMF, with Vanta evidence.
Which models do you build on?
Model-agnostic — Claude, GPT and open-weight — routed on cost and capability.
Do you actually build, or just advise?
Both — and we never advise without being willing to build. If we tell you the answer is Apache Superset, we'll stand it up. If we say hire a senior engineer, we'll write the spec and sit on the panel.
Related
Stop benchmarking. Book a call.
Thirty minutes. No deck. We'll either tell you what to do next, or who you should be talking to instead of us.
Book a 30-min call