Trusted by teams across Australia, North America and beyond
Sound familiar?
Why San Francisco teams call us.
San Francisco is the global AI epicentre — the bar is whether you can ship something defensible and scalable faster than a well-funded startup.
Bay Area teams build AI-native products at the frontier: agentic systems, retrieval at scale, and AI features where the differentiator is the evals, observability and cost discipline behind them, not the model itself.
We ship in your repo and run our own AI products, building the architecture that turns a demo into something that scales and survives a Series A/B technical diligence.
San Francisco is the global AI epicentre — the bar isn't 'can you do AI', it's whether you can ship something defensible faster than a well-funded startup.
"We bolted a chatbot onto our product. 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 would know it works."
"Our prototype has no evals, no guardrails and no observability. We cannot ship it and sleep."
"Our token bill keeps climbing and we cannot tell which features or models drive it."
"We need an agent that does real work across our systems, not a wrapper around one prompt."
What we do
What an AI-native build actually includes.
Every engagement starts with a 30-minute call. If we cannot tell you something useful in that window, we do not take the work on.
AI-native architecture
We design the product around the model: where AI sits, where plain code sits, and where a human stays in the loop.
Agentic workflows
Agents that take real action across your systems, with safeguards that stop anything expensive or irreversible.
Retrieval that grounds answers
Chunking, hybrid search and re-ranking, plus the evals that prove answers come from your data.
Evals and observability
Tracing and quality monitoring, so regressions are caught before your customers find them.
Model routing and cost control
The large model where it earns its keep, smaller ones where it does not, and budgets wired in from day one.
AI UX that builds trust
Streaming, citations and graceful failure, so people trust the feature instead of abandoning it.
How we engage
Three ways to start. All begin with a call.
AI-Native Sprint
Two weeks to a working, evaluated prototype of your highest-value AI feature, with the architecture documented. Fixed scope, fixed fee.
from US$10K
Get a proposalAI-Native Build
Six to ten weeks to a production AI feature or product: agents, retrieval, evals, observability and cost controls, in your repo.
from US$50K
Get a proposalEmbedded AI team
A squad that builds alongside your engineers and leaves them able to run it. Two to four days a week, quarterly outcomes.
from US$12K/mo
Get a proposalCompliance and data
Built for US rules from day one.
We design for HIPAA where health data is in scope, the NIST AI Risk Management Framework, and state privacy law such as CCPA and CPRA.
We are headquartered in Sydney and work with San Francisco teams remote-first, in your timezone, with on-site visits for kickoff and key milestones when needed.
Also in San Francisco
Other ways we help San Francisco teams.
Questions
What San Francisco teams ask.
Straight answers to what San Francisco teams ask before they book.
Are you based in San Francisco?
We are headquartered in Sydney and work with San Francisco teams remote-first, in your timezone, with on-site visits for kickoff and key milestones when needed. We work with teams across SoMa, the Financial District and the wider Bay Area.
How fast can you start?
A two-week AI-Native Sprint can start the Monday after the scoping call. Larger engagements usually begin one to two weeks out.
Do you work in USD and to US rules?
Yes. We contract and price in USD. We design for HIPAA where health data is in scope, the NIST AI Risk Management Framework, and state privacy law such as CCPA and CPRA.
Which models do you build on?
We are model-agnostic: Claude, GPT, Llama and open-weight models, routed on cost and capability. We do not resell one vendor's stack.
Do you hand it over, or keep us dependent?
We hand it over. Code lives in your repo, the architecture is documented, and your team can run and extend it without us.
Will our AI architecture survive technical diligence?
Yes — we harden evals, observability, guardrails and cost so the AI story is a strength in diligence, not a risk.
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 the answer is Apache Superset, we stand it up. If it is a senior hire, we write the spec and sit on the panel.