Business-practice infrastructure
The layer above the LLM
Your CIO and CTO are reimagining their work with AI. We bring the same superpowers to everyone else in the organization: intelligent, grounded, and continuously improving.
We make AI usable for real work by giving it what it’s missing: knowledge of your business, a place in your systems, and work that arrives with its evidence attached, so experience, judgment, and taste stay in charge.
The system your AI is missing
Software engineers got AI tools like Cursor and Claude Code with the practice built in. Everyone else got a chat window and a policy memo. We build the missing infrastructure: reusable, customized to your organization, one job or workstream at a time. It comes in three parts:
01 · The model
A model of your business the AI has to answer to
Not the internet’s general knowledge: your customers, your constraints, your way of working, built from your own material with every claim traced to its source. When the AI says something, you can see where that came from.
02 · The workflows
Workflows for your real jobs, wired into your systems
The market analysis, the grant assembly, the campaign package, encoded end to end so the work runs the same way every time, on your infrastructure, under your control. Not prompts someone remembers; systems that run.
03 · The loop
A checking loop that catches the AI being wrong
Before it acts, the system says what it expects. Afterward, it compares expectation to reality. Small misses correct themselves; big or repeated misses stop and raise a hand for a human. Failure gets loud instead of silent.
What this is: a build partner, with a working interface. We work with you to find the problem, we build the solution, we spec the integration, and we help you manage the rollout; your systems stay with your IT team. Your S37 Workspace carries the engagement: status and steering, around the clock. What we build is yours, we support it only when you need us, and we are done when your team has the mental model and knows how to run it.
So what do you actually do with it?
Point it at the work that eats your team’s time: marketing campaigns, qualitative market analysis, competitive research, grant assembly, etc. It runs them end to end, grounded in evidence.
You could build these skills yourself, and plenty of people do. But without the infrastructure underneath, they inherit the same problem you started with: they make things up and fail quietly. Grounded in Signal 37, they hold up. It can take a few shapes; start anywhere you like:
Turn research you already have into a strategy you can act on, every claim sourced.
Find where your effort is leaking, and get the plan to fix it.
Your workflows, encoded into your stack and grounded, built with you.
A recurring, high-stakes job, packaged to run in-house without us in the room.
Keep it all honest as your reality shifts.
You’re probably in one of two spots
95 percent of enterprise AI pilots return no measurable value, by MIT’s 2025 count. We work with the leader responsible for a function or a business: the AI is bought and not enough has changed, or it isn’t bought yet and there’s no plan.
Bought, not effectively adopted
Enterprise seats are there, everyone has access and they read the rollout memo. But the mental model of how to use it and how to build on it, never arrived.
Start a conversationNot bought, need a plan
AI will change how your team works. We build the plan first, so the first tool you buy is one your people actually use.
How an engagement worksReal work, already running
We built the grant-assembly system for researchers at Fred Hutchinson Cancer Center and the University of Washington: months of work to hours, a high-frequency, high-value research workflow encoded into working scaffolding. The client's own way of working changed with it, stepwise, as his mental model of the system matured.
We proved these tools on WonderTwin: an AI-native system twin, a running model of how real vendor systems behave in production, so teams build against how software actually works, not just the docs. Two people took it from zero to a working product and an acquisition by LocalStack in three months (2026); everything outside the engineering ran on the tools we now build for clients.
WonderTwinWe ran a full market-segmentation synthesis for an operating cycling brand's real question: 29 interviews across three languages in four days, raw utterance to strategic framework in seven. Analog practice runs that arc with four people and five weeks. Client name available on request.
name withheldby request
We run the marketing function of a Series A startup, agentic end to end: repositioning strategy pressure-tested by adversarial review, target-account intelligence compiled into complete campaign packages, CRM automation, and a weekly reporting loop the system generates itself. Strategy through execution, one system.
