We redesign a critical workflow so your people and AI agents work from the same decisions.
We build it, run it on real work until it holds, then hand it to your team.
No deck, no slide-walk. A working conversation about where your growth is stuck.
A leading global strategy consulting firm's teams had AI access to a year of project history — but not a reliable way to know which decisions still governed the work. Reusable AI workflows made decisions more consistent and reduced repeated review.
Read the case study →Access permissions determine what AI may read — not which business decisions are current, why they changed, or who owns them. Why decision ownership, supported by AI, is the operating fix, drawn from a recent engagement with a global strategy consulting firm.
Read the essay →Start with Design & Prove on one consequential workflow. Finish with a capability your team owns and runs without us.
See the model →You need a better way to run the work that matters, and a team that can run it. If any of these sound familiar, that's where we start.
Drafts come back polished, but they revive options you already rejected and ignore constraints you set last week.
Proofs of concept impress, then stall without an owner, a baseline, or a business case.
People spend their time reconciling sources, rechecking outputs and rebuilding context at every handoff.
You have the tools and the budget. What's missing is a workflow that performs, with numbers to show it.
People and AI agents working together to make better decisions and execute faster. You buy a working business capability, not a technology implementation or a recommendation.
Most firms stop at the handoff. We run the capability on real work until it performs, then make sure your team can run it without us.
We pick one consequential workflow, measure how it runs today, and prove the approach on real work before you commit to the build.
We redesign how your people and AI agents share the work: who decides, what AI researches, analyzes and coordinates, and where exceptions go. Then we implement it in your systems, with your data and constraints.
In practice: Improving decision quality in AI-enabled product teams
We run it on real work alongside your team. We measure outcomes, find where it breaks, and tune responsibilities, approvals, quality, cost and controls until the results hold.
We train the people accountable for it and hand over the workflows, agents, decision history, integrations, evaluation methods, SOPs and metrics, against handoff criteria agreed up front. The decisions and context behind the workflow live in teamctx, our open-source context layer, in your own repository.
The scaffolding comes down. Ongoing optimization is available if you want it. It's never required to keep the capability running.
AI does the research, analysis and coordination, and flags uncertainty instead of guessing.
A named owner is accountable for each decision and outcome. Agents are never the authority.
Decision quality, cycle time, cost and reliability. Not how many agents we deploy.
Support is optional, never a condition for the capability to keep working.
Decide what to build, who it's for and why it wins, with named owners and a board-ready plan.
See Product Growth →A working go-to-market engine, deployed in your business and handed to your team.
See GTM System →AI that pays back: readiness, data foundations, AI cost, and function-by-function deployment.
See AI Optimization →Our most recent client work, newest first.
A Fortune 500 apparel and lifestyle company benchmarked agentic AI against its own 18-month innovation process. A 40+ agent system, grounded in proprietary data, compressed roughly 12 months of planning and cross-functional coordination toward weeks — and leadership operationalized it.
A healthcare platform turned stuck pilots into five enterprise expansions, lifting pilot → Phase 1 conversion by 80%.
A healthcare platform cut enterprise cycles from 18 to 6 months and killed 90% of stalled deals with a three-role qualification framework.
A healthcare company consolidated independent modules into a Digitize → Analyze → Optimize platform, launched in 6 months with 80% lift in market alignment.
A hospital systems platform rebuilt its narrative into a Land → Expand → Deepen growth story and closed investor ambiguity across product, GTM, and financials.
A healthcare platform unified fragmented products into a single multi-stakeholder enterprise narrative and opened $12M+ in qualified pipeline.
Industries we know
Working across jurisdictions? Read: The sovereignty tax
AI-native version control for team context. The decisions, rules and assumptions behind the work, approved by the people accountable for them and available to every AI tool your team uses. It's how a handoff keeps its reasoning.
PE operating partners: prove a capability in one portfolio company, then adapt it across the rest.
Only 12% of brands have AI fully integrated into marketing workflows. Why the real question isn't what to cut from agency scope, but which capabilities should become core — and the barbell reshaping agency fees.
AI made building cheaper. It did not make ownership cheap. Why the real cost of enterprise AI sits across human oversight, orchestration, infrastructure and operations — and a framework for deciding what to build, buy, partner for or rent.
SaaS was built on pooling everything across one global deployment. AI and jurisdictional regulation just broke that premise — six sovereignty design levers, four hyperscaler bets, and the tiered architecture that turns the tax into a moat.
AI capability is no longer scarce. Organizational legitimacy is. Why the bottleneck in enterprise AI has shifted from model access to the operating model that decides who answers for what an agent does.
Founder & CEO
25 years in tech and AI platforms.
Former P&L Executive at VC-funded social media, ecommerce, fintech, digital healthcare, and consumer SaaS companies.
Founding CTO, Advisor
25+ years in tech and AI
CTO at modern platforms, startups, e-commerce, fintech, AI, and blockchain.
CPO, Advisor
30+ years in tech, software and AI.
B2C and B2B across security, social, dev tools, edtech, martech, media, healthcare.
We build AI-native operating capabilities inside your business. We redesign a critical workflow so your people and AI agents make better decisions and execute faster, run it on real work until it performs, then train your team and transfer ownership.
With Design & Prove. We select one consequential workflow, establish a baseline, and validate the approach on real work before you commit to a larger build.
Everything needed to run and improve the capability: workflows, agents, project context and decision history, integrations, evaluation methods, SOPs and operating metrics. Ongoing support from us is optional.
Fixed scope and milestone-billed against named deliverables. No hourly billing and no open-ended retainers. We scope on the first call; a statement of work typically follows within 5 business days.
No. teamctx is our open-source (MIT) context layer for decisions, assumptions and ownership. Where it fits the workflow, we use it so that context transfers with the capability and stays in your repository after we leave. There's no licence to buy.
Leadership teams at Series B–D and PE-backed companies, typically $20M–$200M in revenue and 100–1,000 people, in the US and EU.
Senior operators only. Fixed scope with no hourly billing. We build and run a working capability rather than hand over recommendations, and we design every engagement so you stop needing us.
How long would it take your team, and its AI tools, to find the current version, who owns it and what it affects? If the answer isn't obvious, that's where we'd start.
Book a 30-minute conversation