Project work is available when advisory identifies a clear path and accountable outcome. These are scoped selectively, with fixed scope preferred.
AI Operating System + Golden Repo
Most organizations experimenting with AI development have a consistency problem: every team picks different tools, writes different prompts, invents different specs, and ships to different standards. None of it compounds. We build a production-ready golden repo — the forkable foundation every team starts from, containing the linting rules, guardrails, tooling configuration, language standards, and the spec-driven context files that define what AI-assisted development looks like for your organization. Whether your teams are moving fast with AI assistants or running full agentic delivery cycles, they all ship from the same foundation. That's how individual AI experiments become organizational capability.
Best for
Engineering leaders whose teams are each doing AI development differently, with no shared standards, guardrails, or reusable foundation.
Typical duration
Phased fixed scope based on team readiness and existing stack.
AI Microservice Deployment
Most organizations have dozens of recurring workflows where skilled employees are manually moving data, reformatting reports, or bridging systems that should talk to each other. We identify the highest-value targets, build discrete AI microservices that eliminate them, and deploy within your infrastructure so your data stays yours. The result is measurable: hours recaptured, headcount redeployed, and a proof point your board can see.
Best for
Operations and GTM leaders watching their highest-paid people do work that shouldn't require a human.
Typical duration
Scoped per workflow; most deployments are fixed-fee after discovery.
Synthetic Data Perimeter
Most organizations building with AI are doing so on top of real customer data, and the security posture hasn't kept pace with the velocity. The question isn't whether an incident will happen; it's whether customer data is in the blast radius when it does. Synthetic data replaces production datasets with statistically faithful equivalents that carry none of the real-world liability: your teams build and test against data that behaves like your customers' without being your customers'. When something goes wrong, you get to answer 'our security measures worked as intended; no customer data was exposed.'
Best for
Companies moving fast with AI whose data exposure has outpaced their security infrastructure.
Typical duration
Scoped by dataset complexity and required fidelity.
SaaS Stack Repatriation
AI has collapsed the cost of building custom software so dramatically that the build-vs-buy calculus has quietly flipped for a wide range of enterprise tools. Most SaaS products are fundamentally databases with workflow logic on top, backed by a large sales organization maintaining pricing power. We help clients identify where in their stack they can exert real leverage at the vendor table, and either capture immediate savings or bring those workflows in-house at structurally better economics. The deeper prize is data sovereignty: your business history is currently scattered across vendor systems you don't own, and the companies that consolidate and reclaim that data now will hold a durable structural advantage.
Best for
Enterprise leaders under pressure to cut OpEx who haven't applied AI-era economics to their software stack.
Typical duration
Sequenced around your highest-cost renewals and stack priorities.
