AI-native systems that
ship & scale.

The Reality-Check Audit is the diagnostic that maps the leverage point — the single place inside your team where the smallest intervention unlocks the most. We assess the codebase, the team, the workflows, and the AI surfaces you've already built or experimented with. We model what doing nothing actually costs. And we hand you a prioritized action plan you can run with — whether or not we work together on the next step. Every engagement starts here because we won't propose a deployment until we understand what's actually in the way.

The first two weeks are the Reality-Check Audit — we're inside your codebase, your tools, and your team's workflows, mapping what's slowing the work down. From there, we scope a target system to install: an AI-native workflow, a piece of execution infrastructure, or a set of tools that consolidate work that's currently fragmented. We design it together with the people who'll inherit it. We build it in production, not in a sandbox. We pair with your engineers as we go, so the knowledge transfers in real time instead of in a handoff doc at the end. By the time we leave, the system is shipped, your team is operating it, and you have the documentation and decision trail that makes it theirs. Engagements typically run six to twelve weeks. The shape stays the same; the scope changes based on what the audit surfaces.

Most consultancies hand you a strategy deck and disappear. Most contractors build whatever you point at. Fulcro does both — we make the strategic call about what to build (and what not to build), and then we ship the implementation inside your team. We're not handing off a recommendation. We're not pulling work out of your codebase to do it ourselves. We're inside the team for the whole engagement, and the people who'll operate the system afterward are the same people we built it with. The difference shows up at handoff: nothing leaves the door with us — not the code, not the architecture, not the operating knowledge.

Most of our work now is inside teams that already have engineering, product, and revenue — companies past their first scale-up, where the existing execution model has stopped fitting the work. The classic engagement is a Head of Product or VP of Engineering at a growth-stage company who needs to install a new way of operating without disrupting what's already shipping. Early-stage founders are still welcome and we still take that work, but the highest-leverage engagements have been in the middle: too big for a generalist contractor, too specialized for a Big 4 consultancy.

Yes. Most of our engagements happen alongside an existing engineering team — that's where the work has the most leverage. We're there to complement the team, not replace it. We make architectural calls, pair on the hard parts, and install the new workflow inside the team's existing processes. The goal is always to leave the team more capable than we found them, with the knowledge to operate and extend whatever we built. If we did our job right, the team doesn't need us afterward.

Mish runs every engagement personally as the lead practitioner. The strategic calls, the architecture decisions, the code reviews, and the team-facing work all stay with him — no "sold by the founder, delivered by an associate." When the work needs additional hands, Fulcro brings in a small set of trusted collaborators on a per-engagement basis, working directly under Mish's direction. You always know who's in the room and why.

AI is a layer, not a feature. We use it where it does work that traditional code can't do well — extracting structure from unstructured data, making fuzzy decisions at scale, mediating between humans and information. We don't use it where traditional code is faster, cheaper, more reliable, or more maintainable. The question isn't "can we use AI here?" — it's "is AI the smallest, most reliable system that does the job?" Our default bias is toward the boring solution. We only reach for the model when boring stops working.

Buy first, build only when buying creates the bottleneck you're trying to solve. A surprising amount of "we need a custom AI system" turns out to be solvable with a SaaS tool the team didn't know existed. Part of what the Reality-Check Audit does is map the existing buy-able landscape and tell you honestly when something off-the-shelf will do the job. We only propose building when the build genuinely earns its keep — usually when the bottleneck is integration, control, or strategic differentiation, not raw functionality.

All work products belong to you. Every line of code, every architectural document, every credential, every operating runbook — yours, transferred at handoff, full IP. Fulcro retains no rights to the code we write for you and no usage of your data outside the engagement. We work under your standard NDAs and MSAs.

You don't lose access. Every Fulcro engagement ends with a real handoff — documentation, runbooks, training, and a decision log so the team knows not just what was built but why. After that, two things can happen. Most clients go fully independent and operate the system themselves. Others keep us on a light retainer (Embedded Advisory) for ongoing thinking-partner work — architecture reviews, second-set-of-eyes on big calls, occasional pair-programming on the hardest decisions. Both are good outcomes. We don't structure the original engagement to push you toward one or the other.

Engagements are scoped after the Reality-Check Audit, not before. We don't quote a price until we've seen the work — that way the proposal reflects the actual leverage point instead of a generic hourly rate. We work on fixed-fee engagements rather than time-and-materials, so you know what you're committing to. Pricing scales with the scope of the system being installed, not with seat count or team size. Procurement-friendly contracts, standard NDAs and MSAs, net-30 invoicing.

Security-first by default. That means least-privilege access, encrypted data at rest and in transit, audit logging on every AI surface, and a documented threat model for any system that touches sensitive data. We've shipped against SOC 2, HIPAA, and GDPR requirements, and we work inside customer environments for clients with stricter security postures. If your compliance team needs to walk through architecture before we start, that's expected — we'd rather have the conversation early than late.