When AI velocity starts creating risk, not leverage.

For founder-led AI teams shipping fast, costs get noisy and releases get risky. We intervene to restore clarity and stabilize execution.

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Or see the Execution Governor page for solo founders

Common pivot moments: swapping models under load, shipping compliance workflows, or re-architecting multi-tenant foundations.

Best fit: founder-led teams with 3–12 engineers, real users, and rising delivery volatility.

Signals you need an intervention

Velocity without alignment

Shipping happens, but priorities shift weekly. The roadmap keeps changing and the team is unsure what “right” looks like.

Unstable behavior

What worked last week fails today. Reliability slips, confidence drops, and every release feels riskier than it should.

Costs and risk are drifting

LLM spend creeps up, performance degrades under load, and new features compound risk instead of creating leverage.

What the intervention changes

Decision clarity

We isolate the highest‑leverage moves and set the constraints that eliminate ambiguity for the team.

Operational stability

We harden reliability, normalize AI behavior, and install guardrails so shipping feels safe again.

Cost discipline

We reduce waste with smarter caching, model selection, and usage controls without stalling progress.

The Intervention Playbook

1. Triage & Constraints

Define what must be true, what stops, and the narrow set of decisions that de‑risk the next 30–60 days.

2. Stabilize & Instrument

Harden reliability, add observability, and install guardrails so shipping feels safe again.

3. Cost & Behavior Discipline

Normalize AI behavior, reduce volatility, and introduce disciplined usage controls.

4. Operating Cadence

Establish repeatable release rhythm, ownership, and decision hygiene.

View Full Playbook

Recent Interventions

Representative work from seed-stage AI teams and regulated products.

Loma — Multi-tenant foundation

Rebuilt the core SaaS scaffold to unblock shipping and standardize RBAC, auth, and environments.

Before: release regressions and slow environment setup.
Intervention: rebuilt the core scaffold and standardized RBAC, auth, and environments.
After: environment setup: days → hours; onboarding new devs: hours → <1 hour; regressions reduced materially (tracked).

Stepsy — In-product guidance platform

Designed the recorder-to-SDK delivery loop to ship guides safely without redeployments.

Before: frequent rollbacks and slow release cycles.
Intervention: recorder-to-SDK delivery loop to ship guides without redeployments.
After: guide shipping: same-day without redeploys; rollback/patch loops: weekly → rare.

ClaimScribe — Compliance-first correspondence

Built deterministic workflows with human approval and audit trails for regulated claims letters.

Before: compliance risk and inconsistent approvals.
Intervention: deterministic workflows with human approval and audit trails.
After: approval gate enforced + immutable audit trail; review cycle time reduced materially (tracked).

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How Engagements Work

Engagements are limited and scoped after an initial conversation. Typical work involves multi-week interventions rather than ad-hoc tasks.

Not a fit for ad-hoc bug fixing or “build me an MVP” work.

Decision Reset

When priorities are drifting or the architecture is pulling you in two directions. We triage risks, set constraints, and lock the next 30–60 days into 1–2 high‑leverage moves.

Typical fit: unclear roadmap, rising LLM costs, and growing tech debt.

Stability Sprint

When shipping has become risky. We harden reliability, add instrumentation, and reduce cost/behavior volatility so releases feel safe again.

Typical fit: reliability issues, slow delivery, or risky launches.

Risk Governance (Selective)

Ongoing decision support when stakes are high. We pressure‑test strategy, de‑scope distractions, and unblock leadership decisions quickly — without becoming part of the org chart.

Typical fit: founder‑led teams navigating new markets or pivots.