How we work, in six straight answers.
The questions buyers ask before hiring a studio — answered as policy, not promises. Every answer below is contract language, available in writing.
Red flag #1
Who actually delivers the work?
The engineers named on the proposal are the engineers who ship the work. No bait-and-switch to a junior bench after signature — the team is small and senior by design.
Every proposal names the people, their roles, and what each is accountable for. You can meet them before you sign.
Red flag #2
How are we billed, and what happens if scope changes?
Milestone billing, 30 / 40 / 30: 30% at kickoff, 40% at the midpoint demo, 30% at delivery — which includes 30 days of post-launch support. Your money tracks delivered work, never hours claimed.
Scope changes are re-scoped in the open at the midpoint demo. You will never discover a surprise invoice.
Red flag #3
Where does our data go?
Your infra, your keys, no public-API leakage by default. AI features are architected so your data stays on infrastructure you control; a written DPA is available on request.
For AI engagements we state in the proposal exactly which models see which data, and what is logged where.
Red flag #4
What happens after launch?
30 days of post-launch support is in the price of every tier — not a paid add-on. Bugs, tuning, and handover questions are covered while your team takes the wheel.
After that: a support agreement if you want one, or a clean handover if you do not. The codebase is documented for your in-house engineers either way — 100% code ownership, always.
Red flag #5
Have you actually shipped real AI systems?
Production AI systems in the last 18 months — including TalkDrill, which we run ourselves (50,000+ users, 4.9★, real-time voice AI).
Running our own AI products means we live with the consequences of architecture decisions — latency budgets, model costs, moderation, uptime — not just the demo day.
Red flag #6
Is "AI-fast" real, or marketing?
Real, and here is the mechanism: we build with Claude-class coding agents, AI-assisted scaffolding, and generated test suites — the same class of AI we ship for clients. That is how an AI MVP takes 8 weeks against the 6-month traditional quote.
What stays human: architecture, security review, data-handling decisions, and every merge — senior engineers review all AI-drafted code before it lands. Fixed pricing keeps the incentive honest: compression shows up in your quote and calendar, not our margin.
Own-history proof: TalkDrill voice pipeline, 11 weeks to production. 365 automated publishing workflows last year. Client portal, live in a month.
What we don't do
Hourly billing. Junior benches. AI demos that never ship. Scope creep by stealth. Lock-in — to our retainers, our stack, or us. If the fit is wrong, we say so on the first call.