How We Work

Working software
every Friday.

Most AI engagements fail because there's no working software until the end — and by then, the scope has drifted past usefulness. Studios runs differently. Weekly demos, evals defined in week one, code you own from the first commit, and a runbook your team can operate on day one.

Six commitments that shape
every engagement.

These aren't aspirations — they're operating rules. Every Studios engagement runs against them, every team member is trained on them, and every client has the right to call us on them.

01

Eval-first design

We define how we'll measure "done" before we write any code. The accuracy benchmark, edge cases, and test set get agreed on in week one — so quality is engineered in, not bolted on at handoff.

02

Weekly demos

Every Friday, you see working software against the eval set. No multi-month dark periods. Feedback compounds into the build, not into a separate revision phase. You always know where things stand.

03

Fixed scope discipline

Productized services and engagement tiers have defined scope before signing. Scope changes happen through change orders, not "while you're at it" requests. Predictable delivery requires predictable scope.

04

You own everything

Code, prompts, configurations, models, deployment infrastructure — assigned to you on full payment. Studios retains methodology IP; you own your specific build. No lock-in, ever.

05

Operational handoff

Every engagement ships with a runbook, monitoring dashboards, and training video. Your team should be able to operate what we built without us. Studios disappearing should never break your business.

06

Honest recommendations

If you don't need custom AI, we'll tell you. If an off-the-shelf tool is better, we'll name it. If timing is wrong, we'll say so. Our reputation as honest practitioners generates more work than aggressive selling would.

Five stages.
One standard across all engagements.

Whether you're signing a 3-week Claude Agent Builder or a 26-week Enterprise rollout, every Studios engagement runs through the same five stages — they just take longer at scale. The structure is the same, the discipline is the same.

// STAGE 01
01

Intake & scope

30-minute intake call to confirm fit, identify the right product or tier, and confirm we're the right team. SOW within 48 hours if it's a match.

// OUTPUT
Signed SOW
// STAGE 02
02

Discovery & eval design

Stakeholder interviews, requirements walkthrough, success criteria definition. We agree on how we'll measure done before writing any code.

// OUTPUT
Eval rubric + architecture brief
// STAGE 03
03

Build & iterate

Engineering sprints with Friday demos. Each demo is working software against the eval set. Changes happen in the build, not after delivery.

// OUTPUT
Working system in sandbox
// STAGE 04
04

Launch & train

Production deployment, observability hookup, runbook handoff. Your team is trained on operating what we built before support window starts.

// OUTPUT
Production deployment + training
// STAGE 05
05

Support & evolve

Defined support window (14-90 days depending on tier) for tuning, bug fixes, and edge cases. Optional retainer for ongoing optimization.

// OUTPUT
Operating system + your runway

A predictable
weekly rhythm.

The weekly rhythm of a typical Studios engagement in steady state. Times shift slightly by engagement and timezone, but the structure stays constant — and you always know what's happening when.

The Friday demo is the load-bearing event. Everything else exists to make Friday's demo useful — the questions you raise, the priorities you set, and the directions you give. If a demo doesn't happen, the week didn't ship.

You receive a Slack channel in your workspace at engagement start. Async questions get same-day responses during business hours. We're embedded, not external.

// MON
Sprint planning Internal Studios standup · Async update to you
30m
// TUE
Working session Optional — when scope needs alignment
As needed
// WED
Mid-week checkpoint Async written update with current eval scores
Written
// THU
Demo prep Internal — final eval pass before Friday
Internal
// FRI
Working demo Live system walkthrough · feedback captured
60m
// ANY
Slack channel Same-day response during business hours
Async

Four artifacts.
Every week.

You should never have to ask "where are we?" These four deliverables ship weekly to your designated stakeholders — by Slack, email, or whatever channel you prefer.

The Friday demo

Live walkthrough of the current build. Working software against the eval set. We show what works and what doesn't, with the actual data and edge cases you care about. Recorded for absent stakeholders.

Eval scorecard

Quantitative scoring against the test set we agreed on in week one. You see exactly how the system performs on accuracy, latency, cost, and edge case handling — and how that's changed since last Friday.

Sprint summary

Written status: what we shipped this week, what's planned next week, decisions made, decisions needed from you, risks emerging. Three minutes to read, designed for forwarding to your leadership.

{ }

Repo access

Your engineering team has read access (and optional write) to the working repo from day one. Every commit is visible. No black-box delivery, no "we'll show you the code at the end."

Four roles per engagement.
Sized to scope.

Every Studios engagement is staffed with four named roles. Productized services run lean (1-2 people across the roles); enterprise engagements have dedicated coverage. You meet your team at kickoff.

// ROLE 01

Engagement lead

Senior practitioner accountable for the delivery. Your single point of contact for scope, timeline, and any escalations. Sits in your weekly demo every Friday.

// 25-40% ALLOCATION
// ROLE 02

AI engineer

The builder. Writes the prompts, designs the agent or RAG architecture, runs the eval loop. The person whose code ships to production at engagement end.

// 50-80% ALLOCATION
// ROLE 03

Integration engineer

Connects the AI layer to your business systems. CRM integrations, webhook handlers, deployment infrastructure, monitoring setup. Present when integration scope warrants.

// 20-50% ALLOCATION
// ROLE 04

Quality lead

Owns the eval rubric, runs adversarial testing, owns the runbook and training video deliverables. Acts as your independent quality check on the engineering work.

// 10-25% ALLOCATION

Studios vs. traditional
AI agencies.

The differences that compound over a six-month engagement. None of these are theoretical — they're the operating realities behind every Studios delivery, drawn from watching how typical agency engagements fail.

// HOW STUDIOS WORKS

Working software
every week

  • Eval rubric defined in week one Quality engineered in from start, not measured at end.
  • Weekly Friday demos against real data Feedback compounds into the build. No "big reveal" failures.
  • Repo access from day one Your engineers see every commit. Code review possible throughout.
  • Fixed-price productized entry points $7.5K to $25K. No discovery-tax engagements.
  • Operational handoff with runbook + training Your team operates what we built. Studios disappears without breaking you.
  • You own the code on full payment No vendor lock-in. Take it to anyone you want.
// HOW MOST AGENCIES WORK

Black-box delivery
with vendor lock-in

  • "Discovery phases" with high fees, low output Months of slide decks before any working software exists.
  • Mid-engagement scope creep Original scope blown by week 4. Change orders never end.
  • Quarterly demos at best You see the system months in. Drift is irreversible by then.
  • Code hidden until "final delivery" You can't validate quality. Engineering review is impossible.
  • Runbooks promised, not delivered Operational dependency on the agency forever.
  • Proprietary platforms you can't take with you Re-platforming is the next $300K engagement.

What you walk away with
on the final Friday.

The handoff package at engagement close. Every Studios engagement — from productized service to enterprise build — ships these three pillars. The structure is the same, the depth scales with engagement size.

{ }

The codebase

Full repository in your account. Custom code, custom prompts, infrastructure configurations, deployment scripts. Yours on full payment with no further dependency on Studios. Studios Background IP (methodologies, reusable patterns) is licensed to you perpetually as embodied in the build.

📖

The runbook

How to monitor, tune, and operate the system. Troubleshooting guide for known edge cases. Cost dashboards. Refresh procedures. Performance benchmarks. Written for your engineering team to operate without us.

The training

Recorded video walkthrough showing your team how to operate, modify, and extend what we built. 30-90 minutes depending on engagement scope. Watch anytime, share internally, refer back when new team members onboard.

Things buyers ask
about how we operate.

The honest answers to the process questions buyers ask after they've decided the work matters and are now evaluating fit.

We'd rather show you a build that's struggling than hide it until the end. A struggling Friday demo triggers an immediate working session — what's not working, why, what's the path forward. Sometimes the answer is a tactical fix, sometimes it's an architectural pivot, sometimes it's "we mis-scoped this and need to talk about it." In every case, you find out in week 4 or 5, not month 4 or 5 — and the engagement adapts before damage compounds. We've never had a Studios engagement fail at handoff because of a quality issue. We've had several deliberately pivot mid-engagement based on what Friday demos revealed.
Through written change orders, not informal "while you're at it" requests. Scope changes happen frequently — most engagements get one or two during the work — and they're a healthy sign that the project is generating real learning. The discipline is: every change has to be a written change order, signed by both sides, before any new work happens. This protects you (you know what new scope costs and adds in time) and protects us (we don't drift into delivering more than we charged for, which never ends well for either side). Productized services have especially tight scope discipline — if you discover you need more, we usually quote a follow-on engagement rather than absorbing scope creep.
Remote by default. Our team is distributed across timezones, which means we cover more business hours than a single-office shop and we're not commuting overhead. We do periodic on-site sessions when the engagement warrants — kickoff for enterprise builds, mid-engagement working sessions for complex integrations, executive briefings at handoff. On-site travel is billed at cost. For Fractional AI CTO engagements at Embedded and Operating tiers, we accommodate a recurring in-person presence (monthly or quarterly) if your operating model requires it.
Productized services run on locked timelines, so pausing rarely makes sense — the scope is sized to a 3-6 week sprint. For larger engagements (Growth, Enterprise), we can pause for a defined period (typically up to 30 days) at no cost — you tell us by Friday, we close out the current sprint, and we resume cleanly when you're ready. Longer pauses convert to a paused-engagement state with reduced fees but require renegotiation of restart pricing since team allocation has to be rebuilt. We do not pause Fractional AI CTO retainers without 30 days' notice per the retainer terms.
Welcomed and encouraged. We offer "pair-build" engagements where your engineers are embedded with our team for skill transfer — particularly useful when your team will own the system long-term and you want them to come out of the engagement able to build the next one themselves. Pair-build doesn't add cost; it changes the engagement structure. Your engineers join sprint planning, get code review on their contributions, and finish the engagement having shipped alongside us. Especially common for clients pairing Studios builds with Deep Learn Institute training for their team.
Client data is treated under Studios Confidentiality and Client Data terms (Terms §10 and §5 of Privacy Policy). We restrict access to the Studios team members who need it, apply technical safeguards appropriate to its sensitivity, and return or delete it at engagement end unless agreed otherwise. We do not use client data to train our models or improve services for other clients without explicit permission. For regulated data (PHI under HIPAA, financial data under GLBA, student records under FERPA), we execute appropriate BAAs, DPAs, or equivalent before any sensitive data flows to us. Engineering bench access is logged and auditable.
Three paths post-support-window. (1) Your team operates the system independently using the runbook and training video — many SMB clients do exactly this. (2) Pay-as-you-go: hourly support at $250/hr when you need it, no minimum. (3) Studios Retainer ($5K-$25K/month, 6-12 month minimum) if you want ongoing optimization, new features, or dedicated engineer hours. About 40% of Growth and Enterprise clients convert to retainer within 90 days of launch — usually after they've seen what they could build next once the first system is operational.
During the eval-design phase (stage 02) of every engagement. The decision is driven by your specific use case — latency budget, accuracy floor, cost ceiling, data sensitivity, integration constraints. We default to Claude for most agent work because tool-use quality leads the field and Anthropic's enterprise terms align with our data commitments. We default to OpenAI for some high-volume consumer cases where cost dominates. We use open-source (Llama, Mistral) for data-sovereignty cases. We have no reseller relationship with any model provider — there's no kickback steering our recommendations. We tell you which model we're recommending and why, and we change it without ego when the eval set proves a different choice is better.

Build with the team that
ships every Friday.

Tell us what you're trying to build. We'll respond within 48 hours with a recommended path and an SOW you can sign.