Cuemby AI Labs
From AI strategy to AI in production, with one team accountable for the result.
We find where AI pays off, build it with senior engineers, and run it after launch. A flat-fee assessment, then fixed-price milestones.
- MVP ScopingAssessment$2,500Credited
- Crew SetupLarge$3,750Accepted
- Onboarding and accountsLarge$3,750Accepted
- AI assistantLarge$3,750In progress
- BillingMedium$1,750Planned
- Launch OpsMedium / month$1,750Monthly
- 10+
- AI projects delivered
- 100%
- Accepted by the client
- 120+
- Technical assessments
- 11 years
- Building and operating software
How it works
One engagement in 3 phases, with the same senior team from start to finish
You can enter at any phase. You always see where you are and what comes next.
Evaluate
What to build, what it will cost, what stands in the way, and in what order. Every recommendation is tied to cost, revenue, hours, or risk.
Next: Implement
What you get
- Readiness of your team, data, and systems
- Opportunities ranked by business impact
- Risks and what it takes to remove them
- A priced milestone plan for Implement and Operate
Implement
Senior engineers build what the evaluation prioritized, in 2-week sprints with a demo every sprint. Your team learns to own it as we go.
Next: Operate
What you get
- Working software, in production
- Evaluation sets and cost tracking for AI features
- The delivery setup installed in your GitHub
- Knowledge transfer and a handover sprint
Operate
We run what we built, or take over what already exists: monitoring, quality, cost, and model changes, with a steering committee that keeps the roadmap moving.
Feeds the next evaluation
What you get
- A defined monthly milestone of improvements
- Monitoring, incidents, and model updates
- AI quality and cost reported every month
- Steering committee on metrics and roadmap
Where you start
Pick the situation that sounds like yours
Same 3 phases for every company. The goal, team size, and price change.
Build and launch the MVP
A production MVP in about 8 to 12 weeks, and a technical partner after launch.
Pre-seed to Series A, usually 1 to 30 people. Buyer: founder or CEO.
Startup Crew: 3 seats, 1 to 3 people, with a fractional CTO approving design and code.
What you worry about
- Spending the round on a build that misses the market
- Picking the wrong stack, model, or vendor
- AI cost per user that breaks the unit economics
- Nobody technical to own the product after launch
MVP Scoping
About 2 weeks- What the MVP must prove, and what can wait
- Feasibility of the AI features
- Architecture, stack, and model choices
- Cost per user for models and infrastructure
You get: MVP spec, architecture, cost model, priced milestone plan
$2,500 flat fee
MVP Build
About 8 to 12 weeks- 2-week sprints with a demo every sprint
- Auth, security basics, analytics, CI/CD from day 1
- AI features with evaluation sets and cost tracking
- Launch support: first users, investor demo
You get: A production MVP
$900 / $1,750 / $3,750 per milestone, S / M / L
Launch Ops + fractional CTO
Monthly- Hosting, monitoring, fixes, model and cost management
- A monthly milestone of improvements from user feedback
- Fractional CTO for roadmap and investor questions
- Help hiring your first engineers, then handover
You get: Default: 1 Medium milestone per month
monthly, same milestone prices
Take the software to the next level and ship faster
Faster releases, AI in your product, and a platform ready for the next stage of growth.
Series A to C or profitable growth, usually 30 to 500 people. Buyer: CEO, CTO, or VP Engineering.
Scale Crew: 5+ seats with a Delivery lead, working next to your team.
What you worry about
- Release speed dropping as the team and codebase grow
- A platform that won’t hold the next 10x in users or data
- Competitors shipping AI features first
- Cloud and AI bills growing faster than revenue
Scale Assessment
About 3 to 4 weeks- TechScore benchmark of architecture, code, security, and operations
- People, Process, Technology, Product review
- Baseline: release frequency, lead time, incidents
- AI opportunities ranked by customer and revenue impact
You get: Executive report, 90-day and 12-month roadmap, business case per priority, priced milestones
$7,500 flat fee
Scale Crew
By milestone- AI features and agents inside your product
- Platform work: cloud, Kubernetes, data, performance
- AI-assisted workflows for your engineers
- Tech debt reduction tied to delivery metrics
You get: Your roadmap, delivered in fixed-price milestones
$1,350 / $2,625 / $5,625 per milestone, S / M / L
Managed Platform and AI Ops
Monthly- Monitoring, SRE, incident response, patching
- AI quality evaluations, model updates, cost control
- Maintenance or full takeover of parts of the product
- Monthly steering on metrics, costs, and roadmap
You get: Recurring monthly milestones
monthly, same milestone prices
Adopt AI across software development
AI adoption your security team approves, with productivity measured against a baseline.
500+ people with internal engineering or IT teams. Buyer: CIO, CTO, or VP Engineering.
Enterprise Crew: 6 to 10 seats with a Delivery lead.
What you worry about
- Security, IP, and compliance exposure from uncontrolled AI tool use
- Paying for licenses with no evidence of productivity gains
- Uneven adoption: a few teams use AI well, most don’t
- Legacy codebases that seem too old for AI tools
AI Engineering Assessment
About 4 to 6 weeks- Delivery baseline: release frequency, lead time, change failure rate, time to restore
- SDLC, tooling, and codebase readiness, legacy included
- Security, compliance, and data policies for AI
- Pilot team and use case selection
You get: Executive report, AI policy draft, pilot plan, 12-month rollout roadmap, priced milestones
$15,000 flat fee
Pilot, then Rollout
Pilot about 8 to 12 weeks, then waves- 2 or 3 teams on an Enterprise Crew, measured against the baseline
- Guardrails and agent deny rules from day 1
- Model access gateway, usage tracking, standard workflows
- Training by role, internal champions, playbooks
You get: Governed adoption, measured team by team
$1,800 / $3,500 / $7,500 per milestone, S / M / L
AI Engineering Operations
Monthly- License, usage, and cost management across providers
- Productivity and quality against the baseline, monthly
- Policy updates, audits, security reviews
- Quarterly steering with engineering leadership
You get: Recurring monthly milestones
monthly, same milestone prices
AI system in production and nobody owns it?
The vendor left, or the team that built it moved on. We start with a Takeover Review of the code, architecture, cost, and quality, then a stabilization plan. After that, we operate it.
- Code and architecture review
- What it costs per month, and where the money goes
- Quality and failure points
- Stabilization plan, priced as milestones
An AI-first startup in fashion resale
The company is building a platform that helps secondhand clothing resellers identify, price, and list inventory faster. We joined to build the MVP on a fixed-price plan: 4 Large milestones, $15,000.
One senior Cuemby engineer holds every seat on the crew, with a fractional CTO approving design and code. The founder approves stories and accepts each milestone.
- 2 of 4
- Milestones delivered and accepted
- 4 weeks
- 1 milestone per sprint, on schedule
- 1 engineer
- In every seat, plus a fractional CTO
How we deliver: Cuemby Crew
Senior engineers working with AI agents. A person approves every step.
Each role is a seat held by a named senior person, working with AI agents set up for that role. Everything runs on a board in your own GitHub.
- InboxIdea from steering
- RefinedAgent drafts the storyProduct Owner approves
- DesignedAgent proposes the designArchitect approves
- ReadyMeets Definition of Ready
- In ProgressAgent writes code and tests
- In ReviewEngineer reviews and merges
- QAAgent runs the test planQA signs off
- DoneMeets Definition of Done
A small crew at the pace of a larger team
Product Owner, Architect, Engineer, and QA seats, each held by a senior person with agents set up for that role.
You see everything
Every milestone, story, and approval sits on GitHub Projects in your organization, with a demo every sprint.
Your team takes seats over time
Your engineers can hold seats next to ours. Knowledge transfer is part of every milestone.
It keeps working after we leave
The setup, automations, and handbook stay in your organization, licensed for internal use. The last build milestone is a handover sprint your team runs without us.
Why Cuemby
The team that evaluates it builds it and runs it
AI projects tend to break at the handoffs: strategy to build, build to operations. We keep all 3 phases with one team.
Evidence first
Every engagement starts with an evaluation, the same method we use for technical due diligence for investors.
One accountable team
The senior people who evaluate it build it and run it. Nothing gets lost between vendors.
Built for production
Monitoring, cost, quality, and model changes are planned from day 1. Assessments start within 48 hours of signing.
Your team owns it
Knowledge transfer in every phase, senior delivery in English and Spanish, and the whole setup stays in your GitHub.
Where we fit best
Pricing
A flat fee for the assessment, then fixed-price milestones
The assessment produces a plan where every milestone is defined before you buy it: scope, what’s out of scope, deliverables, acceptance criteria, and what we need from you. No hourly rates.
Small
About ½ week
One focused change: an integration, a feature tweak, a report, a policy draft.
$900 for startups
Medium
1 week
A complete feature or workflow.
$1,750 for startups
Large
2 weeks, 1 sprint
A major feature, an agent, a platform component. Bigger work is split.
$3,750 for startups
Questions
Straight answers
We just need developers to build it.
The scoping takes 2 weeks for a startup and gives you a fixed price for the whole build. If you sign the first milestones within 30 days of the readout, the assessment fee is credited toward them.
An assessment sounds slow.
2 weeks for startups, 3 to 4 for scaleups, 4 to 6 for enterprises. It tells you which work pays back first, so the build starts on the right thing.
Will AI write our code without supervision?
No. Agents draft; a named person approves every story, design, pull request, and test result. Agents can’t merge or deploy, and every approval is recorded in GitHub.
What happens when you leave?
The crew runs in your GitHub organization from day 1. The last build milestone is a handover sprint your team runs without us, and the handbook and automations stay in place.
We already bought the AI tools.
Then the assessment measures whether they’re working and makes adoption consistent across teams, with productivity reported against a baseline.
Security won’t approve this.
Policies and guardrails come first, in the assessment and in the pilot. You choose the model provider, and your data policies apply to it.
Big consultancies already pitched us.
Senior engineers do the work, and the same team stays to operate what it builds.
We’ll hire our own team after launch.
Good. Launch Ops includes help hiring your first engineers and a handover plan.
What if we need something outside the plan?
It becomes a new milestone, sized and priced the same way. If a milestone is blocked waiting on something from your side, the timeline moves and the price stays the same.
What we don’t do
- Sell AI tools or licenses as the main offer
- Build demos with no path to production
- Hand over a strategy deck and leave
Free strategy session
30 minutes. We’ll tell you where we’d start and what it would cost.
- Where AI pays off first in your business
- Which phase and company stage fit you
- A first estimate, using the price list on this page
We reply within 1 business day with times to meet.
Prefer email? elsa@cuemby.com
We’ll email you within 1 business day with times to meet. To add context before then, write to elsa@cuemby.com.