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Claude-Powered Product Launch
A customer-facing feature/product(small) built on Claude, live for an agreed group of your users, with its prompts and evaluation set in your repository, guardrails and cost limits in place, and monitoring of quality and cost per user.
Claude-Powered Product Launch
$35K–$60K
- Duration: 4 weeks
- Tier: Launch
- Format: Fixed scope
You leave withClaude-powered feature shipped to the client's customers
Book a Free SessionSound familiar?
Our customers keep asking when we'll have AI in the product, and a competitor just launched something. We built a prototype on Claude at a hackathon and it looked great. But we don't know how to make it safe to put in front of thousands of customers, or what it'll cost us per user.
What you leave with
Feature code, prompts and tool definitions
Format: In your repo
How we know it’s acceptablePasses your code review and deploys through your pipeline
Evaluation set and baseline
Format: In your repo
How we know it’s acceptableYour team re-runs it without us
Guardrail configuration
Format: In your repo
How we know it’s acceptableAdversarial test cases are blocked or handled as agreed
Cost and quality dashboard
Format: Your tooling
How we know it’s acceptableShows cost per active user and quality scores
Rollout plan
Format: Document
How we know it’s acceptableAgreed with the product owner, with the numbers that trigger full release
Runbook
Format: In your repo
How we know it’s acceptableYour engineer changes a prompt, re-runs the evaluation and redeploys during handover
Also included
- The feature live in production behind a feature flag, released to an agreed beta group
- Cost controls: caching, choice of model per task, per-customer usage limits
How it runs
4 weeks
Week 1
Feature spec, definition of a wrong answer, evaluation set from real user questions, start of security and data review.
OutOne-page spec and evaluation set.
Week 2
Build on Claude with tools and retrieval; tracing in place.
OutWorking feature in staging.
Week 3
Guardrails, cost controls, evaluation and adversarial testing, security sign-off.
OutBaseline scores and cost per user.
Week 4
Release to the beta group behind a flag, monitoring, handover.
OutLive feature, runbook, paired handover.
Is this right for you?
This is for you if
- Buyerthe person who owns the product roadmap: a CPO, a CTO or a technical founder at a software or digital product company.
- Also has to say yeswhoever signs off security and privacy, because customer data goes to a model provider; legal, for customer-facing AI terms and disclosures; Finance, for the ongoing model cost.
When this isn't the right fit
- You are committed to another model provider, or need the feature to be model-agnostic. The AI Feature Sprint fits better.
- The feature is internal only. Nothing ships to customers, so the launch work here is not needed. The AI Feature Sprint fits better.
- Your data cannot reach a third-party model, and no approved cloud route exists. The Private AI Deployment Sprint fits better.
- The idea is not validated. If you cannot say which users want it and what success looks like, start with the AI Opportunity Sprint.
What’s included, and what isn’t
In scope
- one feature
- one product surface
- up to one data source for retrieval
- release to an agreed beta group
- guardrails
- evaluation
- monitoring
Out of scope
- redesigning the product interface beyond the feature
- other features
- fine-tuning
- support for full rollout after week 4 (unless priced)
- model API spend, which you pay the provider directly
- drafting your AI terms of use
Before we start
Before day one
- Claude access in your own account (direct, Bedrock or Vertex) with data terms approved.
- Repository and staging access.
- A way to release features behind a flag.
- A named product owner.
- A security reviewer booked for weeks 1 and 3.
What we’ll need from your team
- A product manager: three hours a week.
- An engineer from your team: four hours a week for pairing and code review.
- Security and legal: about three hours each in weeks 1 and 3.
- A designer: two hours a week if the feature has new interface.
Pricing
What we need to give you a quote
- A description of the feature and its users.
- Expected request volume.
- What data it uses.
- Your cloud and hosting setup.
- Who owns the front end.
- Compliance requirements and who signs off.
After we hand over
Who owns it after we leave
The product owner owns quality and the rollout decision. A named engineer owns the prompts and the evaluation set. Someone has to re-run the evaluation whenever a new model version is released, and that is a recurring task worth naming at handover.
How success is measured
- Share of active users who use the feature
- Task success or user rating
- Cost per active user
- Support tickets avoided where relevant
Agreed in week 1 and reported at handover.
What comes next
- Monthly · $10K–$22K a monthManaged Services Growthwhich covers model upgrades and continuous evaluation, is the natural fit.
- Further features on the same foundation.
Questions buyers ask us
If you're already committed elsewhere, we'll build on that instead through the AI Feature Sprint. We go deep on one stack, so we know its tooling, caching and safety controls well, and it's available through AWS and Google Cloud if you need it there.
They can. What takes the time is the evaluation set, the guardrails and knowing your cost per user before launch, not the API call.
We set per-customer limits and measure cost per active user in week 3, before anything reaches customers.
We use the deployment route and data terms your security team approves, and we start that review on day one.
Fair, it is. Four weeks gets one feature to a beta group on an existing product. Full rollout follows once the beta numbers hold.