AI-Native MVP Sprint
Ship a real AI product in weeks, not quarters — a production-grade MVP on a RAG/agent foundation you can raise on and build past.
The problem
Founders need to prove an AI product works before the runway runs out — but a throwaway demo won’t survive real users or diligence, and hiring senior AI engineers takes months you don’t have.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
AI-native architecture
A RAG/agent foundation that’s real product, not a prompt in a wrapper.
Rapid build
A working, production-grade MVP in roughly 4–8 weeks.
Evaluation from day one
Output quality measured, so it holds up with real users.
Cost-aware
Engineered so unit economics work as you scale.
Investor-ready
An architecture and demo that survive technical diligence.
Clean handover
You own the code, docs, and the path to your own team.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- A production-grade AI MVP in weeks
- A foundation you can scale and raise on
- Evaluated output quality, not a fragile demo
- Full ownership and a clean handover
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope AI-Native MVP Sprint?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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