Validate Before You Build with Rapid Prototyping
We turn your riskiest assumptions into working prototypes in 2–4 weeks — so you invest in ideas that have been validated, not just discussed.
Trusted by innovative teams worldwide
Rapid Builders With Enterprise Credentials
Our prototyping team combines startup speed with enterprise-grade engineering practices.
From Concept to Working Demo in Weeks, Not Months
We prototype with production-quality thinking — so when you say "go," the code doesn't have to be thrown away.
Technical Proof of Concept
We build working POCs that prove technical feasibility — API integrations, algorithm performance, data pipeline throughput — with real data and measurable results.
Interactive Prototypes
Clickable, functional prototypes with real user flows — not static mockups. We use Next.js, React Native, or Flutter to build prototypes that feel like the real product.
AI/ML Model Validation
Rapid prototyping of machine learning models — data preprocessing, model training, accuracy benchmarking, and demo interfaces to validate AI-driven feature ideas.
Data Pipeline Prototypes
End-to-end data flow prototypes proving that your data sources can feed the analytics, ML models, or reports your product depends on.
Integration Feasibility
Third-party API integration prototypes — proving that partner systems, banking APIs, payment gateways, or data providers work the way your product needs them to.
Mobile & Cross-Platform POCs
Rapid prototypes for mobile apps, embedded systems, or cross-platform solutions — demonstrating feasibility across devices and operating systems.
Got an Idea? Let's Prove It Works.
Book a free prototype scoping session — we'll tell you what's feasible and how fast.
Stop debating feasibility. Start proving it.
The average failed product wastes 9 months and $500K on untested assumptions. Our prototypes give you real answers in weeks.
Prototyping That's Actually Useful
Our prototypes aren't throwaway demos. They're built with production-aware thinking — clean code, real integrations, and documentation — so the path from POC to product is smooth.
Why Teams Choose Us for Prototyping
We build prototypes that answer real questions — not demos that impress but teach you nothing.
Describe Your Idea or Hypothesis
Tell us what you want to validate and we'll outline a prototype plan within 48 hours.
Our Engagement Process
Idea Clarification
We refine your hypothesis, define success criteria, and scope the minimum viable prototype to answer your key questions.
Architecture Spike
Quick technical investigation of the riskiest assumptions — API availability, data quality, algorithm feasibility.
Rapid Build Sprint
2–4 week time-boxed build with daily check-ins and mid-sprint demos. Working prototype, not wireframes.
Validation & Benchmarking
Performance testing, user testing (if applicable), and comprehensive feasibility report with data.
Go/No-Go Decision
Presentation of findings, recommendations, and production evolution plan if the prototype validates.
What Our Clients Say
“We had debated building a real-time fraud detection feature for six months. OpenMalo prototyped it in three weeks using our actual transaction data. The results were so strong we greenlit full development the same day.
“OpenMalo built us an integration prototype with three different banking APIs in under two weeks. It saved us from choosing the wrong payment provider — one of them had latency issues that only showed up under real conditions.
“Their AI prototype for our claims triage system proved the concept could achieve 89% accuracy with our existing data. That prototype became the foundation of a product feature now used by 40,000 adjusters daily.
From Idea to Production in 11 Weeks — Starting With a 3-Week POC
AI Claims Triage Prototype for InsurTech Labs
How a 3-week AI prototype validated a claims triage concept with 89% accuracy — leading to full production deployment now serving 40,000 insurance adjusters.
An AI idea with promising potential but no validation
InsurTech Labs believed ML could automate initial claims triage, but had no evidence the concept would work with their data quality, claim complexity, or accuracy requirements.
Our Approach: Week 1: Data extraction and labeling sprint across all 5 source systems. Week 2: Model training with three different approaches (rule-based, gradient boosting, fine-tuned LLM). Week 3: Accuracy benchmarking, demo interface, and stakeholder presentation with live data.
Read Full Case StudyFrequently Asked Questions
A POC (proof of concept) validates technical feasibility — can this algorithm achieve target accuracy? Can this API handle our throughput? A prototype validates the user experience and business workflow — does this product concept actually solve the problem? We build both.
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