Which ideas suit a four-week prototype?

Focused use cases with a clear user and a measurable outcome, such as document search, summarization, recommendation, classification, and conversational assistants over your own data.

Can you prototype on our private documents?

Yes. We build retrieval pipelines over your documents inside a sandbox, with access controls so sensitive content stays within your environment.

What if the idea does not work?

That is a valid result. Failing fast on a small budget is the point. You get evidence on what did not work and why, so the next decision rests on facts.

Do you only build LLM-based prototypes?

No. We prototype with language models, classical machine learning, computer vision, and speech. The model is chosen by your performance, cost, and privacy needs.

Which industries have you prototyped for?

Healthcare and life sciences, legal, retail and e-commerce, facility management, financial services, logistics, and manufacturing.

What happens each week?

Week one is discovery and data. Week two is the pipeline build. Week three is UI and integration. Week four is the demo and the production roadmap.

Which tools do you use to move fast?

Accelerators such as Streamlit, Flowise, and the Vercel AI SDK for interfaces and chains, plus frameworks like LangChain and a vector database for retrieval.

Which foundation models can you use?

We select the best fit on performance, cost, and privacy, including OpenAI, Mistral, Meta's LLaMA, and others, and can switch models during the sprint if results call for it.

How involved does our team need to be?

Light but regular: a product owner for decisions, access to data and a few end users for feedback. We run short check-ins through the sprint.

Can the prototype connect to our systems?

Yes, to a sandbox version of your systems through secured APIs, so internal testers see realistic behavior without touching production.

How do you measure whether the prototype works?

We agree metrics at the start, such as answer accuracy, time saved, or conversion uplift, then test with real users and report results against them.

How do you save budget on validation?

By limiting scope, reusing proven components, and testing the riskiest assumption first. Many teams spend a fraction of a full build to learn whether to proceed.

Is our data safe during the sprint?

Prototypes run in a controlled sandbox with role-based access. Security and privacy practices are agreed up front and aligned to your requirements.

Will the prototype be production-ready?

Not by design. It is built to learn quickly. The roadmap at the end shows what hardening, scaling, and governance are needed to go live.

Do you test for hallucinations and bias?

Yes. We evaluate answers against source documents, show citations, and review outputs for errors and bias before the demo.

What happens after the demo?

You choose: proceed to production, iterate with another sprint, or stop with clear evidence. We provide the roadmap, estimates, and architecture for each path.

Can you take the prototype to production?

Yes. The same team can build, integrate, monitor, and scale it, or hand over the code and documentation to your engineers.

Who owns the prototype and code?

You do. The code, prompts, and documentation produced in the sprint are handed over at the end.

Can you support us after launch?

Yes. We offer managed support covering monitoring, retraining, and new use cases, as well as embedded engineers for your team.

Where do I start a conversation?

Use any Book a Sprint Call or Let's Discuss Your Project button and we will respond with a scoped proposal.

Speed To Value

AI Rapid Prototyping: From Idea To MVP In 4 Weeks.

Validate your AI concepts before investing millions. We build functional, testable prototypes using agile methodologies to prove ROI fast.

Book a Sprint Call See the 4-week sprint

Aarvexa rapid prototyping analysis

0 wksIdea to working MVP

0%Budget saved on validation

0%Validated accuracy in a sprint

4-WEEK SPRINTDAY 1 / 28

W1Discovery & Data

W2Pipeline Build

W3UI & Integration

W4Demo & Roadmap

prototype.aarvexa.appCLINICAL TRIAL SEARCH

Protocol_v3.pdf · p.12

Ask anything…

TIME TO INSIGHT

Traditional dev6-9 months

Aarvexa sprint4 weeks

VALIDATION

search accuracy

SPRINT OUTCOMEbuilding…

search accuracy92%

budget saved80%

users tested12

Idea to MVP in 4 weeks

Prototypes built across industries, from first idea to MVP

Healthcare & Life SciencesFinancial ServicesRetail & E-commerceLegal & ComplianceManufacturingLogistics & Supply ChainReal Estate & FacilitiesEnergy & UtilitiesInsuranceEducationHealthcare & Life SciencesFinancial ServicesRetail & E-commerceLegal & ComplianceManufacturingLogistics & Supply ChainReal Estate & FacilitiesEnergy & UtilitiesInsuranceEducation

Why prototype

Fail Fast, Scale Faster.

The biggest risk in AI isn't the technology, it's building the wrong thing. A rapid prototype proves value before you commit to a multi-million dollar development cycle.

Test the riskiest assumption first, not the easiest feature.
Spend a fraction of a full build to learn if the idea earns its keep.
Walk into the budget meeting with a working demo, not a slide deck.

TIME TO INSIGHT

Traditional development6-9 months

Aarvexa rapid prototype4 weeks

0%budget saved on validation

0 wksto a working MVP

Book a Sprint Call

The process

The 4-Week Sprint

A fixed-scope, fixed-time sprint. Every week ends with something you can see, test, and react to.

Let's Discuss Your Project

Week 1
Discovery & Data
We define the use case, audit available data, and select the best-fit foundation model based on your performance, cost, and privacy requirements, whether that's OpenAI, Mistral, Meta's LLaMA, or others.
Data AuditTech Selection
Week 2
Pipeline Build
Engineers build the RAG pipeline, set up the vector database, and perform initial prompt engineering tests.
Vector DB SetupLangChain
Week 3
UI & Integration
We wrap the logic in a simple UI (Streamlit/Vercel) and connect it to your sandbox environment for internal testing.
FrontendAPI Connection
Week 4
Demo & Roadmap
Executive demo day. We present the working prototype, gather user feedback, and outline the path to production.
MVP Delivery

Our toolkit

Built With Modern Accelerators

We leverage low-code and rapid application development tools to focus on logic, not boilerplate.

Streamlit

Instant Data UIs

Turn a Python script into a clickable app in hours, so stakeholders can use the idea, not just read about it.

Flowise

Drag & Drop LLM Chains

Wire up prompts, retrievers, and tools visually and change the logic live during a review session.

Vercel AI SDK

React Streaming

Streaming chat and generative UI that feel like a finished product from the first demo.

Replit

Collaborative Coding

A shared cloud workspace where your team and ours build and test side by side, with nothing to install.

Results

Prototyping Wins

Real results from rapid experimentation.

Clinical Trial Search

92%

Challenge: Doctors spent hours searching PDF protocols.
Prototype: A RAG-based chat interface.
Result: Validated search accuracy of 92% in 4 weeks.

Contract Summarizer

−70%

Challenge: Paralegals drowning in paperwork.
Prototype: An upload-and-summarize tool.
Result: Reduced review time by 70%, greenlit for full dev.

Shopping Assistant

+15%

Challenge: Low conversion on an e-commerce site.
Prototype: A personalized recommendation bot.
Result: 15% uplift in conversion during A/B testing.

Maintenance Request Classifier

65%

Challenge: Facility teams drowning in daily work order backlogs.
Prototype: NLP-based auto-classification and priority-routing system.
Result: 65% faster triage, live across 3 enterprise clients in 4 weeks.

Got an idea?

Don't let it die in a slide deck. Let's build it.

Bring the idea, the data, or just the problem. In four weeks you will have a working prototype and a clear go or no-go.

Book Your Sprint

FAQ

Frequently Asked Questions

Straight answers on AI rapid prototyping: the sprint, scope, data, validation, and what happens next.

AI rapid prototyping is a fixed-time sprint, typically four weeks, that turns an AI idea into a working, testable prototype. It lets you validate accuracy, user value, and ROI before committing to a full development budget.

A proof of concept shows something is technically possible. A prototype adds a usable interface and real data so people can test it. Our four-week sprint ends in an MVP-style demo your team can try and react to.

A working prototype connected to a sandbox, an executive demo, user feedback, measured results against agreed metrics, and a roadmap with effort and cost estimates for taking it to production.

No. The first week includes a data audit. We work with what you have, flag gaps, and use sample or synthetic data where needed so the sprint is not blocked.

Book a sprint call. We discuss the idea, the data, and the success criteria, then propose a scoped four-week plan.