Your Fast Track to AI Deployment

We specialize in rapidly defining, designing, and developing AI PoCs and MVPs that prove market fit and technology readiness in weeks, not months.

How Can AI PoC & MVP Fast-track Your Project’s Success?

AI projects require early validation to mitigate technology risk and secure stakeholder buy-in. PoC/MVP development ensures your investment is focused, measurable, and strategically aligned with business outcomes.

Early Risk Mitigation

Quickly test core hypotheses about model performance and data availability to identify technical roadblocks and potential failure points before large-scale investment.

Stakeholder Buy-In

A working PoC or MVP provides a tangible demonstration of value, making it easier to secure further funding and support from executive decision-makers.

Market Validation

Launching an MVP allows you to gather real-world usage data and user feedback, ensuring the final product meets actual market needs and enhances product-market fit.

OUR IMPACT

Metrics That Show MVP Development Value

15+

years of driving growth

500+

digital projects delivered

94%

customer satisfaction

The PoC to MVP Pipeline

Ideation & Hypothesis

Define the core business problem, formulate clear AI hypotheses, and establish success criteria and key performance indicators (KPIs) for the PoC/MVP.

Data Readiness Assessment

Analyze the quality, availability, and structure of required data sets. We prepare and pre-process the data necessary for training the initial AI model.

PoC Development & Testing

Our engineers build the minimal technical solution (PoC) to validate the core AI algorithm’s feasibility. This is quickly tested against the defined success criteria.

MVP Definition & Build

Based on successful PoC results, we define and develop the Minimum Viable Product with a functional interface and necessary integrations for real user interaction.

Feedback & Roadmap

We facilitate user testing, gather performance data and feedback, and provide a clear, prioritized roadmap for the full-scale production system development.

Focused PoC & MVP Offerings

Generative AI PoC

Predictive Modeling MVP

Computer Vision PoC

Custom ML Model Prototyping

Data Science Feasibility Study

MVP UI/UX Wireframing

Quick Integration Proof

AI Infrastructure Blueprint

Tools for Rapid Prototyping

Using Python, TensorFlow, PyTorch, and cloud-native serverless environments.

Three Ways to Start Building

Dedicated Team Model

An extended team of AI specialists who integrate into your structure to maintain continuous PoC/MVP development and iteration.

Scalable Development Center

Establish a cost-effective center for long-term AI concept validation, prototyping, and foundational MVP development, managed by Colabrat.

Clearly-Scoped Fixed Price

Ideal for PoCs or MVPs with a clearly defined, limited scope and success criteria, ensuring fast delivery within a predictable budget.

Frequently Asked Questions

Clear your doubts about initiating your AI journey. Find essential answers here regarding the cost, timelines, and necessary preparations for a successful AI PoC and MVP engagement.

A PoC (Proof of Concept) validates a single technical hypothesis. An MVP (Minimum Viable Product) is a functional, deployable product with minimal features to validate market viability.

PoC engagements are typically rapid, lasting between 4 to 8 weeks, depending on data availability and the complexity of the core AI model being tested.

Having access to clean, labeled, and sufficient data is the most critical factor, along with a clear definition of the single, measurable success metric for the validation.

A 'failed' PoC is still successful, as it provides critical learning. We pivot, refine the hypothesis, or recommend stopping investment, saving you significant resources on a non-viable project.

The required amount varies greatly by application, but we conduct a Data Readiness Assessment in Step 2 to determine the minimum viable data set required for initial model training.

The MVP serves as the core foundation. We use the collected user feedback and performance data to create a detailed roadmap for refactoring the architecture, scaling the model, and adding enterprise features.