Proof of Concept

Get It Right. Measure Twice, Cut Once.

Don’t get caught up in the hype. Quickly qualify an idea for your solution by testing hypotheses in practice.

About

Expert research or light prototyping to validate the feasibility of your solution for a specific business problem.

Discovering how effectively AI solves your business problem based on early testers’ usage insights, laying the groundwork for your AI approach.

Receive a detailed report that includes performance results, tuning approaches, architectural/design decisions, and operational cost projections.

When to Consider
Proof of Concept?

To determine if a solution approach is the best option for your needs

Process

Discovery

Scoping

Goal-setting

Tech selection

Prototyping

Optimizing

Testing & validation

Iterating

Reporting

Discovery

Identifying specific business challenges or pain points that AI can address.

Who's Involved?

How they contribute

Building the POC

Handles the technical design, construction, and testing of the prototype to assess whether the AI solution satisfies the defined business goals on a small scale.

Your Benefits

Risk reduction

Strategic clarity

Product innovation

Fast and cost effective go/no-go decision on further development.

Technologies, Frameworks & Tools

PythonTypeScriptMCPLangChainPGVectorMLFlowJupyterNotebooksPEFTLoRASIMBAGEPAMiPROv2

Have an idea but not sure it's worth building?

FAQ

An AI POC is a small-scale experiment with limited scope, designed to test the hypothesis and feasibility of an AI solution for a particular business problem before full deployment.

Previous Service

Enterprise Application Modernization

Next Service

Minimum Viable Product

Get It Right. Measure Twice, Cut Once.

Process

Who's Involved?

Your Benefits

Technologies, Frameworks & Tools

What is an AI Proof of Concept (POC)?

What information do we need to provide to get started with an AI POC?

When is the best time to consider an AI POC?

What happens if the POC shows the AI approach isn't viable?

Our team has limited AI expertise. Is this a problem when testing an AI POC?

How does having an AI POC help justify further AI investment to stakeholders?

What is the outcome at the end of the AI POC?

How do you ensure the AI solution fits a specific industry or domain?

Can I be involved throughout the process of creating an AI POC?

Does the AI POC include building a working prototype or just providing recommendations?

Do you work with existing AI models or build custom ones?

Do you help with scaling the AI solution after the POC?

What if we want to modify the approach based on POC results?