G2

Simran B.

Technology, AI & R&D Associate

Computer Software

Mid-Market (51-1000 emp.)

11/7/2026

"Quickbase Streamlines AI PoC Tracking with Customizable, Stakeholder-Friendly Apps"

4/5

What do you like best about Quickbase?

From my perspective, I find the most interesting aspect of Quickbase to be its ability to structure the journey from an initial idea to a validated proof of concept (PoC). In AI experimentation, the challenge often lies not just in building a model, but in maintaining the connections between various approaches, prompt versions, evaluation results, feedback, and implementation decisions.

With the help of Quickbase’s customizable applications and relational data structure, one can design an innovation-tracking environment where individual experiments are linked to their specific objectives, expected outcomes, testing status, and evaluation feedback. I particularly appreciate its potential to allow both technical and non-technical stakeholders to understand the innovation pipeline, without having to rely constantly on developers for every minor update. Review collected by and hosted on G2.com.

What do you dislike about Quickbase?

From my perspective, there are certain limitations to consider when using Quickbase for AI-focused R&D workflows. While the platform can be useful for organizing the experimentation lifecycle, specialized technical tools will still be required for actual model testing, complex statistical evaluation, and advanced AI experimentation.

Another improvement I would like to see is greater flexibility in integrating AI evaluation pipelines, specifically regarding automated result ingestion, comparisons between experiment versions, and the collection of structured feedback. If the setup and configuration in these areas were made more intuitive, end-to-end innovation tracking could become even more efficient for technology teams. Review collected by and hosted on G2.com.

What problems is Quickbase solving and how is that benefiting you?

In technology, AI, and R&D, the traceability of experimental decision-making is a relatively overlooked challenge. For instance, when multiple AI approaches, automation concepts, or technical solutions are evaluated for a business problem, it can later become difficult to determine which approach was tested, what the results were, what limitations were identified, and why a specific decision was ultimately made for implementation.

Quickbase can serve as a structured AI innovation evaluation hub to address this gap. It allows individual Proofs of Concept (PoCs) to be linked with their objectives, experimental versions, evaluation criteria, human feedback, estimated feasibility, and final recommendations.

The benefit here is that R&D efforts are not confined merely to the experiments themselves; the insights gained from them remain valuable for future technology decisions. To me, this is the true value of Quickbase: transforming ideas into structured technical evidence and making the journey from experimentation to practical implementation more transparent. Review collected by and hosted on G2.com.