Before you build it out,
confirm it with something that works.

We define “what we need to confirm in order to decide” first. Before starting to build anything that works, we spend a short time putting into words the hypotheses to validate and the yardstick for telling success from failure. Build with this left vague and you end up with “it sort of worked, but we can’t decide,” so this is where we spend the most time at the start.

• Putting the hypotheses to validate into words
• Setting the criteria for success and failure
• Separating out what we won’t validate this time
• Identifying the data and collaborators needed

Good fit: unsure whether to build / vague on what would settle the decision

We quickly implement only the parts needed for validation in a working form. We avoid over-building and narrow to the minimum that lets you decide. With AI-driven development that builds generative AI into everything from organizing requirements to implementation, we compress prototypes that would normally take months into weeks. Where something is meant to be rebuilt in full development, we separate it out and tell you up front.

• Implementing only the features needed to validate
• Speed-first prototyping with AI-driven development
• Building it to accept real data
• Separating throwaway parts from reusable ones

Good fit: need a working demo quickly / requirements not yet settled

Assuming generative AI and LLMs, we quickly test the feasibility of summarization, classification, extraction, dialogue, automation, and more. To avoid “it worked in the demo but the accuracy isn’t there in practice,” we judge where it pays off, including output variance, failure patterns, and the API cost of running it.

• Prototyping summarization, classification, extraction, dialogue, automation
• Validating LLM output accuracy and variance
• Getting a read on prompt and data design
• Rough estimate of running cost (API fees)

Good fit: want to bring generative AI into your work / unsure the accuracy will hold

We have it used with real data and real usage, and measure whether the hypothesis held. Beyond quantitative numbers like usage rate, accuracy, and time saved, we also pick up the qualitative reactions — where users got stuck — and organize it into results you can use for the next decision.

• Measurement with real data and real usage
• Measuring the quantitative (usage rate, accuracy, time saved)
• Collecting the qualitative (reactions, sticking points)
• Judging whether the hypothesis succeeded or failed

Good fit: want to confirm it’s used as expected / want to see the effect in numbers

We organize things so you can decide “proceed, rebuild, or stop” with solid grounds. If you proceed to full development, we spell out the expected scope, a rough estimate, and which parts of the PoC should be rebuilt, and the same team carries straight on into design, implementation, and operations. A conclusion of “stop” is, as an investment decision, a big result too.

• Organizing the proceed / rebuild / stop decision
• Scope and rough estimate for full development
• Spelling out which PoC parts should be rebuilt
• Handover into the design and operations phases

Good fit: want material for an investment decision / want to decide whether to proceed to full development