AI & ML prototypes

What this looks like

AI has a hype problem. Most vendors promise transformation and deliver a demo that breaks on real data. We do the opposite: start with your actual data, build a working prototype, and give you an honest assessment of whether it's worth going further.

Sometimes the answer is yes and we help you productionize. Sometimes the answer is "this isn't ready yet" and we save you six figures. Both are valuable outcomes.

What we deliver

• Rapid prototyping using OpenAI, Azure AI, or open-source models
• RAG (retrieval-augmented generation) systems for internal knowledge bases
• Classification, extraction, and summarization pipelines
• Evaluation frameworks so you can measure quality, not just vibes
• Honest feasibility assessments with clear go/no-go recommendations
• Production handover path when the prototype proves out

How we work on this

We start with the use case and the data behind it. Most AI projects fail at the data stage, not the model stage. Then we build a working prototype in short cycles, testing against real inputs. Each week you see what the system can and can't do. At the end you get an honest recommendation: scale it, adjust it, or shelve it.

Who this is for

Teams with a specific AI use case in mind who don't want to commit to a six-month project before knowing if it works. You bring the problem, we bring the prototype.