In two weeks, your team evaluates real models against real work. You walk away with a tested recommendation and a deployment plan. We handle the model curation and the environment.
If you're considering on-prem AI for your team, you're navigating two questions with not much data to go on. Will an open-weight model meet your quality standards on your actual work? And what hardware does that model need to run well for a team your size?
These are hard questions to answer without real equipment and real testing. Most teams guess, overbuy, or do nothing. The ones that try to answer it themselves typically 2-3 months of part-time evaluation and still end up without hardware benchmarks. The POC answers the same questions in two weeks.
Before the PoC starts, we narrow the universe of open-weight models down to 2-4 candidates that are realistic for your use case, your budget, and the hardware footprint that would make sense for your team. We then stand up a chat interface and coding assistant, point it at those candidates, and hand it to your team for two weeks to try against their actual work.
We take the top candidate from Phase A, run it on dedicated GPUs that match the hardware we'd recommend, and measure throughput, latency, and concurrent capacity under realistic load. This is where we answer what hardware is actually required to run the model for your team.
Everything produced during the PoC is yours, whether you proceed to deployment or not.
A working evaluation environment your team can use for two weeks. Chat interface and coding assistant, pointed at candidate models we've pre-selected for your use case and budget. Your team tests against their actual work, not demos.
A written report covering the candidate models, measured performance benchmarks, hardware recommendations, and a deployment plan.
A 60-minute recommendation call to walk through every finding and answer any questions your team has before committing to anything.
A formal install proposal if you decide to proceed.
Every PoC ends in one of four outcomes, based on your team's evaluation and the benchmark data.
Your team finds a candidate model that meets their expectations. We have the hardware spec and benchmarks ready.
The models work for some of your use cases but not all. Your team and ours map out which parts of the workflow are a fit and which aren't.
None of the candidates quite meet your expectations, but based on AI advancements, the trajectory suggests revisiting in 3-6 months.
Your use case needs capabilities only the frontier closed models offer today. Better to know now than after buying hardware.
100% of the PoC fee credits toward the install if you sign within 60 days. If you don't proceed, you still walk away with a tested recommendation and a hardware plan before spending anything on infrastructure.
You have a specific use case for AI, whether that's coding assistance, document processing, an internal knowledge base, or something similar.
Your customers, compliance posture, or contracts make sending code or data to third-party AI services complicated.
You're seriously considering on-prem or private-cloud AI, not just exploring whether AI is worth using.
You're unsure whether current open-weight models are good enough for what you need.
Tell us what you're working on and we'll send a scoped proposal with pricing, timeline, and specifics.
Request a proposal