Every six months, a new frontier model crosses a benchmark nobody thought it would cross this year. Every six months, the median enterprise AI project still fails to land.
If you work anywhere near real enterprise deployments, you already know the pattern. The demo is astonishing. The pilot is promising. The pilot does not reach production, or it reaches production and nobody uses it. A different vendor shows up with a different demo six weeks later. The cycle continues.
I used to think the problem was model quality. It isn’t, and it hasn’t been for about a year.
The bottleneck moved. It is no longer what the model can do. It is what the model does not know — about your company, your team, your clients, your specific piece of work. And there is no prompt long enough to fix that.
I watched a financial-services team spend four months on a reconciliation copilot. The model could reason through the accounting; that part was trivial. What it could not do was know which of three counterparties with slightly different spellings was the same entity, which reconciliation exceptions the CFO had learned to wave through in Q4, which ledger mapped to which product line under the new org chart. Those four things are what a tenured analyst learns in their first year. They are not written down anywhere the model can read. That is context.
Context is not retrieval. Retrieval answers “find me something like this.” Context answers “what should this agent know before it writes a word?” Those are different problems.
Context is not a prompt, either. A prompt is the smallest possible piece of context — a few thousand words, stale the moment something changes, incapable of describing who the user is or which client they are writing to. Long prompts are a workaround for the absence of structure, not a solution.
Context, as I mean it here, is a model of the team. It has a schema. It has owners. It has a review cadence. It has an authority level per entry and a freshness stamp per field. It is read by agents, edited by humans, and audited by anyone who needs to know why an agent said what it said. It is infrastructure, not a prompt.
This is the bet I am making with Sempleo. The team-context layer is the most important unclaimed position in enterprise software. The company that builds it well becomes the system of record for how teams work with AI — the layer every agent, every copilot, and every internal tool reads from and writes back to. That is a decade of compounding value.
I am not trying to win on models. Models are an implementation detail. I am trying to be right about context — and I am staking the next five years on it.
If the thesis lands for you, I am taking on a small number of founding customers in 2026. I will sit with you through the first pass personally. Applications are open.
