The case for AI you actually own
Every AI vendor wants your documents in their cloud and a seat-based bill that never stops. We built the opposite — and it turned out to be the easy part.
Most AI products sold to businesses share a shape: your data goes up to their cloud, and a per-seat meter runs against you forever. For a lot of companies that trade is fine. For the ones we build for — construction firms sitting on proprietary drawings and bids, freight forwarders whose whole edge is their rate book — it's a non-starter.
So MaraponeAI started from a constraint instead of a feature list: nothing the customer feeds it ever leaves the building, and they pay for it once. Everything else had to be designed around that.
On-prem is a design decision, not a deployment note
When you commit to running on the customer's own hardware, a lot of comfortable assumptions disappear. You can't lean on a fleet of cloud GPUs. You can't ship a fix by redeploying a container nobody sees. The model has to be small enough to run on commodity machines and good enough that the size doesn't show.
That pushed us toward a quantised llama.cpp engine with a domain layer trained on real operational data, rather than a giant general model behind an API. It's less glamorous and far more useful: it runs offline, it's fast enough on hardware a firm already owns, and it's theirs.
You buy it once. It runs on your infrastructure. You get the source. Nothing leaves the building.
The privacy guarantee is the product
Once the engine lived entirely on-prem, the same guarantee carried into everything built on top of it — the construction suite reading Ontario Building Code drawings, the logistics suite auditing freight invoices. Same engine, same promise. GasperAI, the assistant inside both, never phones home because there's nowhere for it to phone.
The lesson we keep relearning: the hard part wasn't the model. It was having the discipline to say no to the cloud-and-subscription default that every other tool in the category defaults to.