Process

How Manar
Builds.

Every deployment follows the same five steps. Train, deploy, handover, adapt, compound. The steps do not change from one industry to the next. What changes is the data, the hardware, and the job the model is trained to do.

These five stages are how a deployment is built. Inside a client engagement they are carried by the Adoption Path, which begins with a Site Read and ends with your staff operating a system that met its number.

The five stages

From Registry. To Compound.

01 Train

A deployment starts from a registered model built for the general classification a client needs, then trained further on the client's own data. Its cameras, its documents, its machines. Nothing generic stays generic past this step.

02 Deploy

The trained model installs on hardware the client owns, sized to what the specific model needs to do. Everything runs inside the client's building, offline if required, with no cloud dependency built in.

03 Handover

A system nobody uses has failed, whatever the accuracy numbers say. Handover is complete when your own staff can operate it without Manar in the room.

04 Adapt

A model retrains as the client's own data grows, so it reflects the operation as it is today rather than as it was on the day it went live. Scheduled with the client. Never run silently.

05 Compound

Accuracy improves with every day the model runs on the client's own conditions. An organization that starts training now holds a record a competitor starting later cannot shortcut or buy.

How a model is built
HIKMA حِكمة

The House of Wisdom took what existed and refined it into something that did not exist before. Hikma works the same way.

Every model Manar deploys begins as an openly available foundation model. The strongest one available for a given domain, reviewed as the open field moves forward. What makes it useful to one specific client is what happens after that starting point.

It gets trained on the data of the exact domain and environment it will serve. Local conditions and local patterns no general training run has ever been shown. What leaves that process is not the same model that entered it. It belongs to the client, runs on hardware the client owns, and reflects a site no other model in the world has been trained on.

A typical engagement

Starts with a site visit. Not a proposal.

Manar looks at the cameras, records, or systems already running before recommending anything, because the right model and the right hardware depend entirely on what is actually there. After that, training runs against real data from the site. Validation happens against the same conditions the model will run in once it is live. Installation and handover follow once the client has seen the results and is satisfied with them. Nothing about this timeline is fixed in advance, because no two sites start from the same place.

Model naming

A discipline. Not decoration.

Every model that gets trained is given a formal code before it is deployed: a family, a domain, and a method, in that order. A vision model trained for a security site is not the same registry entry as a vision model trained for quality control on a production line, even if both come from the same family. It means every model Manar has ever trained can be found, checked, and accounted for.

View registry Start a conversation
Continue
Related
Model Registry
Browse all registered models.
Related
Industries
See it applied by sector.
Related
Contact
Start with a site visit.