This is not a sales page. It sets out which category 361 defines, why the timing is today, what the architecture actually defends, and how each risk is answered. No numeric claims — a thesis.
On one side sit the core systems that take years to replace: the data is there, the process is there, the authorization model is there — but the intelligence is not. On the other side sit general-purpose AI tools spreading fast: smart, but connected to none of the company's data, none of its permission model and none of its processes.
Enterprises are caught between the two. Replacing the core is too risky to contemplate; general-purpose tools cannot reach the real work of the business.
Data and process live here. But changing them means a risky transformation that takes years.
A governed AI layer that sits on top of your existing business systems and connects to enterprise data, permissions and processes. It does not replace the core; it adds intelligence on top of it.
Smart, but not wired into enterprise data, role- and row-level security, or business process.
Model access has commoditized. 17 AI providers and a broad model pool mean the same class of capability is reachable through more than one route. In the same period, models that run on local hardware matured — serious work is now possible on a reasonable server.
The consequence is clear: the model itself is increasingly a component. What creates the difference is the layer that binds the model to enterprise data, authority and process — the part that decides which data a model may see, on whose behalf it may act, and where the result lands inside a process.
A second force arrived at the same time: pressure for data sovereignty. KVKK (Turkish data protection law) and sector regulation make a non-cloud execution path mandatory for many organizations. Local execution is no longer a preference; in some sectors it is the price of entry.
Defensibility is measured by how the architecture is built, not by marketing claims. 361 rests on four structural pillars, and all four are part of the running architecture.
Business logic lives in definitions, not in code. Adding a module or changing a schema requires no restart. That prevents a separate fork per customer — one core, many organizations. The most common enterprise-software disease, the one that blocks a product from scaling, is solved here structurally.
AI is bound to the organization's real data model, its state machines and its role- and row-level security. What you ask is not a chat message; it is an authorized query. This is precisely the place general-purpose chat layers cannot reach: the inside of the enterprise data model.
It is not only the language model that runs locally. Embeddings, OCR, speech-to-text, text-to-speech and image generation run on-machine with no token cost. That is a structural advantage on both data sovereignty and unit economics.
Reseller and sub-reseller tiers, signed manifest authorization and automated installation flow are inside the product. It is designed to scale together with its sales channel — the channel is part of the architecture, not a commercial arrangement added later.
Every line above represents a relationship built in the field over years and an installation that is running today. The AI layer lands on that base: it opens on ground that already knows these organizations, knows their processes and is already in contact with them.
In practice this means one thing: there is no cold-start market risk. The first conversation is not an introduction; it is a conversation about what to add on top of an installation that already exists.
One process is chosen and an installation running on the organization's own data goes live. The existing system stays untouched.
The outcome is discussed as measurement, not as a presentation: how much faster the process got, how much of it became automatic, which step is still human.
New processes and modules are added on top of the running installation. Expansion is not a new project; it is the same layer growing.
Commercially, this model shortens the enterprise sales cycle. Entry friction is low — a small scope over a short period — and because nothing in the existing system changes, it creates no risk item for IT to push back on. The decision stops being a large transformation decision and becomes the decision to try one process.
The objections raised in this category are well known. Below is each one, and what stands against it.
| Risk | How it is addressed |
|---|---|
| The "does-everything product" perception | Entry is always through a single process. A layer architecture does not displace existing tools; it runs above them. Scope widens at the organization's own pace. |
| Model provider lock-in | Support for 17 providers and a full local stack exist side by side. Switching provider is a configuration matter, not a redevelopment. |
| AI reliability | 4 detectors + guardrail pipeline (personal data, hallucination, prompt injection, cost ceiling), human approval flow, audit trail and a persistent emergency stop. |
| Data sovereignty | Fully local and air-gap operation. In localOnly mode, cloud AI providers are not listed and, if required, requests are categorically refused — a technical guarantee. |
| Cost unpredictability | Request, daily and cost ceilings are defined; soft and hard modes can be chosen between; notifications fire as a threshold is approached. |
| Implementation capacity | A reseller and partner channel with tiered authorization architecture. Implementation capacity does not depend on a single team; it is multiplied through the channel. |
Partnership tiers and the scale architecture are set out on the channel page. If you would rather talk directly, our line is open.