Register once, distribute everywhere. Agents drive the workflows. Business data flows back, so the models keep learning your industry. If your web app runs on Linux, it is already a Topobius application.
The models are fine — the applications just aren't planted where the business actually happens. Topobius exists for the four walls every organization hits: anywhere there are people, processes and private data, AI should not live in someone else's rack, inside a chat window.
Authority converges to a single point in the group; sites always receive derived views. Control flows over the access plane, business flows over the Topobius bus — two planes, clearly separated, never mixed.
Choose by efficiency, not by hardware. Every site ships two model tiers behind an OpenAI-compatible interface — the platform neither discloses nor requires you to care about the engine underneath.
Throughput tiers are the design baseline of site products; actual figures vary with model, context length and site configuration. "Frontier-class" refers to the frozen benchmark suites and methodology published with each release.
No private DSL, no private container format, no private language. Twenty years of the web ecosystem works as-is; the only bar is a standard web service running on Linux.
Events flow up into the authoritative ledger; projections flow down with your department's process and org view. That is the entire protocol surface — implement these two and agents can orchestrate your app into the business flow.
# topobius.yml — one manifest says it all name: expense-app port: 8021 # loopback only; the edge is the platform's health: /healthz # fails → flagged red on the portal auth: sso # auth delegated, no local credentials declares: [flow-node] # declare: invocable by workflows $ topobius register ./expense-app ✔ structural gate · role matrix verified ✔ listed on the distribution portal, rides every site image into each organization
# ① events up — business actions into the ledger POST /bus/ingest {"flow":"expense","node":"submit","amount":1200} # ② projections down — your dept's flows & org view GET /bus/projections/R%26D # model runtime (OpenAI-compatible, tier aliases) POST /v1/chat/completions {"model":"expert-model"} # or "speed-model"
A six-station automated pipeline built into the platform: governed and synthesized business data enters training, passes evaluation, ships with a rollback pointer, and rolls out to sites over the projection channel. Fully automated — humans stand only at the release gate.
Read-only bypass on ledger & session archives; business untouched
Masking, de-identification, sensitive-field red-line list
Domain corpora built; training scheduled in windows
Auto-regression on frozen real-business benchmarks
Eval report + rollback pointer — the only human gate
Live metrics flow back as next-round preference signals
The two-piece Topobius matrix: the AI-hub brings expert inference and agents to the business floor; Topobius industrializes the distribution, driving and evolution of enterprise applications. Each runs alone — together, cloud-mesh-site as one.
Domain post-training, frontier-class local models and a multi-stream agent runtime in one desktop box. Plug in power and ethernet, and it stands watch — data never leaves your domain.
see the hardware page →Distribution, driving and evolution of enterprise AI applications. Full-stack freedom to integrate, two interfaces into the agent-driven track, and a flywheel that keeps teaching your industry.
you are reading it · book a demo below ↓No conflict with the models themselves — Topobius runs industry-adapted models and manages everything that comes after a model lands in your business.
| Dimension | General model API | Topobius |
|---|---|---|
| Cost model | Billed per token, linear with scale | One-time site investment, marginal cost approaches zero |
| Data sovereignty | Data leaves your domain, compliance gates everywhere | Inference local, governed controlled return flow |
| Governance | Org, permissions, approvals are your own problem | SSO · role matrix · approval chains built in |
| App form | A chat window | Workflow nodes invoked by agents along the process |
| Evolution | Model capability decided by the vendor | Industry adapters evolve with your business data |
The standard move for a new organization or branch: flash the site image → power on (auto-joins the mesh, pulls model packages, registers, self-checks and reports) → load the industry pack → management dictates the workflows → people start executing.
Bring your business scenario and your existing stack. In one demo: registration, distribution, agent-driven execution, and the data flywheel closing the loop.