No matter how flagship an online model claims to be, general-purpose LLMs lack access to internal enterprise domain data and mission-critical generalization. Our engineering practice proves: an agile foundation model post-trained on curated internal domain corpus decisively outperforms any online flagship in targeted professional tasks. Continuously fine-tuned with live enterprise operational context, it creates a globally unique model customized exclusively for your enterprise—entirely on-premises, with zero data egress.
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.
Every layer is orchestrated around the Model Intelligence Customization Flywheel: base model selection and optimization, hardware-accelerated local inference, DeepSeek Harness agent runtime driving live workflows and capturing context, corpus bus governance, and automated on-prem post-training.
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 ↓Generic frontier models are internet generalists lacking internal domain data. Topobius delivers globally unique, self-evolving on-prem intelligence.
| Dimension | Public Generic Model API | Topobius Custom Model Flywheel |
|---|---|---|
| Domain Depth | Generalist; lacks proprietary data; prone to professional hallucinations | Agile foundation model + curated domain post-training, outperforming online flagships |
| Self-Evolution | Static; model capabilities dictate by vendor; ignores enterprise context | Live operational context feeds continuous on-prem tuning into a unique custom model |
| Data Sovereignty | Confidential data exported to public clouds; compliance bottlenecks | Strictly on-premises; models, corpus, training, and inference never leave your perimeter |
| System Engineering | Bare API only; language environments, sandboxes, and databases must be self-built | Topobius OS provides full-stack runtimes, secure sandboxing, and DeepSeek Harness host |
| Concurrency & Resilience | Per-token billing; network jitter causes total downtime; unpredictable latency | Gradientron appliance; 16-agent lock-free concurrency; persists offline without degradation |
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.