How is TotalClaw fundamentally different from a thin wrapper around someone else's AI?

Our AI agent engine is entirely developed in house, so we control it from the underlying logic up to the application layer. That makes compliance review possible and white-label delivery practical. With a wrapper, neither the source of the engine nor the pace of its evolution is in your hands.

Does TotalClaw support private and offline deployment?

Fully. By default it runs on employee machines or your own servers, and outbound network access can be switched off. Private delivery includes local inference, intranet model scheduling and a signed RPC cluster.

Is going live within a week realistic?

For a mid-size team on a standard intranet setup, evaluation, installation, skill configuration and go-live can be completed within a one-week window. Complex integrations take 2-4 weeks depending on scope.

Which large language models can be connected?

More than 40 mainstream models, both domestic (DeepSeek, Tongyi, Ernie, Baichuan, Zhipu, Kimi, Doubao) and international (GPT, Claude, Gemini), plus private inference via Ollama or vLLM.

How is data security handled?

All data stays within your own boundary. TotalClaw does not access, store or reuse it. Log retention defaults to six months or more to meet regulatory requirements, and interfaces are provided for content review, generation watermarking and cross-border consent.

TotalClaw Built in house · Runs offline · LAN clustering · Ecosystem-neutral: an AI platform deployed in the company's own environment

Every machine can run an AI assistant that understands the task, operates software and builds up experience,
at a price small businesses can afford, with stable operation and data you can trust.

Built in house Our own engine · ready for certification

Runs offline Data never leaves the company

LAN clustering Work dispatched across machines · shared memory

Ecosystem-neutral 40+ models · 9 messaging platforms

Most AI assistants on the market are built on the same American open-source engine and keep data in the vendor's cloud.
TotalClaw's engine is our own, and it is deployed in the company's own environment.

Where the difference lies

Four differences between TotalClaw and similar products

Many products look similar on their websites. Whether you can rely on one over the long term depends on the underlying design: the engine, the data, the models and how machines work together.

Engine Built on the American open-source OpenClaw; if it changes its licence or cuts off supply, you can only react Engine built from scratch, copyright held by us, eligible for domestic-technology certification review

Data Uploaded to the vendor's cloud, hard to trace if something goes wrong Stays 100% on your machines, and runs with the network unplugged

Models Tied to one vendor, so price rises and rate limits just have to be accepted Switch among 40+ models at any time, which leaves you options on price

Collaboration Either every machine runs on its own, or everything has to go to the cloud Tasks dispatched to several machines on the LAN and completed together

TotalClaw delivers a team of AI employees deployed in your own environment,
and the longer it runs, the more business experience it builds up.

One · Four capabilities we own

All four capabilities are our own

An engine built in house, offline operation with data you control, LAN clustering, and cross-ecosystem integration: we own all four. Classified, financial, public-sector and domestic-technology procurement usually requires them.

Engine built in house · ready for certification

The platform's core is built by us from scratch, not a repackaging of any open-source agent framework. Most comparable Claw products are built on open-source frameworks.

We hold the engine's copyright: upstream going closed-source, changing its licence or export controls don't affect it.
Trimmable and white-labelable: keep the features you need, customize the interface and switch to your own brand.
Suited to sovereign procurement: an advantage in domestic-technology, compliance and technical reviews.

No dependence on an upstream open-source project.

Runs offline · data you control

Local or private deployment, with company data 100% under your control and never leaving the company, running even with the network unplugged.

Offline licensing: generated and verified on the intranet; login, marketplace, webhooks and other outbound entry points can all be turned off.
Intranet models: local inference with Ollama, vLLM or a privately fine-tuned model, so sensitive data stays inside the boundary.
Three pluggable memory layers plus long-term memory: from files and conversation stores up to enterprise five-layer LTM, with all material kept in the company's hands.

An option for classified, financial and public-sector customers.

LAN clustering · machines working together

Several machines on the customer's LAN dispatch tasks, share memory and execute together. Cloud multi-tenancy is not this kind of cluster.

Networking: UDP multicast discovery, TCP JSON-RPC and HMAC signature verification to block spoofed nodes.
Cluster memory: context shared between nodes via MongoDB, with no third-party middleware.
Controller and worker topology: one controller and N worker machines, tasks dispatched across them, audited in one place.

Combined with 01 and 02, it meets domestic-technology, intranet and sovereign-control requirements at the same time.

Cross-ecosystem integration

Customers choose their own models, and mainstream office and social platforms are connected, with no tie to any single vendor.

40+ large models: Tongyi, Ernie, Zhipu, DeepSeek, Kimi, Hunyuan, Claude, GPT and more; any model with an OpenAI-compatible interface can be connected.
9 messaging platforms: WeChat, WeCom, Feishu, DingTalk, Douyin, Xiaohongshu, Telegram, Slack and WhatsApp.
One core across every client: desktop, mobile, web and enterprise API all share the same core.

Changing models or platforms doesn't mean changing systems.

Two · Product advantages

It holds a conversation, and it does the work

Underneath are 50+ in-house core modules, a bidirectional NDJSON protocol, Docker sandbox isolation and intelligent routing across 40+ providers. That is why it can operate software, coordinate across machines and run locally without data leaving the boundary, with every operation auditable by the company.

A few numbers

40⁺ Mainstream model providers connected Domestic, international and private models all supported

9 Messaging and office platforms WeChat / WeCom / Feishu / DingTalk / Douyin / Xiaohongshu / TG / Slack / WhatsApp

244k+ Skills available Development · office · creative · data · security

Layered memory

It remembers the company, the team and each person, and what you discussed is still there next time.

Short-term: preferences mentioned in passing and the context of the current task, usable from the very next message.
Medium-term: kept across days, sessions and devices, so a conversation can pick up where it left off.
Long-term: company material, policies and customer records build up into the company's own knowledge base.
Enterprise long-term memory (optional on Flagship · Enterprise and Custom Services): five LTM layers (workspace, episodic, semantic, archival and knowledge graph), isolated by team, project or session, with fused retrieval and knowledge-graph reasoning, so several agents running at once never mix up records.

Execution

Besides answering questions, it can go and get the work done.

Reads, writes and revises documents
Researches, summarizes and produces reports
Runs processes, sends messages and sets reminders
Draws and recognizes images, takes voice input and reads aloud
Runs on a schedule, online 24 hours a day

Building experience

The platform improves itself: the more it is used, the better it knows the business, the team and each person.

The process of every task is recorded as experience.
Methods that worked are turned into skills and reused next time.
Internal policies, knowledge and templates gradually become its default way of working.

The longer you use it, the less you have to explain.

Collaboration

Use it as a department, as a partner for a team, or as the hub for the whole company.

As a department: fills the missing piece in an existing team, with the whole department plugged in directly.
As a team partner: always on call; @ it in the group chat and it takes the job.
As the company hub: conversations up front, execution in the background, and one shared memory and set of processes for everyone.

Three · Service advantages

Deployment, operations, support and compliance

Hands-on deployment

Across systems and chips: supports Windows, macOS, Linux and domestic environments.
One-command install: in common environments, one command runs the install script and completes the deployment.
Delivery your way: subscription, perpetual license, private deployment or custom development, at the company's own pace.
Quick to go live: a mid-size team needs relatively little time to go from evaluation to production.

Low-maintenance operations

Built-in diagnostics: after installation it can check itself and automatically fix common environment problems.
Complete logs: every conversation, tool call and decision is recorded, meeting compliance and audit requirements.
Centrally pushed configuration: headquarters configures once and branches sync with one click.

Support you can count on

Documentation: developer docs, operations manuals, migration guides and skill development guides.
Enterprise projects: complex requirements go through a dedicated delivery track handled directly by our engineering team.
Long-term support: ongoing product and technical support from selection consulting through delivery and day-to-day iteration.

Compliance handled

Log retention of six months or more; the default configuration meets domestic regulatory requirements.
Interfaces for content review, generation watermarking and cross-border data consent are all available.
Data stays in the company's own environment. We don't access it, store it or reuse it.

Four · Why choose TotalClaw

Three kinds of AI assistant compared

Compared on six points: engine, data, offline operation, clustering, models and deployment threshold.

Dimension	Comparable products on open-source frameworks	Public cloud SaaS assistants	TotalClaw
Engine origin / sovereign control	Repackaged open source	Vendor's closed cloud	✓ Entirely in house · ready for certification
Does data stay with the company	×	×	✓ Never leaves the premises
Offline · runs disconnected	×	×	✓ Offline licence + intranet models
LAN clustering	×	Cloud multi-tenancy only	✓ Work across machines · cluster memory
Freedom of model choice	Mostly locked to one vendor	×	✓ 40+ providers, fully open
Deployment and decision threshold	Project-dependent	Sign up and go · data to the cloud	✓ Self-service from 30 minutes · custom in as little as 7 days · IT can decide

A general comparison that does not refer to any specific product; actual features and delivery boundaries follow the release notes and the contract.

Five · In closing

The product we want to build

TotalClaw wants to give companies an AI assistant platform of their own:

one that lives on your own servers,
speaks the language of your industry,
remembers what your team has done,
runs your company's processes,
and over time, comes to feel like a long-serving member of the team.

Our own engine not constrained by upstream projects

Your own data data never leaves the company

An open ecosystem 40+ models · 9 messaging platforms

Taitu Technology · TotalClaw

Talk to us See the product and solutions