Put LLMs to work on real business problems
One TokenHub, running on your own servers. Combine model integration, the RAG knowledge base, access control, auditing and cost governance to fit each use case. These four scenarios cover the paths most companies take.
Ask about any policy, product or tech doc and get an answer
Bring scattered company knowledge into one place. Employees ask in plain language and get answers that cite the original source, while department-level data permissions are always respected.
Challenges
- Policies and docs are spread across shared drives, wikis and email, so people can't find what they need
- The same questions come up again and again, eating up time from HR, IT and subject-matter experts
- Employees paste company data into public AI tools, risking leaks with no way to verify answers
- Each department's material must stay walled off, not open to everyone
Solution
- Automatic parsing, chunking and embedding for PDFs, Word files, web pages and wikis
- Hybrid search plus reranking, with source citations and page numbers in every answer
- Knowledge bases are permissioned by department or project, and search filters out anything a user can't see
- Available in WeCom, DingTalk, Feishu (Lark) or your intranet portal
ResultsSample
How it works
Features used
24/7 answers without blowing the budget
Answer customer questions automatically from your product knowledge base, send each request to the right model for its difficulty, and hand off to a human when the bot isn't sure.
Challenges
- Big swings in ticket volume, high staffing costs and no one on shift overnight
- Keyword bots miss the point, so too many chats end up with an agent
- Sending every question to a premium model makes costs hard to control
- Inconsistent answers with no way to review or trace them
Solution
- Build a support knowledge base from product manuals, FAQs and past tickets
- Route by complexity: simple questions go to low-cost models, hard ones to the most capable
- Frequent, similar questions are answered straight from the semantic cache
- Low-confidence or sensitive questions go to an agent automatically, with a chat summary
ResultsSample
How it works
Features used
Give every engineer AI, from one place
One endpoint for IDE plugins, the command line and CI pipelines. Manage quotas and permissions per team, and keep core code on internal models only.
Challenges
- Everyone signs up for their own model accounts, and keys end up in repos and on laptops
- Usage and spend can't be rolled up by team, so budgets keep getting blown
- Code gets pasted into public tools, putting source code at risk
- Onboarding is slow, and accounts aren't revoked promptly when people leave
Solution
- OpenAI-compatible: point IDE plugins, CLIs and CI at a new address and you're done
- Issue API keys per team with quotas, rate limits and allowed models
- Force requests that touch core code to a local coding model
- Self-service access via SSO, and keys revoked automatically when someone leaves
ResultsSample
Architecture
Features used
Deployed locally, runs offline, data never leaves your network
The platform, models, knowledge bases and vector store all live in your own environment. Manage every local model in one place and log every call, ready for internal audits and regulatory reviews.
Challenges
- Data can't leave the internal network, so public cloud models are off the table
- Several local models are deployed, each with its own API and no central management
- No visibility into usage makes audits and compliance checks painful
- Nothing can be installed or upgraded online inside the isolated network
Solution
- TokenHub, knowledge bases and the vector store all run on your own servers, with air-gapped deployment that works fully offline
- One place to manage local inference servers like vLLM, Ollama and Xinference
- Complete audit logs plus the Live Dashboard show usage at a glance
- Offline installer and offline upgrades, with support for domestic hardware and OS environments evaluated on request
ResultsSample
Deployment
Features used
* All results are sample figures that illustrate the order of magnitude. Actual results depend on your use case and environment.
Built for the security and compliance needs of every industry
Typical use cases by industry. Every setup can be tailored to your business and compliance requirements.
Finance
Research Q&A, compliance policy search, agent assist and QA
Manufacturing
Equipment manual Q&A, process know-how, after-sales troubleshooting
Government
Policy Q&A, document drafting, on-prem deployment on internal networks
Education
Campus knowledge base, teaching support, student and staff help desk
Healthcare
Hospital policy search, documentation help, data stays on-site
Internet
Multi-model routing and cost optimization, developer productivity, moderation
Start with one scenario and put LLMs to work
Download TokenHub for free and get your first use case running on your own server. For commercial deployments, contact us about a commercial license. We're happy to talk through your deployment plan too.