Private, on-premise enterprise AI · Built on open models

Custom AI agents that run inside your firewall
built on open models, owned by you

Banfang builds production-grade AI agents and living knowledge bases that run entirely on your own servers or private cloud — powered by open models like DeepSeek, Qwen and Llama. Your data never touches a third-party API, and you own the models, the weights and the stack. For regulated teams in finance, legal and healthcare, that's the difference between an AI pilot and AI in production.

0
Bytes of your data sent to third-party APIs
100%
You own the models, weights & stack
2018
Shipping production AI since
14 days
From kickoff to a working pilot
Built for regulated industries where data cannot leave the building
Finance & fintech
Legal & compliance
Healthcare & life sciences
Insurance
Government & public sector
Manufacturing
SERVICES · Private-first AI engineering

Private AI, built on open models,
running inside your firewall

We do not pipe your data to OpenAI or Anthropic. We build on open models you can self-host, deploy on your own servers or private cloud, and hand you the weights — so your data never leaves and the system stays yours.

Private, on-premise LLM deployment

Run capable open models entirely on your own servers or private cloud. Your data never leaves your environment, and the weights are yours to keep.

  • Open models: Qwen / DeepSeek / GLM / Llama / Mistral
  • Runs on your own NVIDIA / AMD GPUs (and domestic accelerators)
  • Fine-tuning & distillation — no per-token API bills, no data egress

Custom AI agents inside your firewall

Autonomous agents tailored to your workflows — they reason, call tools and self-correct, all running against your private models so nothing sensitive is sent to a third-party API.

  • Multi-step planning / tool use / self-reflection
  • Long- and short-term memory and context engineering
  • Runs against your own models — zero third-party API calls

LLM Wiki · private living knowledge base

Move beyond stale RAG. Let your private model actively maintain a company wiki that grows on its own — synthesized once and reused, never shipped to an outside API.

  • Raw sources → wiki pages auto-synthesized and cross-referenced
  • Conflicts and duplicates detected, continuously linted
  • Fully on-premise, with permissioned and auditable access

AI customer service agent

A human-like AI rep with memory and product knowledge — online 24/7, deployable on your own stack so customer data stays in-house.

  • Omnichannel: web chat, WhatsApp, Slack, Teams, e-commerce platforms
  • Long-term memory recognizes returning customers
  • Smart fallback before human handoff to cut complaints

Workflow automation

Hand reporting, approvals, contracts, recruiting and data cleanup to AI, so people can do higher-value work — on infrastructure you control.

  • Native integration with Slack, Microsoft Teams, Google Workspace
  • Hybrid RPA + LLM orchestration
  • Observable, reversible, auditable

AI strategy consulting

For regulated teams deciding where AI can help without putting data at risk — we map the use cases worth building first.

  • Use-case scanning and value ranking
  • ROI modeling and a delivery roadmap
  • Data, security and compliance assessment
WHAT WE BUILD · Products & reference builds

The systems we build,
running on private infrastructure

These are our own products and open-model reference builds — the capabilities we deploy on your servers. Figures below are from our internal benchmarks and demos, not claims about named clients.

Our product · Moss

Moss · the autonomous AI employee we deploy privately

Moss is our enterprise-grade autonomous AI Agent platform. Give it a goal — "do the Q3 competitor analysis", "triage and reply to these 200 emails", "run a company-wide database performance review" — and it breaks the task down, calls tools, produces the result, and reflects and self-corrects along the way. We deploy it on your own models and hardware, so the entire loop stays inside your firewall.

98.6%Task success (internal eval set)
3.2×Output in pilot demos
$950K+Modeled annual labor savings
Moss AI employee autonomous task execution
Our product · Agent Duoduo

Agent Duoduo · AI customer service that remembers and feels human

The biggest problem with traditional support bots is that every visit feels like the first. Agent Duoduo has a long-term memory system: it remembers what each customer asked before, bought before, and prefers. Combined with multi-turn reasoning, it is nearly indistinguishable from your best human rep — except it is online 24/7 and never quits. We deploy it on your own stack, so conversation data never leaves.

96.2%CSAT in benchmark
↓ 72%Human handoff (demo)
0.8sAvg. first response
Agent Duoduo AI support conversation and memory graph
Our build on open source · OpenClaw

OpenClaw Enterprise · run the hottest open-source Agent safely inside your network

OpenClaw is a phenomenon in the 2026 open-source community — an autonomous AI assistant with 330K+ GitHub stars. Banfang packages private deployment, an enterprise permission gateway and industry skill extensions for mid- and large-size companies, so OpenClaw can do real work inside the firewall — not just be a developer toy.

100+Native skills (open source)
60+Banfang enterprise skill packs
↑ 12×Self-service tasks (demo)
OpenClaw Enterprise AI assistant capability matrix
Our build on open source · Hermes

Hermes Memory Hub · AI that gets smarter the more you use it

Hermes is an open-source self-evolving AI Agent framework from Nous Research, built around persistent memory and automatic skill capture. On top of Hermes, Banfang builds an enterprise "AI memory hub": every conversation, task and document is continuously synthesized into an LLM Wiki and reusable skills, with one-click integration into Slack, Microsoft Teams and the IM tools you already use.

186Auto-generated skills (demo)
6+Native IM integrations
↓ 65%Repeat questions (demo)
Hermes Agent enterprise memory hub workflow
Reference build · Retail growth

Retail & e-commerce · AI marketing growth Agent

A reference growth Agent we build for retail teams: it analyzes omnichannel traffic in real time, auto-generates A/B copy, identifies high-value customers and reaches them at the right moment. It can make tens of thousands of micro-adjustments a day, turning manual operations into intelligent decisions.

+187%Illustrative quarterly GMV
+220%Illustrative repeat-purchase
1/5Ops headcount needed
Retail e-commerce AI growth Agent dashboard
See all case studies →
HOW WE WORK · Our process

From a 30-minute chat to results,
in as little as 14 days

Use-case diagnosis

A 60-minute deep dive to map the parts of your business most worth reshaping with AI.

PoC validation

A demoable proof of concept in 5 to 10 working days, validating feasibility and ROI on real data.

Production delivery

Full system build, integrated with your CRM / ERP / LLM Wiki / messaging tools — usable, stable, observable.

Continuous improvement

Pay-for-performance options plus monthly iteration, so your AI employees keep getting smarter.

FAQ · Common questions

What business leaders ask us most

Our data is sensitive. Can we avoid sending it to public LLM APIs?

Yes. We deliver fully private, on-premise deployments running on your own GPUs or private cloud, supporting open models such as Qwen, DeepSeek, GLM, Llama and Mistral, so your data never leaves your environment.

We have not decided what to build yet. Can we still talk?

Absolutely — that is exactly what we are good at. The first conversation is free. In about half a day we help you map the 3 to 5 use cases most worth building, with a rough ROI estimate and a priority recommendation.

What if the AI is unreliable and breaks in production?

We use evaluation-driven development: every Agent ships with a real-world evaluation set, and it cannot go live until it passes the CI scoring gate. In production we add full monitoring, graceful degradation and human takeover, so it is genuinely production-ready.

Our budget is limited. Can we start small?

Of course. Our 14-day MVP package proves value at minimal cost first, then scales once it works. Most clients start from a single use case.

How are you different from big-tech AI teams and consulting firms?

We are smaller and more focused. Our team comes from frontier LLM companies and engineering teams — we understand both the cutting edge and how to actually get things done inside a company. We do not sell slide decks; we ship systems that run.

LET'S TALK

Keep your data in-house — and still ship real AI

If your industry won't let you send data to a public API, you don't have to sit out the AI shift. Book a free architecture call and we'll map a private deployment for your stack.

📧 bd@thebanfang.com 📞 +86 187 0117 8691