Enterprise AI Agents
you can govern.
Inside Feishu.
SiNan is an enterprise-grade AI Agent gateway. It opens a chat entry inside Feishu / DingTalk / WeCom for your employees, connects to your internal knowledge base and data sources, and bakes governance — audit, quota, policy, multi-tenancy — into every call. We don't rewrite the agent — instead it's pluggable, so Hermes, OpenClaw and other open-source agents can run as the core interchangeably.
Live in production · Whole workforce in a week · 2746 automated tests
Handing employees raw ChatGPT
is the CIO's most expensive compromise.
Most AI Agent products solve the easy part — making it talk. The harder enterprise question is: who is allowed to talk, what did they say, what did it cost, and can the answer be trusted? SiNan answers all four.
PAIN 01
Employee chats leave the building
Free-tier ChatGPT and Claude train on the conversations. Customer lists, contract values, internal source code that an employee pastes in — they all end up in someone else's training set.
PAIN 02
Foreign services don't work in China
OpenAI doesn't sell to China. Anthropic doesn't sell to China. The gateways that do exist either carry compliance risk, have unpredictable latency, or have no Feishu / DingTalk integration at all.
PAIN 03
Audit trail is essentially zero
When something goes wrong, the CIO has to answer 'who, when, what data, what query'. SaaS export tools are too thin to satisfy a regulator.
PAIN 04
Token cost is uncontrolled
Without per-user quotas, a single employee can burn a department's monthly budget in 24 hours. LLM bills have no tiers; the end of the month is a surprise.
Sidecar pattern —
augment, don't rewrite.
SiNan does not build a new agent loop. It uses pluggable open-source agents as the core — Hermes, OpenClaw and others can be swapped in without touching the governance layer — and wraps them with enterprise governance, Chinese IM ingress, and a self-learning Skill library. Same code, your servers.
HEADLINE 01
28+
Self-Learning Skills
Query, analysis, generation, and SQL — four categories out of the box. The system learns your workflows and generates new Skills automatically — it gets sharper the more you use it.
HEADLINE 02
2746
Automated Tests
Full coverage across Phase 0–3. Every commit runs the whole suite; zero failures gates the merge. This is the hard definition of 'enterprise grade'.
HEADLINE 03
6
Native IM Channels
Feishu (full API: send / stream / upload / download / cards), DingTalk, WeCom, web, REST, and custom webhooks. Employees keep their existing tools.
Five sidecar layers,
agent becomes enterprise service.
SiNan turns one employee message into an 11-step observable, auditable, interruptible request flow. Every layer is independently replaceable — swap IM, LLM, database, or KMS without touching business code.
LAYER 01
IM Ingress
FeishuAdapter / DingTalkAdapter / WeComAdapter — webhook parsing, signature verification, attachment download, card rendering
gateway_extensions/
LAYER 02
Governance
AuditLogger (SHA256 hash chain) / QuotaManager (4-tier + Redis sliding window) / PolicyEngine / SSO / RBAC
governance/
LAYER 03
Agent Engine
Pluggable agent core — Hermes, OpenClaw and other open-source agents plug in via JSON-RPC adapters. MockClient for local dev.
adapters/
LAYER 04
Skills & Knowledge
28 self-learning Skills (system auto-generates new skills) + pgvector knowledge base (Feishu wiki / drive direct + live ACL) + Skill marketplace (sandbox review)
skills/ + knowledge/
LAYER 05
Persistence
Postgres + RLS (SET LOCAL ROLE sinan_app forces isolation) + AES-256-GCM envelope encryption + S3 files + Redis quotas
persistence/ + delivery/
Every layer has its own health check and fallback path. One layer failing does not collapse the whole chain.
Six ready-to-deploy subsystems
covering every hard enterprise agent need.
Native IM Ingress
Employees do not switch tools. They @ SiNan inside Feishu and get a card message back with download buttons (Excel / Word / PPT / PDF).
Knowledge · Direct Feishu
Whole-tenant scan of Feishu wiki and drive; document changes flow back within 60 seconds. Permissions are checked live against Feishu ACL on every query (5-min cache). No mirroring, no stale grants.
28 Self-Learning Skills
Query, analysis, generation, and SQL — four categories out of the box. The system learns from your business and generates new Skills automatically — the more you use it, the sharper it gets. SHA256 content cache, cross-process quota, audit callback all built in.
Per-tenant Key Isolation
Each tenant's API keys are AES-256-GCM envelope-encrypted in Postgres. KEK comes from OpenBao or an HSM. Dashboard CRUD with test-before-save validation.
Multi-tenant Physical Isolation
Physical Postgres database isolation + RLS (SET LOCAL ROLE sinan_app forced) + container-level isolation. New tenants spin up with one click; Feishu org sync auto-discovers members.
Audit · Quota · Policy
SHA256 hash-chain audit is write-once. Four-tier quota (ORG > CUSTOMER > TEAM > VK) with cluster-safe Redis sliding window. Policy engine gates field-level sensitive actions.
Secure by default,
not by ops discipline.
Every security decision in SiNan is enforced fail-closed in code, not as a 'be careful' note in a runbook. Every boundary has a hard constraint with integration tests.
DEFENSE 01
Data Stays Inside
SiNan deploys entirely on your own servers. Feishu / DingTalk content is carried by your own Feishu account. Embedding and LLM API keys are yours.
DEFENSE 02
Permissions Stay Live, Not Mirrored
Document permissions are not copied into Postgres. Every query confirms with Feishu in real time whether the current user is still allowed to see the document (5-min cache). Departures and revocations take effect immediately.
DEFENSE 03
Envelope Encryption + KEK Rotation
tenant_api_keys table: each secret has its own DEK, the DEK is wrapped by a KEK, and the KEK comes from OpenBao or an HSM. AAD binds (tenant_id, slot); cross-tenant row copy fails to decrypt.
DEFENSE 04
Tamper-Evident Audit Chain
Every audit event contains the SHA256 of the previous one. Any tamper breaks the chain. WORM storage + async flush as belt-and-suspenders.
Why not
build it yourself?
We spent six months getting Phase 0–3 done. Below is the cost of not picking SiNan.
vs Build
Save 6+ months
SiNan today is 221 source files, 2746 tests, and 30+ subsystem docs. Six months is the floor — most in-house projects stall at 'demoable but not production-safe'.
vs SaaS Gateways
Data does not leave
Most AI Agent SaaS products store conversations in their own database. SiNan runs on your servers; the KEK and database credentials are in your hands.
vs Foreign Stacks
China-compliant + IM-native
OpenAI and Anthropic do not sell to China. LangChain and LlamaIndex have no Feishu / DingTalk adapter, no RLS, no OIDC, no audit chain.
vs Thin Wrappers
Three layers of defense
Audit, quota, and policy are not decorative features. When regulators want logs, when the CFO wants a spend breakdown, when product wants conversation replay — SiNan answers all three on day one.
Deploy SiNan
into your Feishu.
SiNan is live in production — put your whole workforce on AI within a week. From key configuration and Feishu integration to full rollout, we walk it with you end-to-end, including instrumentation, quota tuning, and custom Skill development.
First conversation is free · 1 hour · includes a technical architecture walkthrough