Your company's AI workspace.
Team chat grounded in your internal knowledge, agents that automate complex workflows, and evals that keep model quality honest — all running on your own infrastructure.
Everything teams need to work with AI safely.
Team Chat with RAG
Multi-model chat interface shared across your organization. Retrieval-augmented generation grounded in your knowledge bases, with conversation history and threading.
Knowledge Bases
20+ connectors: Slack, Confluence, SharePoint, Google Drive, GitHub, Jira, Notion, and more. Incremental sync, delta updates, and HNSW indexing handled automatically.
Agents & Workflows
Build multi-step agents with tool use and conditional branching. Trigger from chat, schedules, or webhook events. Human-in-the-loop approval steps with audit trail.
Web Search
Live web retrieval blended with internal knowledge base results. Configurable search providers and result-count controls. Source citations shown inline in every response.
Voice (STT / TTS)
Speech-to-text input using Whisper-compatible endpoints. Text-to-speech playback with multiple voice options. Works with on-prem model deployments — no cloud required.
Evals & ELO Leaderboards
Run head-to-head model comparisons on your own datasets. ELO scoring tracks model quality drift over time. Export eval results for compliance reporting.
From setup to grounded answers in one afternoon.
Connect your knowledge sources
Authorize connectors for Slack, Confluence, Drive, GitHub, and more. ManyLayers syncs incrementally on a schedule and indexes new content automatically.
Teams start chatting immediately
The Workspace UI is ready with no configuration. Users pick a model, ask a question, and get a grounded answer with source citations from your internal documents.
Run evals to keep quality honest
Define a test set from real user queries, run it across model candidates, and let ELO scoring surface the best option. Schedule weekly eval runs automatically.
Grounded answers from your own data, never a third-party index.
Consumer AI assistants index your documents on shared infrastructure. ManyLayers Workspace keeps embeddings, vector indexes, and conversation history inside your own Postgres — no data leaves unless you tell it to.
- All document embeddings and vector indexes stored in your own Postgres instance
- Per-user model preferences — teams can choose GPT-4o, Claude, or an on-prem model
- Workspace routes through Gateway, so budget limits and guardrails apply automatically
- SAML/OIDC SSO and SCIM provisioning supported out of the box
- Offline-capable — Workspace continues working when internet is unavailable
“Our teams were using a dozen different AI tools with no visibility into what was being shared externally. ManyLayers Workspace centralised everything — one interface, one audit log, all on our servers.”
Head of Engineering — Financial Services Firm