Zero data leakage.
Remove bureaucratic friction.
Empowering employees.
PandaBear gives innovation teams a secure way to adopt AI without sending sensitive company data to external model providers. Deploy it on your infrastructure, connect the systems people depend on, and let employees turn questions into governed action.
Human collaboration, amplified
The people closest to the work can move it forward.
PandaBear makes company knowledge usable without turning every question into a chain of meetings. It brings the right, permission-aware context to the employee who is ready to build, decide, or improve a process.
An employee spots an operational problem and asks PandaBear what has already been tried.
PandaBear connects approved knowledge, systems, and policies inside the company environment.
It returns the relevant context and routes the right local action, without exposing raw company data.
One person moves a problem forward. The whole organization can build on it.
The problem
The future of work is more autonomous, but large organizations become more bureaucratic as they scale.
Employees need trusted access to the context and systems required to act.
Move work forward.
PandaBear makes that possible without asking the enterprise to give up control of its data.
A private AI operating layer for innovation and transformation teams: deployed on your infrastructure, governed by your policies, and built to help employees create momentum across the organization.
Sovereign execution layer
Bring AI to your data. Keep control where it belongs.
Work Where Employees Already Work
PandaBear meets employees inside approved workspaces and connects their questions to the context, policies, and tools they are allowed to use.
- Prompt and diff capture
- Active-file and selection context
- Branch, commit, and repo state
- Generated AGENTS.md and Cursor rules
- One-click context retrieval
A Sovereign Company Intelligence Layer
Company context, permissions, and approved capabilities remain inside the customer environment, creating one governed layer employees can safely act through.
- Event API and durable queue
- Postgres + pgvector KAG-lite
- Graph extraction worker
- Rules and context generator
- Self-hosted per organization
Answers That Can Become Action
Employees ask PandaBear instead of chasing departments. It finds the approved context, checks policy, and routes the right local capability to move the work forward.
- get_task_context()
- explain_related_history()
- submit_prompt_event()
- get_agents_md()
Momentum Without More Handoffs
PandaBear brings policy, precedent, and approved tools together before another employee loses days collecting answers from separate teams.
- Network-isolated sandbox jobs
- Test, lint, and typecheck evidence
- Prompt-to-diff attribution
- Outcome scoring and audit trail
Open Models, Deployed Privately
Run open-source models on infrastructure you control, with narrow responsibilities and local access to approved capabilities instead of unrestricted access to company data.
- Prompt libraries with context
- Reusable agent configurations
- Known pitfalls and fixes
- Versioned team playbooks
Governance
Admins control what each role can see, which local actions AI can take, when approval is required, and what is recorded for audit.
- Role-aware access
- Versioned organizational rules
- Analytics for adoption gaps
- Private VPC deployment path
It starts where AI work already happens.
A developer starts a task in Claude, Codex, Cursor, VS Code, or GitHub. PandaBear captures the prompt, files, ticket, branch, diff, and local context as work happens.
The local layer connects context to capability.
PandaBear resolves the request against approved company context, policies, and deterministic tools. Sensitive records and credentials remain inside the customer environment.
Employees get a governed path to action.
PandaBear returns the relevant answer or routes an approved local action. External models, when used, receive sanitized intent and status signals—not raw company data.
Production readiness
Built beyond demo quality.
PandaBear separates intelligence from trust. Sensitive data, credentials, permissions, and execution remain inside the customer environment. When external reasoning is needed, it receives only sanitized intents and allowed status codes—never raw company data. That is how AI becomes governed infrastructure, not another shadow workflow.
Autonomy, with guardrails
Give employees room to build without giving up enterprise control.
PandaBear helps employees solve cross-functional problems through one governed interface while sensitive data, permissions, and execution stay under company control.
Fewer bureaucratic handoffs
Employees can reach approved context and capabilities without turning every operational question into another chain of meetings.
Entrepreneurial employees
The people closest to a problem can research, prototype, and improve internal workflows without waiting for a new central project.
Governed from the start
Leaders define access, approvals, and audit rules before AI touches a workflow, then expand only what proves safe and useful.
Inside the private AI layer
The pieces that keep intelligence useful and trust local.
VS Code / Cursor Extension
Captures task starts, prompts, selected code, active files, git state, and diffs from the developer environment.
Context Router
AI clients retrieve related history, known pitfalls, and generated repo rules through one adapter.
Knowledge Node
Stores events, queues durable work, extracts graph entities, and keeps AGENTS.md as a generated local mirror.
Governed memorySandbox Runner
Runs isolated checks, captures logs, and turns passing workflows into reusable evidence.
Governed memoryBusiness Intent Graph
Connects the why in tickets to the how in prompts, diffs, PRs, tests, and decisions.
Governed memoryEnterprise Controls
Role-aware memory, versioned rules, private deployments, and analytics for duplicated or missing capability.
Governed memoryDeployment
Start with one workflow. Expand a sovereign AI capability.
Innovation and transformation teams can prove value in a controlled pilot, then expand private AI across departments without changing the data boundary.
Private Foundation
Establish the local execution layer.
View the repo
- VS Code/Cursor capture client
- Prompt and workflow memory
- Codex capture wrapper
- AGENTS.md local mirror
- MCP tools for retrieval
Team Pilot
Prove one high-value workflow with the innovation team.
self-hostable
Start a team pilot- Everything in Open Source
- Knowledge Node event API
- Redis queue + graph worker
- MCP for Claude, Codex, Cursor
- Generated rules and repo memory
- Sandbox evaluation path
- Jira, docs, and chat integration roadmap
Enterprise Node
Expand governed AI inside the customer environment.
private cloud
Plan private rollout- Everything in Team Pilot
- Dedicated Knowledge Node
- Role-aware governance
- Org analytics and duplication maps
- Customer data isolation
- Sandbox worker scaling
- GitHub, Jira, docs, and chat integrations
Company Operations
Turn cross-functional friction into forward motion.
PandaBear works inside the systems teams already use. It connects the relevant context, policy, and local tools so employees can build internal solutions instead of spending days chasing answers through departments.
What could your employees build if the organization’s context was available when they needed it?
“The future of work is autonomous. Enterprise trust must remain intact.”
PandaBear gives employees more agency while keeping data, permissions, and execution under company control.
PandaBear separates intelligence from trust.
Open-source models, credentials, raw data, permissions, and execution stay inside your environment. Any approved external reasoning receives only sanitized intent and constrained status signals.
Plan a private deploymentQuestions buyers will ask.
Who should lead the first PandaBear deployment?+
Innovation and transformation teams are the natural entry point. They can select one cross-functional workflow, define the governance boundary, and prove measurable value before expanding.
Why is this more than another enterprise chatbot?+
PandaBear does not stop at an answer. It connects employee intent to approved company context, policy checks, and deterministic local tools that can move work forward safely.
How does it prevent company data from leaking?+
The local execution layer keeps credentials, raw records, permission checks, and tool execution inside the customer environment. External models receive only approved, sanitized requests with constrained outputs.
Where does it start?+
Start with one painful, cross-functional workflow where employees lose time gathering information or approvals. Prove the private deployment and governance model there, then expand.
Give employees the agency to build. Keep enterprise control intact.
PandaBear helps organizations adopt more autonomous ways of working without compromising privacy, governance, or the trust employees need to act.