Garden Research is an independent research institution working at the intersection of epistemology and organizational practice. We build the knowledge systems that teams and organizations rely on, in the conviction that rigorous thinking about evidence, method, and the quality of knowledge belongs far beyond the laboratory.

Artificial intelligence is accelerating research — rewriting peer review, generating hypotheses, synthesizing literature at scale. Agents already work alongside us, and they are rewriting how teams operate and how knowledge is stored and passed on.

The question of how we know what we know has stopped being purely philosophical. It is a live engineering problem, and one we help solve.

Garden Research was founded by Angelina Zaitseva, an epistemologist and researcher whose current work focuses on the agentic web layer and open-web access.

This is our research frame.

Here is what we work with in practice: the technical stack we build projects on. We build it ourselves, deploy it, and leave the team able to maintain and grow the system.

The applied areas above come together as projects, each with a clear deliverable. Below is a catalog of eleven: what we build, and what stays in your hands.

1. Process and project-management audit. A diagnostic of processes and project management through the lens of information and knowledge management — where the biggest potential for agent automation sits today. We look at how the organization creates, retains, and loses knowledge: a series of interviews, a review of tools and flows, metrics — time-to-find, reuse, coverage, leak points. The output is a map of the gaps and a roadmap: which processes to rebuild and automate first, and what that yields. Often this is the first step everything else starts from.
2. Digital office. The team's operating system: one place where projects, knowledge, decisions, and people stay current. Linked master databases, page templates, department spaces with their own permissions, dashboards without meetings, conventions, and automated reporting. We build it in Notion — or, for teams with IP and regulatory requirements, as a server-native office on your own infrastructure. Operational overhead drops from eight to ten hours a week to one.
3. AI agents and process automation. Agents that do repetitive work and reach into your systems — Notion, Slack, git, trackers.
4. Intelligent search across your data. A single semantic index over all your scattered sources, and agents for semantic work.
5. Competitive intelligence. A signal stream tuned to your vertical — competitors, regulation, science, capital movements — where every signal is checked for credibility before it reaches you.
6. Onboarding and a competency map. Experience and expertise move out of heads and chat threads into structure: who knows what, where decisions live, what has already been tried. New hires reach productivity faster, and the team's lessons stop leaving with the people who hold them.
7. Editorial pipeline. Content production and publishing as a flow: gathering material, assembly, review, release to a social-media digest or CMS. Editorial rules and tone are built into the pipeline, the agent drafts, a human approves. A weekly release goes from eight hours of human time to ninety minutes.
8. Scientific-literature monitoring. Tracking the science in your field — PubMed, bioRxiv, Crossref, arXiv — with synthesis and gap detection. For knowledge-intensive areas where falling behind the literature is expensive.
9. Integrations and custom MCP servers. The connective tissue between your tools: API and webhook integrations, custom MCP servers for your stack, so agents and systems can see each other. Whatever the off-the-shelf connectors lack, we write for you.
10. Sovereign infrastructure. Deploying the whole stack inside your perimeter: servers and models in the EU, data and history entirely in-house. GDPR by design, audit trails, work under NDA. For regulated industries and sensitive data, where this is a condition, not an option.
11. Educational programs. Programs the team keeps using after we leave: live workshops, recorded, with workbooks inside your own space. The output is your own conventions, templates, knowledge architecture, and a way to bring the next person in.