BotanicsAcademiaTruleaf Biologicals R&D
Research systems by Truleaf

The AI workspace for R&D development

Meet Kwáu

Put AI to work across your R&D operation without commissioning an internal software project.

Explore Kwáu Private by designBuilt around your workflows
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Kwáu for R&D

Your startup is here to build new science. The AI infrastructure already exists.

What Kwáu changes

Four parts of a working research system

01

Recurring research becomes a reusable workflow

Turn literature reviews, evidence extraction, technical comparisons, and research preparation into workflows your team can refine, rerun, and improve with every project.

02

Project knowledge stays available

Keep documents, context, decisions, and corrections together instead of restarting from scattered files and chat histories.

03

Every output can be inspected

Sources, assumptions, and review points remain visible so specialists can challenge the work and decide what the evidence supports.

04

The software grows with the research

Add new R&D workflows without launching another internal build or creating a separate tool for every use case.

Agentic by design

Research work, coordinated.

Kwáu gives people and specialized agents one shared project environment. Work remains visible, reviewable, and available to the team wherever it operates.

01
Live collaboration

Researchers work together in the same project context as it changes.

02
Cloud projects with a complete timeline

Project files, changes, and decisions stay versioned and available in the cloud, with Git and GitHub maintaining the underlying history.

03
Agents help with setup

Agents organize the workspace, prepare context, and establish repeatable workflows.

04
Multi-agent orchestration

Specialized agents divide complex work, exchange results, and move it toward review.

05
In-browser access

Open the project and continue the work without depending on a single workstation.

One shared research environment

People decide. Agents prepare the work.

Kwáu keeps scientific judgment with the team while agents collect context, coordinate tasks, and prepare inspectable outputs.

From bottleneck to working workflow

The Kwáu launch

You choose

One real R&D bottleneck

We begin with work that already costs your team days or weeks and define what a useful result needs to contain.

We configure

Kwáu around the work

Your documents, inputs, review points, and expected outputs become part of a repeatable process inside the application.

We test

A real or historical project

The team compares Kwáu with the current process, examines the reasoning, and identifies where human review remains necessary.

You operate

A capability your team can reuse

The launch ends with working software, team onboarding, and a defined route to the next R&D workflow.

One workflow deployed. Knowledge retained. The next project starts ahead.

A clear working boundary

Your science stays yours.

Kwáu provides the research software. Your team keeps the science, judgment, and market advantage that make the company different.

Your data

Your documents, experimental records, and confidential project materials remain private to your organization.

Your outputs

Your reports, analyses, decisions, and project results belong to you.

Your review

Specialists remain responsible for interpreting evidence and approving what enters the research process.

Kwáu software

We maintain the application and its general workflow framework so you do not need to build or operate that layer.

Kwáu partner edition

Offer Kwáu under your own name.

For consultancies, research platforms, and technical providers that already serve R&D teams. We maintain the core software while you bring the customer relationship, market knowledge, and service layer.

Your brandIdentity, domain, and client-facing experience.
Your customersYou lead the commercial relationship.
Our maintained coreOne product, updated without partner-specific forks.
Custom developmentPaid workflows, integrations, and deployment work.

Start with evidence

Bring one R&D workflow that takes too long.

We will map the current process, configure it in Kwáu, and test it on work your team can judge.