The agent control plane for AI-native companies
digisquad builds a knowledge graph from your repos, documents and websites, turns it into runbooks for winning customers, and puts your people and agents on one record. It works with the agents you already use.
Control plane
Routine work runs within limits you set. Consequential decisions come to you. Every agent has a guardian, the person answerable for it, and a trust ladder sets what needs approval and what just gets tracked. You stay in control without approving every step.
Approve the partner outreach experiment?
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Your agents
digisquad gives them your context and your way of working, and brings what they do back to one record. You don't have to move your work or switch tools.
Brain and vault
The central store for your institutional knowledge. It keeps the memories from all your AI tools and carries context into and out of every agent conversation.
Indexes and curates your organization's documents, repos and files into one knowledge graph. It feeds your agents skills, runbooks and best practice, and takes in and organizes everything they produce.
Knowledge graph
Bring your repos, documents and websites. digisquad turns them into one knowledge graph, keeps track of where each fact came from, and brings conflicts to you as simple questions.
What you have
What it finds
Annual plan: $490
Pricing page, updated in March
Annual plan: $590
Sales deck, updated in June
What it asks you
Which price is current, or do they apply to different customers?
Newer doesn't automatically mean right. You decide.
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Provenance and governance
Every fact keeps its source. Every agent and teammate works within the access you grant. Everything done through digisquad is logged: who did it, what they used, and who accepted it. New information waits for review instead of overwriting what you've verified, and you can restore an earlier version at any time.
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Skills and runbooks
Start from a library of skills and runbooks: best practices and standard operating procedures for specific work. Adapt them to your organization, with every version kept. Each project and experiment teaches you something, and once you approve the lesson, it reaches the people and agents doing that work.
From the library
The starting best practice
Adapted by Maya
Added your qualifying questions
Lesson approved by Sam
Shorter first messages, from your last experiment
Now used by 2 teammates and 3 agents
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Continuous learning loops
Run customer acquisition experiments against your goals, with measurement built in. See what you spent, what happened and how complete the data is. When an experiment earns it, graduate it into an ongoing campaign with its own goals, budget and governance, and keep the full history.
1,000 new subscriber signups
596 so far, from 2 experiments
X Ads 15-day campaign
Meta Ads 20-day campaign
Meta Ads signs people up for less. What should happen next?
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Who it's for
Founded AI-native, with small teams getting real work done through agents.
Small groups using AI to solve real business problems, growing like startups and taking on more of the core business over time.
Your shared context, skills and best practices travel to every agent you connect, across tools and platforms. You never start from the beginning, and every round of work starts stronger than the last, with your people making the decisions.
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