It’s clear users are hungry for agentic tools — but AI agents like OpenClaw have shown how disastrous they can be when hastily rolled out or improperly executed.

Quill, an AI startup, hopes to do better with what it calls “a chief of AI staff,” Quilliam. Rather than just transcribing meetings or logging Slack conversations, the agent plugs into collaboration tools to gather context and help users take action.

Importantly, unlike OpenClaw, Quill says it has adopted a policy of security by design: Data is private, stored locally, and users have complete control over what it does and where it goes.

“OpenClaw is like the ultimate in letting go. It’s a little bit of gambling,” said Michael Daugherty, Quill founder and CEO. By contrast, Quill is “proactive AI, with humans in control.”

Plugging into workflows, remembering context

Quill estimates that today’s professionals spend 75% of their day collaborating with colleagues in calls, virtual meetings, and messaging. But ultimately, they don’t know what to do with all the information they record or log, “because no one goes back and reads 10,000 words for an hour to understand what happened,” Daugherty noted.

Quilliam seeks to address this by connecting to numerous tools — including Slack, Notion, Salesforce, Gamma, Linear, Affinity, Obsidian, Airtable, Manus, and others — via Model Context Protocol (MCP).

Because it has persistent contextual memory, the agent can learn over time to make useful recommendations, automate workflows, and craft templates, emails and other documents based on user histories and preferences. “AI is most powerful when it has more and more context on you, your goals, your contacts,” said Daugherty. He added that “a single summary is almost never the right solution for everyone.”

Naturally, though, enterprises and users may be concerned about how they can remain in control of their data. Addressing this, Quill is “local-first with options,” meaning transcription and speaker recognition run on-device and audio never leaves that environment. The agent never stores data, and enterprises have access to configurable endpoints to ensure zero exposure.

For instance, a research agent can develop a four-step plan and request access to three specific tools to execute on it. The human user can approve or not, and the agent is granted access only to specific tools while it’s executing; that access is revoked once it’s completed.

Whereas with OpenClaw, “you set it up, you have it do stuff; maybe it’ll start a business and make you a million dollars, maybe it’ll delete your hard drive,” Daugherty noted. Quill is more, “‘Let me do some research. Let me create a plan. Let me show you what I’m going to do, and you approve it.’”

Playing at home or away

With Quill, users can do what they want with their data and choose the best integrations. AI inference can run in enterprise cloud providers (such as Google Vertex or AWS Bedrock, both Quill partners) or local models for those requiring fully isolated environments. This can be particularly appealing for companies in regulated industries such as financial services, healthcare, or critical infrastructure.

Quill’s backers are committing more than just cash. Clayton Bryan, a partner at 500 Global and self-described Quill superuser, uses the tool to triage investment opportunities, and has seen significant time savings with the platform. In fact, he’s joining the startup as head of enterprise because he believes so strongly in the product.

500 Global gets anywhere from 7,000 to 8,000 applications twice a year for its accelerator program. Bryan then makes 500 to 600 follow-up calls and 100 to 200 ensuing interviews.

“I would have six weeks where my calendar looked like a losing game of Tetris, it would just be meeting after meeting after meeting,” said Bryan. Previously his Apple notes would be flooded; but now, Quill presents all of his meetings in one interface.

Further, after initial calls and interviews, Bryan would spend about a week identifying rejections and sending rejection emails. Quill, though, can cull through applications in a matter of three hours, using context from Bryan’s meetings and 500 Global’s criteria, pulling out applicants that aren’t a good fit. It can then craft rejection emails.

All of this has reduced Bryan’s time weeding out rejections by 20 hours or more. “It has saved me tremendously,” he said.

Daugherty, for his part, said Quill has helped him get more done at higher quality. For instance, he admits he’s “very bad” at following up on sales calls. But Quill created a template that automatically populates post-call so Daugherty can quickly send follow-ups.

As generative and agentic AI roll out in the workspace, users are essentially becoming agent managers, he said, adding that he may bounce between five different Claude instances at once. Quill’s goal is to provide as much context as possible across this agentic environment so humans can focus on strategy and higher-level execution.

“It’s more than just having a conversation, getting a generic note, and saving it somewhere,” said Daughtery. “It’s helping you be the version of yourself that is good about follow ups, executes on the things you promise, helps you to feel smart and connected.”