This page preserves the original early-access URL while updating the purpose: DeepBrainz is looking for real human-agent workflows where AI can help produce useful, reviewable outcomes.
From early access to workflow discovery
The old co-writing program language is no longer the center of the product story. The modern question is broader and more practical: what work should an agent help a person complete, and what evidence would make the result trustworthy?
Who this is for
Founders, developers, engineers, PMs, researchers, analysts, and operators who have a repeated workflow that is slow, unclear, or difficult to verify.
What to send
Describe the workflow, the input you start with, what you do manually today, the output you want, how urgent it is, and whether you are open to a short customer-discovery conversation.
What carries forward
- Lexopedia workflows: market research, competitive analysis, monitoring, decision support, and technical synthesis.
- AgentFoundry workflows: debugging, code review, test generation, verification, approvals, and handoff.
- Labs workflows: evaluations, benchmarks, readiness analysis, and evidence review.
Related paths
- (/contact/)
- (https://www.lexopedia.in)
- (https://www.agentfoundry.in)
Additional depth for current readers
What changed from the original program
The original early-access page belonged to an older co-writing and assistant moment. The preserved URL now serves a better purpose: collecting real workflow evidence from people who are willing to show where agentic systems could help with actual work. That keeps the historical route authority while aligning the public story with the current product direction.
Useful participation
A useful note describes the work, not just the desired feature. Good inputs include the type of task, the material available at the start, the manual steps currently required, the expected output, the review standard, and the urgency. This helps DeepBrainz identify repeated patterns instead of collecting scattered opinions.
Examples of strong workflows
A founder may need market research turned into a decision memo. An engineer may need a failing test investigated with a clear patch plan. A PM may need competitive monitoring summarized every week. A researcher may need evaluation evidence organized into a readiness view. These examples are valuable because they produce inspectable outputs.
Human review remains central
DeepBrainz does not frame human-agent collaboration as unchecked automation. Sensitive work needs approval, evidence, and clear boundaries. The goal is to learn where agents can reduce effort while keeping people in control of judgment, risk, and final decisions.
What to do with this topic now
The useful question is whether AI helps with bounded work, visible outputs, and evidence a person can review.
For customer discovery, every claim should connect to real work. That work has a starting input, a person responsible for judgment, a sequence of actions, an expected output, and a way to review whether the result is good enough.
The practical adoption path is therefore simple. Start with a task that already happens. Capture what makes it slow or uncertain. Run a small agent-assisted version. Compare the output with the user's current process. Keep the evidence. Ask whether the user wants to run it again. That loop matters more than large public claims.
If this matches your work, start with a concrete problem: a research question, a software task, a monitoring need, or a decision that needs evidence before someone can trust the result.
What to do with this topic now
The useful question is whether AI helps with bounded work, visible outputs, and evidence a person can review.
For customer discovery, every claim should connect to real work. That work has a starting input, a person responsible for judgment, a sequence of actions, an expected output, and a way to review whether the result is good enough.
The practical adoption path is therefore simple. Start with a task that already happens. Capture what makes it slow or uncertain. Run a small agent-assisted version. Compare the output with the user's current process. Keep the evidence. Ask whether the user wants to run it again. That loop matters more than large public claims.
If this matches your work, start with a concrete problem: a research question, a software task, a monitoring need, or a decision that needs evidence before someone can trust the result.
What to do with this topic now
The useful question is whether AI helps with bounded work, visible outputs, and evidence a person can review.
For customer discovery, every claim should connect to real work. That work has a starting input, a person responsible for judgment, a sequence of actions, an expected output, and a way to review whether the result is good enough.
The practical adoption path is therefore simple. Start with a task that already happens. Capture what makes it slow or uncertain. Run a small agent-assisted version. Compare the output with the user's current process. Keep the evidence. Ask whether the user wants to run it again. That loop matters more than large public claims.
If this matches your work, start with a concrete problem: a research question, a software task, a monitoring need, or a decision that needs evidence before someone can trust the result.