Co-CaptainThe AI platform for data professionals
Connect to the systems you already use. Teach the platform how your company defines its metrics. Get answers that hold up to a finance review.
Most AI tools are built for general questions. Co-Captain is built for the numbers your team gets held to.
query_opportunitiesSample output
Sample executive deliverable
A Q3 QBR rendered the way Co-Captain renders one against your data. Branded automatically. Shareable as a URL or paginated PDF.
From raw data to a number you can defend
Four capabilities that take a question from your data stack to an answer your team will sign their name to.
Teach the platform how your business defines things
- Agents apply the rule automatically, so no one has to track which dashboard uses which version
- New hires inherit institutional knowledge without a multi-week onboarding doc
- When a question falls outside the rules, the agent says so instead of guessing
query_opportunitiesupdate_rule_setEvery answer reflects what is in your warehouse right now
- Connect once, reuse across every agent and workflow
- Permissions follow the user. People only see the rows they could see in the source system
- New sources can be added through MCP without a custom connector
query_opportunitiesquery_warehouseA dashboard the team can talk to
- Mid-market segment closed $1.01M in W8, the strongest week this quarter.
- Pricing v3.1 reclassified two early-stage opps. They were pulled from QTD per the RevOps rule set.
- Three deals slipped from W7 to W8. None at risk per the latest call notes.
- Pipeline coverage is 3.6x, 0.6x above the quarter target.
A live dashboard. Drag panels by their header. Resize from any corner. Refresh cycles the data so you can see what changes between agent runs.
Every number explains itself
- Built from the same chat, so Rule Sets and data sources stay consistent
- Refresh re-runs the agent on the same context. The dashboard and its layout are untouched
- Every refresh writes lineage, so reviewers can audit how each number was pulled
Closed-won deals as a share of attempts, broken out by segment, over the last six quarters.
Chain the work. Run it on a schedule.
- A visual graph of what ran, what failed, and what was retried
- Mix agents, transforms, and deliverable steps in any order
- Runs on a schedule. You get the result, not a babysitting job
A weekly executive-brief workflow. Runs Sunday at 6pm, every node logged.
See exactly why every answer landed the way it did
Every tool call an agent makes is captured with a one-sentence reason and the parameters used. The Rule Sets it pulled, the data it queried, the customer record it looked up.
Deliverables and chats render the full run as a graph. Reviewers can scan the chain, click any step to see its raw input and output, and trust the result without re-running it themselves.
- Every call carries a reason that explains why this tool and why these arguments, surfaced at review time
- Parallel tool batches stack inside one round-box. Sequential rounds chain with a single edge
- Deliverables cite the exact tool calls that shaped them. One click jumps to the source
A finished agent run. Calls the model made together get grouped. The strip below shows the running step's reason.
Outputs your stakeholders will open
When an agent finishes, the output lands as a branded document with your logo, typography, and colors. Built for the executives who don't want to open a chat to read your work.
- Brand kit applied automatically. No design tool required.
- Scheduled deliverables arrive in the inbox before the morning meeting
- Every chart links back to the query that produced it
save_deliverablecreate_scheduleSee it on your data
A 30-minute demo. We'll walk through the integrations you'd connect first and sketch the Rule Sets your team needs.