Autonomous AI discovery built for real-estate platforms.
Your warehouse already holds the leads, the agent activity, the SLA logs, the contracts, and the listing changes. DecisionBox runs discovery against all of it overnight — and ships a ranked backlog before Monday's pipeline review.
Built for VP Operations, Heads of Sales, and Heads of Data at platforms moving leads, listings, and deals through a CRM.
Your pipeline report shows the funnel. Not the leak.
The patterns that move sign rate — the SLA bucket draining buyer contact, the channel feeding leads nobody works, the agent cohort quietly carrying the close rate, the listing type stalling at price reduction — only show up when leads, agents, contacts, listings, and SLA logs are read together.
DecisionBox runs them overnight. The agent decides what to investigate, writes the SQL, re-queries every number against your warehouse, and ships a ranked backlog of findings — each with the affected count, the target segment, and a numbered recommendation.
How the agent picks what to investigateWhat stays hidden when the questions don't get asked.
Three benchmarks every operations leader at a real-estate platform already accepts. The discovery loop runs against all of them in a single pass.
Three findings the agent surfaced on a real-estate platform.
One run on a platform's warehouse. The agent picked the analysis areas, wrote the SQL, re-queried every number, and ranked what to fix. Three shapes of finding — one cliff in a single table, one pattern across many, one anomaly hiding under an average.
Point it at your warehouse. See what the discovery loop finds before Monday's pipeline review.
Same data, five sets of questions.
One discovery run produces findings ranked for the team that owns the metric. Each panel is what the agent investigates in your warehouse — questions it picks on its own.
Where the funnel actually breaks.
Your pipeline report blends every lead source into one number. The agent reads each source against every conversion stage — and ranks where the volume is dying. No pre-built funnel, no ticket to the data team. Pair with Find revenue leakage.
- Which lead sources are wasting compute and never converting
- Where the conversion gap sits between agent-created and scraped leads
- Which channels quietly stopped paying back, by market
- Where the archive flag is hiding leads worth contacting
- Which segments show the steepest source-quality decay
Three properties your pipeline review can defend.
DecisionBox is built so that the findings on Monday's call are not the kind that fall apart under a follow-up question.
Sees what nobody had time to look for.
The agent picks the questions. It reads every lead source against every stage, every SLA bucket against every sign-rate outcome, every agent cohort against every close. The investigations that don't make it onto a Jira ticket happen anyway.
Every number you'd defend in the pipeline review.
An independent verification query re-runs every count from a different angle. If the agent can't reproduce its own claim, the finding is dropped or adjusted with the corrected number, visibly. The SQL trail stays attached.
Your warehouse. Your lead PII. Your infra.
Self-host with Docker Compose, Helm, or Terraform on GCP / AWS / Azure. Customer names, addresses, and contact logs never leave your environment. Open source under AGPL v3 — every line of the agent and the validation loop is on GitHub.