Qrambo turns the logic already embedded in spreadsheets into AI agents that run routine operational workflows, routing judgment calls to human reviewers for one-click approval. The platform connects to existing data stacks, keeps everything inside the customer's VPC, and improves accuracy by roughly 1.4 points per week as operators correct the AI. Pilots typically go live in about 30 days on a single high-volume workflow.
Funding
Funding not disclosed
Founders
Product
Problem
AI automation projects in operations teams frequently stall in pilot phases and never reach production, leaving teams to handle repetitive, high-volume workflows manually. The bottleneck is not the speed of individual steps but the serial chain of tasks requiring human involvement at every stage, which caps throughput and prevents scaling without proportional headcount increases.
Solution
Qrambo provides an AI agent platform that runs end-to-end operational workflows, processing every case optimistically and routing only the judgment calls to human reviewers for one-click approval or override. The system connects to a company's existing tools, data, and warehouse, with forward-deployed engineers integrating it into the stack and building the first flows. Every human decision feeds back into the model, improving accuracy continuously so the share of cases requiring human review shrinks over time. The platform operates entirely within the customer's VPC unless data sharing is explicitly permitted, and every decision carries a full audit trail for accountability.
Target Audience
Primary customers are operations teams in media, gaming, and entertainment companies handling high-volume workflows such as payouts, disputes, content moderation, and fraud checks, as well as any organization with spreadsheet-driven operational processes that need AI automation with human oversight.
Features
- AI agents process every case in parallel, eliminating serial handoffs and clearing routine work without human touch
- Human-in-the-loop approval workflow with one-click approve/override and median hand-off time of roughly 3-4 seconds for risky cases
- Continuous learning loop where every operator correction becomes a labeled example, lifting accuracy by approximately 1.4 points per week off a 78% baseline
- VPC-deployed architecture ensuring data never leaves the customer's infrastructure unless explicitly configured
- Full audit trail on every decision, logging what was requested, what the AI found, which rule applied, and who signed off
- Forward-deployed engineering team that integrates the platform and builds initial workflows, with most pilots live in about 30 days
- Volume calculator and interactive dashboards for tracking auto-resolution rates, human review loads, and accuracy trends