MangoDesk provides a cloud‑native platform for building and running custom reinforcement‑learning environments that replicate enterprise knowledge‑work workflows. It enables AI teams to execute large‑scale simulations, collect granular performance metrics, and integrate evaluation pipelines into ML‑Ops via CI/CD hooks, while offering multi‑tenant security and analytics dashboards.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises developing AI-driven knowledge‑work tools often lack production‑grade evaluation environments that reflect real‑world task dynamics, making it difficult to quantify model performance on meaningful use cases and to iterate quickly. This gap leads to prolonged development cycles and uncertain ROI on AI investments.
Solution
MangoDesk offers a cloud‑native platform that lets AI teams design, deploy, and run custom reinforcement‑learning (RL) environments mirroring their target workflows. Users define task specifications and reward functions, then execute large‑scale simulations that generate granular performance metrics tied directly to business outcomes. The platform aggregates results into a unified analytics dashboard, enabling data‑driven model tuning and rapid A/B testing. Built‑in CI/CD hooks allow evaluation pipelines to be integrated into existing ML Ops stacks, shortening the feedback loop from weeks to hours. Security‑focused tenancy and role‑based access ensure that proprietary data and models remain protected throughout the evaluation lifecycle.
Target Audience
Primary customers are AI product teams, ML engineers, and data scientists building models for enterprise knowledge‑work applications such as document automation, decision support, and workflow orchestration.
Features
- Visual editor and SDK for constructing domain‑specific RL environments with custom state, action, and reward schemas
- Scalable simulation engine that runs millions of episodes in parallel on GPU‑accelerated clusters
- Real‑time metric collection (e.g., task success rate, latency, cost per decision) with export to Prometheus or Grafana
- Automated CI/CD integration via RESTful API and Terraform modules for seamless inclusion in ML Ops pipelines
- Multi‑tenant architecture with end‑to‑end encryption and fine‑grained IAM policies for enterprise compliance
- Dashboard with cohort analysis, statistical significance testing, and model version comparison
- Exportable evaluation reports in JSON, CSV, and PDF formats for stakeholder review