Synth AI provides a Managed Research platform that automates end‑to‑end AI research workflows on real code repositories. It provisions containerized workspaces, runs verification and evaluation tasks, and captures durable artifacts so experiments are repeatable, inspectable, and can serve as the basis for subsequent runs. The service supports public Open Research for quick exploration and private, credentialed projects for proprietary work.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI research and engineering tasks on real code repositories often require manual setup, ad‑hoc scripts, and fragmented tooling, making experiments hard to repeat, verify, and scale. Teams struggle to track progress, capture evidence, and reuse context across runs, leading to wasted effort and unreliable results.
Solution
Synth AI offers a Managed Research platform that automates end‑to‑end research workflows on actual codebases. Users define objectives, launch runs via an agent client, SDK, or web UI, and the system provisions hosted workspaces, executes verification, evaluation, and data assembly tasks, and records durable artifacts. Progress is tracked through milestones, tasks, and a message queue that enables steering and operator communication without manual intervention. All outputs—including reports, model artifacts, and usage metrics—are stored and can be inspected through a unified interface, allowing each run’s results to serve as the starting point for subsequent experiments. The platform supports both public “Open Research” runs for quick exploration and private, credentialed projects for proprietary work.
Target Audience
Primary users are AI/ML engineers, research scientists, and development teams that need repeatable, inspectable research workflows on real code repositories, as well as organizations seeking to automate code‑centric AI experiments.
Features
- Objective‑driven run model with directed effort, general, and open‑ended discovery modes
- Automated provisioning of containerized workspaces and managed agents behind the scenes
- Durable progress tracking via milestones, tasks, and a message queue for real‑time steering
- Comprehensive evidence capture: reports, result files, model bundles, PRs, and usage logs
- Public Open Research interface for one‑hour/four‑hour exploratory runs without authentication
- SDK and MCP client integrations for scriptable, repeatable research pipelines
- Project abstraction to attach repositories, datasets, credentials, and reusable knowledge