AI post-training startup Bespoke Labs raises $40M in funding

Bespoke Labs Inc., a startup working to streamline the post-training phase of artificial intelligence projects, has raised $40 million in funding.

The company stated today that the capital arrived in two tranches. Bespoke Labs raised the bulk of the funds, $31.75 million, through a Series A round led by Wing VC. The firm was joined by Mayfield, The House Fund and employees at major tech firms such as Anthropic PBC. Bespoke earlier raised $8.25 million from a consortium that included Google DeepMind chief scientist Jeff Dean.

The workflow through which developers build a custom AI model comprises two main steps. The first is the pre-training phase, which equips the neural network with the core skills and knowledge it requires to answer prompts. The second phase, post-training, hones the model’s reasoning skills. It can also provide improvements in other areas such as long-horizon task completion.

Developers often carry out post-training using a method called reinforcement learning. The basic idea is to provide an AI with  sample tasks similar to the work it will carry out in production. When the model completes a sample task correctly, it receives a “reward.” The reward is a piece of data that adjusts the algorithm’s configuration to boost its output quality.

Reinforcement learning is carried out in virtual environments tailored to each project. For example, a productivity agent might be trained in a sandbox that simulates employee workstations. A coding agent, meanwhile, may require a simulated GitHub repository.

Bespoke Labs offers a platform that makes it easier to create reinforcement learning environments. According to the company, the software generates simulations using automation workflows and input from a network of human experts. It claims that the platform does so significantly faster than traditional manual approaches.

The platform runs the AI environments that it generates using what Bespoke describes as a sandboxing layer. The latter component helps minimize latency and boost throughput.

The platform’s third core component automatically optimizes the output quality of the AI models being trained. One of the technologies that it uses for the task is GEPA, an open-source project Bespoke released last year. The software automates prompt engineering, the process of finding the specific requests and prompt formats that maximize an AI model’s output quality.

Reinforcement learning is not the sole focus of Bespoke’s open-source work. The company is also prioritizing another popular post-training method called supervised fine-tuning, or SFP. It works by providing AI models with a set of sample prompts and answers that they can use to refine their output.

Assembling SFP question sets can be a highly time-consuming process. Last January, Bespoke released a dataset called OpenThoughts that contains more than a million sample prompts and responses. The company says OpenThoughts provides better post-training results than earlier SFT datasets.

Bespoke Labs will use its newly raised capital to enhance its reinforcement learning platform and finance more AI data research.

Photo of founders: Bespoke Labs

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