Domain-specific agents for software partners. Training data, environments, and benchmarks for teams building AI. One mission: bringing frontier AI to the real economy.
INDUSTRY KNOWLEDGE / FOUR OUTPUTSPVG / FIELD STUDY
Industry experts and approved workflow records provide the starting material.Gaia connects task design, practice environments, training, and evaluation.Deliver useful agents to partners and approved training products to AI teams.
01 / DOMAIN AGENTS
Post-training for the work you know
A service visit to prepare. A production plan to revise. A reconciliation to complete. We work with vertical software partners to train agents around the jobs their customers do, using approved data and industry expertise.
An engagement brings together task design, a baseline, a training approach, and an evaluated agent with documented limitations. Fine-tuning teaches from examples; reinforcement learning lets an agent practice against defined outcomes. Gaia connects the training environments and evaluation work.
Use the resulting capability internally, alongside your software, or through an integration your engineers approve. Deployment responsibilities, review requirements, and ongoing improvement are agreed with your team.
For model developers, AI product teams, and enterprises that have their own training stack. Scope a dataset around a task, its source material, and examples of useful work.
The delivery specification covers records, annotations, provenance, coverage, known limitations, and permitted uses. Demonstrations can support supervised fine-tuning; separately reserved cases support evaluation. Format and sample acceptance are agreed before a larger delivery.
Interactive environments recreate a scoped workflow, so agents can attempt tasks without experimenting on live customer systems.
An environment specification includes starting state, available tools, constraints, reset behavior, and checks on the outcome. Use it for reinforcement learning, tool-use development, or repeated evaluation. We agree on the interface and validate a representative task with your team before extending coverage.
Compare agents on your customers’ work, then check whether the next model or software change helps. A benchmark engagement defines held-out tasks, scoring rules, baseline runs, and failure analysis, with a versioned protocol for later comparisons.
Tell us what work you want an agent to handle or which capability you want to train. We’ll define a useful first delivery around that task.
For software partners, approved data and environment licensing can also generate recurring revenue under buyer agreements, shared on agreed terms. We agree on buyers and uses with you before sharing material.