Start with a baseline
Test an existing model with appropriate instructions, tools, and reference material. Identify where it fails before deciding to train.
POST-TRAINING & DOMAIN AGENTS
AI trained for
your line of work
We post-train models and build agents for your workflows, tools, and standards. Your industry knowledge becomes capability your customers and teams can use.
Discuss an agent projectAGENT DEVELOPMENTPVG / FIELD STUDY
Knowing an industry
isn’t enough
A useful agent must act within your constraints. A dispatcher needs a feasible route. An accountant needs a reconciliation that balances. A maintenance team needs a recommendation grounded in equipment history.
We combine domain expertise, task design, model training, and evaluation. Gaia connects that work around environments where agents can practice without experimenting on live customers.
We aim for measurable improvement in a specific job: fewer errors, more completed tasks, less review, or lower operating cost. We agree on those measures with your team.
TRAINING APPROACH
Use the method
that fits the problem
Learn from examples
Supervised fine-tuning can teach repeatable behavior from curated demonstrations: how to use a tool, follow a workflow, or produce a required output.
Learn from outcomes
Reinforcement learning can improve behavior through repeated attempts when an environment provides a reliable way to judge results.
Test on unfamiliar work
Keep evaluation tasks separate from training. Compare quality, failure patterns, latency, and cost before choosing a model for deployment.
Your benchmark.
Built around your business.
Know which agent works best for your customers. We build a reusable benchmark from real jobs in your industry: a repair to plan, a shipment exception to resolve, or an account to reconcile. Your experts help define what a good result looks like.
You receive test cases, scoring criteria, and baseline results, so your team can compare new models, check upgrades, and track progress over time. Quality, speed, and cost are measured against work that matters to your business.
These tasks stay separate from training. Each comparison uses a consistent test version, so a higher score reflects better performance, not an easier test.
Explore industry benchmarksMore than a model checkpoint
Workflow and data preparation
A defined task, approved source material, examples, and evaluation cases reviewed with domain experts.
Training and evaluation
Model selection, a task-appropriate training approach, and comparisons against a baseline. Findings include limitations, not just a headline score.
Deployment planning
Tool access, human review, monitoring, and a route back if performance falls short. Hosting and infrastructure choices are scoped for your requirements.
Further improvement
Use reviewed failures and feedback to inform another training cycle. Agree on ongoing support and data use rather than assuming either.
Your product stays yours
An agent can support internal work, run alongside existing software, or become part of an integration your engineers approve. We agree on deployment responsibilities, ownership, usage rights, and access before implementation.
You don’t need to commit to a product rewrite or an open-ended research effort. Begin with one workflow and a clear evaluation.
Bring us a workflow