Service
AI Agents — ai agents for product teams.
An AI agent is a software assistant that uses a large language model to understand a goal, decide which actions to take, and complete a specific job — answering customer questions, triaging requests, or operating inside your product. Upgalax builds custom AI agents that are grounded in your data, constrained to your rules, and measured against real outcomes.
What it is
An AI agent is a software assistant that uses a large language model to understand a goal, decide which actions to take, and complete a specific job — answering customer questions, triaging requests, or operating inside your product. Upgalax builds custom AI agents that are grounded in your data, constrained to your rules, and measured against real outcomes.
AI Agents
- Best for
- Customer-facing or product-embedded assistants
- Typical timeline
- 3–6 weeks
What's included
Everything a ai agents build ships with.
- Agent persona, scope, and behavior design
- Retrieval over your documents and knowledge base
- Tool use for actions inside your product
- Evaluation suite to measure accuracy and tone
- Analytics on conversations, deflection, and satisfaction
Our process
How we build ai agents.
- 01
Define the job
We pin down the single job the agent owns, its boundaries, and what "done" looks like in measurable terms.
- 02
Ground it in your data
We connect your docs, knowledge base, and product APIs so the agent answers from facts, not guesses.
- 03
Evaluate and tune
We build an evaluation suite of real questions and edge cases, then tune until accuracy and tone meet your bar.
- 04
Launch and measure
We ship with analytics on deflection, satisfaction, and failure modes so the agent keeps improving.
FAQ
AI Agents questions, answered.
How is a custom AI agent different from a chatbot?
A basic chatbot follows scripts or answers from a fixed FAQ. A custom AI agent understands goals, retrieves from your live data, takes actions through tools, and adapts its responses — while staying inside the rules you set.
Can the agent take actions, or only answer questions?
Both. We design agents that can read and write — drafting replies, updating records, triggering workflows — always behind allowlists and approval steps for anything irreversible.
How do you keep the agent from making things up?
We ground every response in retrieved source material, constrain the agent to verified tools, and run an evaluation suite of real questions before launch so hallucination is measured and minimized, not assumed away.