The technology we use to build agents you can trust.
Plain-English explainers on the building blocks, and how we use each in production.
Voice AI lets an agent hold a natural spoken conversation, understanding a caller and responding in real time.
Speech to text turns a caller’s spoken words into text the agent can understand.
Text to speech turns the agent’s reply into natural spoken audio.
how software understands and works with human language.
the model that gives an agent its language understanding and reasoning ability.
RAG keeps an agent’s answers grounded in your data instead of its training memory.
the collection of documents an agent answers from.
Fine-tuning trains a model further on your data to specialize it for a task.
a numeric representation of text that lets an agent measure how related two things are.
A vector database finds the most relevant chunks of your knowledge quickly.
what an agent remembers within a conversation and over time.
how an agent breaks a goal into steps and works through it.
Computer vision lets a model interpret images and video.
OCR and document AI read text and structure from real-world documents.
An autonomous agent pursues a goal with minimal step-by-step human input.
Orchestration coordinates several specialized agents into one reliable workflow.
Automation engines run the multi-step logic that connects an agent’s actions.
how an agent triggers real actions in your tools.
an open standard for connecting agents to data sources and tools.
how we keep your data safe when agents act on it.
the set of policies and controls that keep AI use responsible and auditable.
Responsible AI means building and using AI in ways that are safe, fair and accountable.
Data privacy and residency give you control over how and where your data is handled.
Want the technical detail?
Our team walks your engineers and security reviewers through the whole stack.