Expertise
LLM Agents & Voice AI
An agent is a language model given tools, memory, and the ability to choose its own next step. The hard part is not making one work once, it is making it behave predictably on the thousandth call, and knowing when it should stop and fetch a person.
In practice
In practice: support lines that answer at 3am, systems that qualify an inbound lead before anyone reads it, and internal copilots that can act on data rather than just describe it.
What it gets used for
- Support that answers around the clock
- Lead qualification before a human reads it
- Voice agents for phone and IVR
- Internal copilots over private data
Stack
What we can scope together
- A bounded agent workflow with explicit tools and permissions
- Conversation or voice integration suited to the use case
- Evaluation cases, failure handling, and a human handoff path
Final deliverables depend on the agreed scope.
What the project needs from you
Start with representative conversations, the actions the agent may take, the tools it must use, and the situations that require a person. Voice work also needs an agreed approach to consent, recording, and escalation.
Keep people in control
Separate drafting from consequential actions. Give each tool only the access it needs, validate its inputs, and require approval for actions whose mistakes are difficult to reverse.
Do I need an agent or a chatbot?
If the job is answering from documents, a retrieval assistant may be enough. An agent becomes relevant when the system must choose among tools or execute a multi-step task. More autonomy adds evaluation and oversight work.
Define, build, evaluate, hand over.
We first agree on the workflow and acceptance criteria, implement a bounded version against representative inputs, review errors and edge cases, then decide what is ready for release. Deployment, documentation, ownership, and ongoing support are agreed explicitly.
Start with an architecture consultationWhere this shows up in the work
See it running
The projects and case studies show this work in production, with the architecture and the results.