"Agentic AI" has become one of those phrases that can mean almost anything. For us it means something specific: software that uses an AI model to do a real task inside a real workflow, not just answer questions in a chat box.
We build agents for other companies, but we started by building them into our own products. That way, when we recommend an approach to a client, it is one we already run ourselves. Here are three of them.
1. Finio: an assistant that handles the paperwork
Finio is our personal finance app. Its AI assistant does three jobs that people usually put off:
- Reads receipts, so expenses don't have to be typed in by hand.
- Flags unusual spending, so something out of the ordinary gets noticed.
- Drafts financial summary letters, so a person starts from a draft rather than a blank page.
The word that matters in that last point is drafts. The agent does the slow part, and a person stays in charge of what actually goes out.
2. BrahmAI: several models on the same question
BrahmAI brings 43 AI models from 13 providers into one workspace. Its "Council" feature puts several of those models to work on the same question, side by side.
Different models are good at different things, and they don't always agree. Seeing their answers next to each other makes it easier to judge which answer to trust, and which model suits which kind of task. That habit of comparing before committing is one we bring to client work too.
3. Just Layla: a voice agent with memory
Just Layla is a voice-first AI companion that remembers you across conversations. Memory is what turns an assistant from a clever demo into something people come back to. It doesn't have to start from zero every time.
Memory also comes with responsibility. If an agent remembers things about people, you have to be deliberate about what it stores and who controls it. That question belongs at the start of a project, not the end.
What we look for before building an agent
Across these products, and in conversations with companies, the same questions keep deciding whether an agent is worth building:
- Is the task real and repeated? Agents earn their keep on work that happens often: answering the same kinds of requests, reading the same kinds of documents, supporting customers through the same steps.
- Is "done" clear? If you can't say what a good result looks like, you can't check whether the agent is producing one.
- Where does a person stay in the loop? Drafting, flagging and suggesting are often the right first step, before anything acts on its own.
- Does it work inside the tools people already use? An agent that needs a new habit is far harder to adopt than one that fits into existing tools.
- What data does it touch? Decide up front what it can read, what it keeps and where that data lives.
How we work with companies
We follow the same three steps every time:
- Discover. We look at your workflows and pick the task where an agent would make the clearest difference.
- Pilot. We build a focused version on real work and measure it against the "done" we agreed.
- Production and support. We take what worked into everyday use and keep it running.
If there is a task in your business that people spend too much time on, tell us about it. We'll tell you honestly whether an agent is the right answer.