Voice agents
Agents that answer calls, hold a real conversation, and act on the outcome in the systems behind the business.
Explore Venaio Voice
Realtime
Telephony
Node
Building AI systems that run in production
AI agents that do the work, not the demo
Explore AI agents, assistants, automation, and software engineering with Venaio.
What we build
AI that is wired into a real product and real data — not a chat window bolted onto a website.
Custom assistants & chatbots
Assistants grounded in your own documents and data, so answers come from your business rather than the model's imagination.
RAG
Vector DB
Python
Workflow automation
The repetitive judgement work — triage, routing, extraction, follow-up — handed to agents with humans kept on the decisions that matter.
Agents
Queues
Webhooks
AI inside your product
Adding intelligence to software that already exists and already has users, without destabilising what works.
APIs
Evals
Guardrails
Product engineering
The application around the intelligence — mobile, web and the backend that carries it. Most AI work fails here, not at the model.
Flutter
React
Node
Rails
Infrastructure & cost control
Cloud, deployment, monitoring and the token bill — because an AI feature that works but costs more than it earns is not shipped.
AWS
Azure
Docker
How we work
Short cycles and working software early.
Technical call
Discuss the problem and what you want to achieve.
Narrow the problem
We find the smallest version that proves the idea works, and tell you what to leave out.
Ship and measure
Into production behind evals and monitoring, so you can see whether it is actually working.
The practical details
Before you book the call
Straight answers to the questions technical teams usually ask first.
What happens to our data?
It stays yours, in your accounts. We design around sending the model the minimum it needs, and we will tell you plainly which parts of a design send data to a third-party provider before we build it.
Which AI models do you use?
Whichever fits the task, cost and latency budget — and we build so the model can be swapped. Tying a product to one provider is a risk, not an architecture.
What if AI is the wrong tool for our problem?
We will say so. Plenty of problems sold as AI are better solved with ordinary software that is cheaper to run and easier to reason about.
AI development guides
Practical guides to AI agents, applications and automation.
AI Agent Examples: When an Agent Helps and When a Workflow Is Enough
Practical AI agent examples, when fixed workflows suffice, and how to limit agent permissions.
AI App Development Cost: A Practical Estimation Framework
Estimate AI application costs with a worked example and a checklist of build and operating expenses.
AI Voice Agents: How They Work and How to Choose an Architecture
How AI voice agents work, how to choose an architecture, and what to test before launch.
AI Workflow Automation: Where to Start and Where to Keep Human Review
Choose a workflow for AI automation, separate validation from action, and plan human review.
Retrieval-Augmented Generation: How RAG Works and Where It Fails
How retrieval-augmented generation works, with a policy assistant example and common failure modes.
Start a conversation
Tell us what you want to build
Have an idea for an AI agent, voice experience or software product? Share the problem you want to solve.
A good place to start
- What you want to achieve
- Who will use it
- Any systems or data involved
No complete brief? A few sentences are enough to start.