AI-Based Smart Applications
AI agents, chatbots, RAG systems and voice assistants for teams in the USA, Canada and Australia — built safely, evaluated properly, and shipped as production software, not a demo.
Built around your workflows, not a template's
App Commandos designs and develops browser-based software for companies that have outgrown spreadsheets, disconnected tools, or generic off-the-shelf products. We translate real workflows into secure, maintainable applications that your team can operate and improve over time.
For AI work specifically, that means grounding every feature in your real data and permissions, evaluating it before it ships, and giving your team an escape hatch to a human whenever the model shouldn't be making the call alone.
Six problems AI is actually good at solving
AI Agents & Automation
Autonomous and semi-autonomous agents that read context, plan multi-step work, call tools safely, and hand off to a human when a decision needs judgment rather than a rule.
Custom AI Chatbots
Grounded, on-brand assistants that answer from your real data, hold account context securely, and route to a human the moment a conversation needs one.
RAG Knowledge Assistants
Retrieval-augmented systems that answer from your documents with citations, honour document-level permissions, and get evaluated before they ship.
AI-Powered SaaS Features
Multi-tenant products with model orchestration, usage metering, and knowledge permissions built in from the first migration, not retrofitted after the demo.
Voice AI Assistants
Real-time voice interfaces for customer and staff workflows, built for low latency, safe confirmed actions, and a clean handoff to a person.
AI API Integrations
Practical AI features added to an existing product — summarization, extraction, classification — with costs controlled and outputs validated before they reach a user.
What sits
behind the model
Streaming responses, typing indicators and voice interfaces built for how people actually talk to AI, not a chat bubble bolted onto a form.
Agent orchestration, tool calling and workflow logic that decides what the model is allowed to do, and checks its work before it acts.
Vector search for retrieval-augmented generation, chosen on latency, filtering needs and how much metadata you attach to each embedding.
Model selection weighed on cost, latency, context window and data-handling terms, with a fallback path when a provider has an outage.
How an engagement runs
Planning & Analysis
Requirements, user stories, technical specification, wireframing, database design and architecture planning.
Design & Prototyping
Mockups and interactive prototypes, with attention to user experience, accessibility and responsive behaviour.
Development
Clean, maintainable code under version control, with code reviews and automated testing throughout.
Testing & QA
Unit, integration and user acceptance testing, plus security audits and performance testing before release.
Deployment
CI/CD pipelines with staging environments, so final testing happens before anything reaches production.
Maintenance
Monitoring, bug fixes, feature updates and regular security patching once you are live.
Plan your AI
application
Share your users, workflows, data and launch goals. We can recommend a scoped build, a proof of concept, or a readiness assessment first.
