Your Team Spent 14 Hours This Week on Tasks AI Could Handle in 14 Minutes

A chatbot responds to messages. An AI agent reasons about a task, calls external tools -- APIs, databases, file systems -- and executes multi-step workflows on its own. We're talking booking appointments, processing refunds, generating reports, triggering real actions. Not just replying with text.

We're model-agnostic. Most projects end up using a mix -- GPT-4o or Claude for complex reasoning, something lighter like GPT-4o-mini for simple classification steps. Our architecture lets you swap models per workflow step, so you're balancing cost and quality at the same time. We also support self-hosted models via Ollama or vLLM if you need to keep everything on-prem.

Three layers. Structured tool calling with typed schemas forces valid outputs. RAG pipelines keep responses grounded in your actual data. Human-in-the-loop checkpoints catch edge cases before high-stakes actions execute. On top of that, automated eval suites flag accuracy regressions before every deployment goes out.

Yes. Agents can connect to anything with an API -- CRMs, ERPs, databases, email platforms, payment processors. We build typed tool definitions for each integration with proper authentication, rate limiting, and error handling baked in. No API? We can build one, or use browser automation as a bridge.

A focused single-workflow agent typically takes 4-6 weeks from kickoff to production. Multi-agent systems with several tool integrations and approval workflows usually run 8-12 weeks. Every project includes a 30-day post-launch window for prompt tuning and performance optimization.

When data sensitivity demands it, we deploy entirely on your infrastructure. For regulated industries, agents can run inside your VPC with nothing leaving your network. We support private LLM endpoints, encrypt all data at rest and in transit, and give you full audit logs of every agent action and tool call.