Build Intelligent Systems That Actually Work

Traditional automation follows fixed rules — if X happens, do Y. AI agents reason through problems. They can handle ambiguous inputs, decide which tools to use, recover from errors mid-task, and adapt when the situation changes. The practical difference: rule-based automation breaks the moment something unexpected happens. Agents handle the unexpected as part of the job.

A focused proof of concept — something that proves the core idea works — typically takes 2 to 4 weeks. A production-ready system with proper error handling, monitoring, and cost controls is usually 8 to 16 weeks depending on complexity. The gap between demo and production is where most projects stall; we build for production from week one so there's no expensive rework later.

All of them. We don't have a preferred vendor. The right model depends on your latency requirements, cost targets, data privacy constraints, and the specific task. We've deployed GPT-4o for reasoning-heavy workflows, Claude for long-context document analysis, and open-source models like LLaMA and Mistral for on-premise deployments where data can't leave your infrastructure.

You can't eliminate hallucinations entirely, but you can engineer around them. We use retrieval-augmented generation to ground responses in verified data, output validation layers to catch errors before they reach users, and human-in-the-loop checkpoints for high-stakes decisions. Every system we build has explicit fallback strategies for when the model gets it wrong.

Yes — and that's usually the right approach. Most AI value comes from augmenting what already exists: adding an AI layer on top of your CRM, plugging an LLM into your existing document workflow, or adding a reasoning step to an existing pipeline. We assess what you have, identify the integration points, and build the AI layer to fit your current architecture.

We start with a short discovery sprint — usually one week — to understand your data, your users, and the specific problem. Then we build a working prototype before committing to a full build. This catches bad assumptions early. From there, we work in two-week cycles with continuous deployment, so you see real progress every sprint rather than waiting months for a big reveal.

AI systems need active maintenance in ways traditional software doesn't. Model APIs change. Data distributions shift. New edge cases surface in production. We offer ongoing retainer arrangements that include monitoring, prompt tuning, cost optimization, and model upgrades as better versions become available. We can also hand off with full documentation if you prefer to manage it in-house.