AI-Powered Predictions

Depends on the task. Some problems work with hundreds of labeled examples; others need millions. We assess data readiness first and often help you collect or augment what you have before committing to a model.

Yes. We integrate with databases, data warehouses, APIs, and CSV exports. We also help modernize infrastructure where it's holding back AI adoption.

Proof of concept in 2–4 weeks. Production deployment in 6–12 weeks. We validate fast before committing to full-scale build.

We define accuracy targets before starting and use held-out validation sets to test honestly. If performance targets aren't met, we iterate — or tell you upfront if the problem isn't solvable with the available data.

Most engagements start with a fixed-scope proof of concept in the $8k–$20k range, with full production builds typically $25k–$80k depending on data complexity, model type, and integration work. We scope every project with a fixed quote after a free discovery call — no open-ended hourly billing.

Yes. Alongside classical ML and analytics, we build LLM-powered chatbots, RAG systems, and AI integrations using OpenAI, Claude (Anthropic), and open-source models like Llama 3. We default to Claude for long-document and support use cases, but pick the model per task based on latency, cost, and privacy needs.

Yes. We encrypt data in transit and at rest, enforce role-based access, and can run models inside your own cloud or on-premise so nothing leaves your environment. For regulated work we align to GDPR, HIPAA, and SOC 2 controls and sign NDAs before any data is shared.