AI that solves operational problems, not AI for the sake of it
The situation
How we approach it
See this in practice
Common questions
Primarily Claude (Anthropic) for reasoning, analysis, and generation. We also use Gemini for high-speed extraction, OpenAI for embeddings, and ElevenLabs for voice. The model is chosen for the specific task — not because it's trending.
Structured prompts with source material, confidence scoring on every output, retrieval-augmented generation (pulling from your actual data, not the model's training data), and human review gates before anything consequential leaves the system.
Yes. Most AI integration work happens on top of existing platforms. We add classification, analysis, or generation capabilities to systems that already handle your data — rather than building from scratch.
AI features typically range from $5,000 for a focused integration (email triage, document classification) to $30,000+ for multi-model intelligence systems. Ongoing API costs are separate and usually modest — most business workloads cost less than a few hundred dollars per month in model usage.
Your data is processed through provider APIs with enterprise agreements (Anthropic, Google, OpenAI). Nothing is stored by the model providers for training. Sensitive data can be processed with additional safeguards — redaction, local models, or on-premise deployment where required.