AI integration services for the systems you already run

Integrations we are asked for most

We start with the workflow where people spend the most time copying information between screens, and measure the result before adding the next one.

• CRM: account summaries, follow-up drafts and call notes logged from a transcript
• Help desk: suggested replies grounded in your knowledge base, and ticket routing
• ERP and back office: data read from documents and prepared as records for approval
• Documents and intranets: search and answers across your own content
• Internal tools: AI steps added to the workflows your teams already use

LLM integration, done properly

Behind every integration sits a model layer: which provider handles which request, what happens when one is slow or down, how instructions are versioned and what each call costs. We build that layer explicitly, with provider fallbacks, versioned instructions, cost tracking per feature and hard limits, so you can change models later without rewriting the integration.

Security and data handling

Integrations run under service accounts that reach only what the feature needs. We document which data goes to which model provider, under which terms and for how long it is kept, and we log calls so your team can audit them.

From our own products

Converso talks to models from several providers in one workspace, so we maintain a multi-provider model layer in production. GitDoc integrates with GitHub and reads private repositories with each user’s own access. When the project is an ERP implementation rather than an AI feature, Azinove’s ERP team handles it.