The visionof engineeringishuman+AI

A RAG (retrieval-augmented generation) pipeline connects a language model to your own documents. Content is ingested, split into chunks, embedded into vectors and indexed; at question time the most relevant chunks are retrieved, reranked and handed to the model, which answers with citations. Aiporate GTM builds these pipelines inside the client's cloud account, with a fixed evaluation set so answer quality is measured on every release.

An agent workflow is a loop in which a model reads state, decides, calls tools (CRM, documents, ads, email, a warehouse) and writes results back. Aiporate GTM designs the tools, the guardrails and the approval gates: agents draft into branches or review queues, a human approves, and every run is logged with inputs, tool calls, cost and outcome.

Prompts change constantly and, left in chat windows and notebooks, cannot be reviewed, tested or rolled back. Prompt management means a registry with versions, variables, an evaluation set per prompt and pull-request review, so a change that lowers quality fails before it reaches a user. It is the same discipline software teams already apply to code.

Model orchestration is a routing layer in front of LLM traffic that picks the model per task by quality, cost and latency, falls back to another provider on failure, caches repeated calls and reports spend per feature. Aiporate GTM sets this up vendor-neutral across Claude, GPT, Gemini and self-hosted open-weight models, so prompts and evaluations stay portable.

Consulting starts at EUR 4,000 and a single custom system, for example one RAG pipeline or one agent workflow with evals, starts at EUR 8,000. The platform layer (retrieval service, prompt registry, model gateway, observability) is scoped from the number of sources and features; the diagnostic on this page produces the build order and a scoping call fixes the price. Engagements are milestone-based and everything is built in the client's own accounts and repository.