Gen AI Architecture & Engineering
Gen AI architecture, designed for real data and real users.
InfoMover designs the architecture of Gen AI systems, from RAG and AI agents to multi-agent workflows, and stays through delivery so the design holds in production. We start from your documents, systems and risks, prove the approach in code, then build with evaluation, guardrails and monitoring in place.
Sound familiar?
- Your RAG pilot answers well in the demo and confidently wrong on page 47 of the policy manual.
- Agents call the right tools in testing and the wrong ones on Friday afternoon.
- Nobody can say whether last week's prompt change made answers better or worse.
- Security wants to know where the data goes. Nobody has a clear answer.
What you get
- A design you can defendRetrieval, chunking, model choice, agent boundaries and failure modes, written down and reviewed with your team.
- Working code on your dataNot a notebook. A service with an API, tests and an evaluation set built from your real questions.
- Evaluation and guardrailsAnswer-quality metrics, regression checks for prompt changes, and limits on what agents may touch.
- A path to productionDeployment, monitoring, cost controls and a runbook your engineers can own.
Ways to engage
Pick the shape that fits.
Most Gen AI work starts with an advisory sprint or a proof of concept on your own data.
Built by us
Projekts.ai
Projekts.ai runs on our own agent runtime and MCP server, in production. The RAG and agent advice we give comes from building and running it.
Visit Projekts.ai ↗Prefer to learn it?
Building Intelligence with RAG — Build a Grounded Knowledge Assistant
A hands-on RAG workshop for your engineers, live online or at your office.
See the course ↗Questions & answers
Can you work with our existing LLM provider?
Yes. We work with OpenAI, Anthropic, Google, open-weight models and private deployments. The choice is made per use case, on cost, quality and where your data is allowed to go.
How do you know the answers are correct?
We build an evaluation set from your real questions and expected answers before tuning anything, then measure every change against it. Gut feel is not a metric.
Can you take over a pilot another team started?
Yes. A two-to-four-week Advisory Sprint is usually the right start: we review what exists, measure it, and tell you what to keep, fix or rebuild.
Do you build agents that act on our systems?
Yes, with clear boundaries: which tools an agent may call, with what permissions, and what needs a human to approve. Narrow and reliable beats broad and surprising.
Start a conversation
Talk to an architect about Gen AI Architecture & Engineering.
Tell us what the system should answer or do, and on which data. We'll come back with questions, then a way to prove it.