Question 1

What models and stacks do you use for production AI agents and RAG?

Accepted Answer

We select models against latency, compliance, and cost, typically frontier APIs for orchestration plus open-weight or private models for sensitive inference. RAG pipelines use your document stores with citation-backed retrieval, evaluation harnesses, and role-based tool permissions. Edge deployments can run on Nvidia Jetson or private VPC GPUs so training and inference data never leave your boundary.

Question 2

How long does a custom AI agent take to reach production?

Accepted Answer

Most AI engagements start with a focused architecture sprint, then 14-day engineering sprints. A focused agent or RAG assistant often reaches staging in 4–8 weeks. Multi-agent platforms, computer vision lines, or regulated deployments typically land in 3–6 months depending on integrations and evaluation rigor.

Question 3

How do you keep AI systems secure and auditable?

Accepted Answer

We design for data minimization, encrypted transit and storage, audit trails, and clear retention policies. Prompt and tool permissions are documented; human-in-the-loop checkpoints cover high-stakes paths. Vendor models are chosen against your SOC2, GDPR, and industry controls. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.

Enterprise AI, Custom LLMs & Intelligent Automation Solutions