We build production RAG and custom AI agents.
Automatico is an AI engineering consultancy. We ship enterprise knowledge systems and agentic workflows that actually sit on your data and APIs—with retrieval evaluation and observability so you can tell when quality slips.
- Engineering-led delivery from discovery through production handoff
- Working reference builds with architecture and security evidence
- AWS, Azure, GCP, on-prem, or hybrid—selected for the workload
Nationwide delivery · On-site in Dallas–Fort Worth
Three engineering disciplines. One delivery team.
Strategy, implementation, and platform work scoped to production requirements.
- RAG development & enterprise knowledge systems — ingestion, hybrid search, reranking, access control, and evaluation. Answers should come from your corpus, with citations you can check.
- Custom AI agents & multi-agent orchestration — agents that retrieve, call tools, write records, and escalate when they should. Permissions stay bounded. Actions stay auditable.
- AI infrastructure & data pipelines — identity, pipelines, APIs, and deployment. The boring layers that decide whether a demo survives contact with production.
Inspect a live RAG platform.
The stack at rag.automatico.llc is the same class of system we deploy: Titan embeddings, Qdrant, hybrid BM25 + dense fusion, reranking, agent routes with access control, and evaluation hooks. It is Cognito-gated, so a walkthrough is how you see queries, traces, and retrieval quality for real.
- Hybrid + rerankDense and lexical fusion with cross-encoder precision on the shortlist
- Ops instrumentationStage traces, corpus audit, and advisor findings on live traffic
- Production postureCognito auth, private VPC, least-privilege IAM, evaluation hooks
Stack-agnostic engineering across
- AWS Bedrock
- Azure AI
- Google Cloud
- Qdrant
- LangGraph
- Amazon Connect
- API Gateway
- Private / on-prem
From discovery to production handoff.
Fixed-scope engagements. No multi-year platform pitch before the first workflow ships.
- 01 Discovery Requirements, data boundaries, integrations, and deployment constraints for one workflow.
- 02 Build Fixed-scope implementation—typically days to a few weeks for the first production path.
- 03 Validate Test against real scenarios, retrieval evaluation where RAG is in scope, and security review.
- 04 Handoff Runbooks, train-the-trainer, and a clear path to the next capability when the first holds.
More working demonstrations.
Executive summary on each build; architecture, security, and deployment notes underneath.
- AI voice agent — intent capture, structured records, rules, notification, and human handoff on an Amazon Connect path. Architecture notes
- Secure document pipeline — verified upload, private storage, and staff alerts. Live demo ↗ · Notes
- Qualification and routing agent — validate submissions, apply rules, persist a record, notify the owner with grade and summary. Live demo ↗ · Notes
Common questions from technical buyers.
What is RAG development?
Retrieval-Augmented Generation: the model answers from your documents and systems, with citations. We build the production path: ingestion, hybrid search, reranking, access control, evaluation.
What are agentic workflows?
Agents that retrieve, call tools, write records, coordinate when needed, and escalate to a person. Permissions stay bounded.
Do you ship prototypes or production systems?
Production systems. Reference builds demonstrate capability; client work includes integration, security, observability, and handoff.
Cloud, on-prem, or hybrid?
All three. The reference RAG stack runs on AWS; the same patterns deploy on Azure, GCP, private infrastructure, or hybrid when requirements call for it.
How do engagements start?
A technical discovery on one workflow: data, integrations, deployment constraints, and whether models, RAG, or deterministic automation is the right tool.
What makes Automatico different?
Working demonstrations you can inspect, engineering depth underneath, and honest labeling. No invented case studies or unsupported ROI claims.