Services

Design, engineering and orchestration — end to end.

I take AI ideas from a sketch to a deployed, monitored product. Pick a single piece or the whole pipeline.

AI Agents & Swarms

Autonomous systems that plan, act and coordinate.

I design multi-agent systems with LangGraph: an orchestrator that delegates to specialized sub-agents, with tools, memory and auditable decision logic. Not a chatbot — a system that does the work.

  • Orchestrator + specialized sub-agents (LangGraph)
  • Tool calling, memory and state management
  • Human-in-the-loop checkpoints and audit logs
  • Parallel execution tuned for cost and latency
antonelli runtime
run.0xA7
Orchestrator
Pedro Antonelli
ship the RAG retrieval upgrade
working
1
Research Agent· queued
scanning docs + recent papers
2
Code Agent· queued
implementing hybrid retrieval
3
Review Agent· queued
running evals + unit tests

RAG & Knowledge Systems

Answers grounded in your own data.

Retrieval-augmented pipelines that turn messy documents into trustworthy answers: ingestion, chunking, embeddings, hybrid search, re-ranking and citations — so the model stops guessing.

  • Ingestion + chunking + embedding pipeline
  • Hybrid (vector + keyword) retrieval & re-ranking
  • Cited, grounded answers with eval harness
  • Vector DB setup (pgvector, Pinecone, Qdrant)
Retrieval
How do refunds work?
refund-policy.md
returns accepted within 30d
··
pricing.md
tiers and billing cycle
··
support-faq.md
how to request a refund
··

Multi-Cloud & DevOps

Shipped, scaled and observable.

I deploy and operate AI workloads across GCP, AWS and Azure: containers, serverless, CI/CD, infra-as-code and observability. The system runs reliably and you can see exactly what it’s doing.

  • Containerized deploys (Docker, Cloud Run, ECS, AKS)
  • CI/CD pipelines and infra-as-code
  • Secrets, cost guards and rate limiting
  • Logging, tracing and alerting for LLM apps
Deploy
api:1.4.2
rollout
build
push
deploy
GCP
Google Cloud
Cloud Run · containers
QUEUED
AWS
AWS
ECS · scaling replicas
QUEUED
AZ
Azure
AKS · health checks
QUEUED

Automations & Skills

Stop doing by hand what a system can do.

I find the repetitive work in your operation and wrap it in reproducible skills and scheduled automations — same input, same output, every time, plugged straight into the tools you already use.

  • Reproducible, versioned skills
  • Scheduled & event-driven automations
  • Integrations with your existing tools & APIs
  • Docs and handoff so it lives in your stack
Workflow
cron · 09:00 daily
queued
1Fetch orders
2Transform rows
3Validate
4Write to Sheet

Full AI Product Design & Build

The whole thing — UX, code and the AI inside.

When you need the complete product: I design the interface, build the full-stack app and embed the AI so it feels like one coherent thing. Design taste and engineering depth from the same person.

  • Product & UX design (Figma → shipped)
  • Full-stack build (Next.js, TypeScript, APIs)
  • AI features embedded natively in the UX
  • From prototype to deployed, polished product
Build
landing.app
designbuildship
landing.app
Get started →

How an engagement runs

From brief to running system

A clear, repeatable path — with a swarm of agents accelerating every stage.

01

Map

We pin down the real problem, the inputs and the edge cases — before writing a line of code.

02

Design

I design the system and the experience together: architecture, data flow and the interface around it.

03

Build

I build it — accelerated by my swarm running research, code and review in parallel.

04

Ship & operate

Deployed across the cloud you choose, with monitoring, cost guards and a clean handoff.

Not sure which piece you need?

Describe the problem and I’ll map the smallest system that solves it.

Tell me about it