AI Engineer — multi-agent orchestration & MCP servers, shipped end to end, solo. Open source, runnable, documented.

Based in Limerick, Ireland. I design and build AI systems where multiple models, tools, and services cooperate on a real problem — agent councils that propose, debate, and adversarially verify; MCP servers that give LLMs safe hands on real APIs; and the unglamorous plumbing (multi-tenant data, auth, billing, deploys) that real products need underneath.

Most product source is private — it's commercial. Everything below is open, runnable, or written up as a case study you can read end to end.

↑ Live — this card updates within seconds of me writing code. Built with devcard (open source, works with any AI coding agent).

🧾 deputy-mcp — open-source MCP server for Deputy (workforce management) Self-hosted alternative to the hosted Deputy connectors: your token stays on your machine. Three auth modes (API token, OAuth 2.0 with loopback flow, iCal fallback), self-service /my/* endpoints so a plain employee token works — no manager permissions needed — and graceful degradation when access is denied. Python + FastMCP, tested end to end against a mocked Deputy API, writes locked behind an opt-in flag. → Repo

⚖️ council — a deliberation engine that distrusts its own consensus Five lenses propose independently, debate, then the pipeline computes convergence (which claims multiple lenses reached alone), adversarially attacks the top claims with dedicated refuters, and only lets a neutral chair synthesize from what survived. Runs against any repo or question; heterogeneous models per lens; a deterministic mock backend runs the full pipeline offline. Source private for now — I'm happy to walk through it.

🏛️ etica — a multi-agent book production pipeline An agent-orchestrated pipeline that turns a public-domain source text into a typeset book — print PDF, EPUB3/Kindle, generative cover art, multi-language — with quality gates that block a stage when the previous one fails. Pipeline + a Part I sample are public; the full adapted prose is a separate paid edition. → Repo

🔏 SCPE — verifiable provenance for what an agent produced When a pull request arrives from someone you don't know — a person, or increasingly an AI agent — trust rests on a username and reading the diff by eye. SCPE adds a signed envelope proving who produced a contribution and that nothing was tampered with, verified offline with no protocol server and no new accounts, using signing keys the contributor's git host already publishes. I wrote the spec and three independent verifiers — Python, Go and Rust — that must reach the same verdict across 18 normative test vectors. Ships as a GitHub Action that seals or gates pull requests. Apache-2.0, on PyPI. → Site · Repo · PyPI

📡 Wavr — explainable multi-modal sensor fusion, privacy-first Fuses network scan, WiFi CSI, camera pose, and mmWave radar into one explainable room-occupancy state — confidence = strength (trust weight × the source's own confidence × freshness decay), and the dashboard always shows why. Ships its own MCP server, so an LLM agent can ask the house what's happening. Loopback-only, camera frames never stored. Python 3.11 + FastAPI, CI green with every hardware path mock-tested, AGPL-3.0. Runs with no hardware — off-localhost the frontend self-switches to a simulator. → Repo

🐱 Lucky Cat — multi-tenant restaurant SaaS Two full apps on one backend: Ownly (owner dashboard — shifts, payroll, stock, cash-up, live orders board) and Tillr (customer ordering PWA with real-time tracking). Postgres Row-Level Security as the isolation boundary, Stripe Connect wired for split payments, Cloudflare edge. Built end to end in under two months, solo; demo-complete and awaiting its first commercial rollout. → Case study · Ownly demo · Tillr demo

🛩️ PitchPilot — field-sales companion with a RAG copilot A white-label app for door-to-door reps. Its core is Wingman, a retrieval-augmented copilot (Supabase pgvector + Gemini) that answers a rep's question from the product's own playbook — grounded and cited. The case study is the honest version: an IVFFlat recall collapse on a tiny corpus, a thinking-token budget eating the output, and a cost-ordered model fallback under quota — what actually broke and how I fixed it. → Case study · Live demo

🔎 rag-demo — the retrieval pipeline, runnable A minimal, clone-and-run RAG pipeline: chunk → embed → pgvector → grounded answer that refuses to invent. docker compose up, a local sentence-transformers model for keyless indexing, pytest, and a match_chunks() SQL function shaped like a Supabase RPC. The inspectable code behind my copilots. → Repo

🪪 devcard — live dev stats card for the AI coding era The card at the top of this page. A local hook captures real AI-assisted coding output (not editor time), syncs an anonymized copy — language, line counts, never project names or code — to a Cloudflare Worker + D1 that renders a live, embeddable SVG: i18n by viewer language, auto dark/light, pinned repos with live stars, auto Sponsor button. Hooks git itself, so it works with Claude Code, Codex, Cursor, local models — anything that commits. One-command setup. MIT. → Repo

Working with: Python · multi-agent orchestration · MCP (Model Context Protocol) · LLM pipelines & RAG (pgvector, sentence-transformers) · Claude & Gemini APIs · FastAPI · Postgres + RLS · Supabase Edge Functions · Cloudflare Pages/Workers · Stripe Connect · TypeScript/React · Git-based CI.

Open to freelance and AI-engineering roles — multi-agent systems, MCP servers, RAG pipelines, and full-stack products built to ship.

• 🌐 Portfolio & CV — augustobastos.pages.dev
• 💼 LinkedIn — linkedin.com/in/augustobastos
• 🟢 Upwork — freelance profile
• ✉️ Email — augustobastos123@gmail.com