Freelancers using LangChain in SpainFreelancers using LangChain in Spain
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Facundo Cappella's avatar
Facundo Cappella
maxMadrid, Spain
Tech Lead | Solution Architect | AI Engineer
137
Followers
Tech Lead | Solution Architect | AI Engineer
Cover image for 🚀 New promotional video for
🚀 New promotional video for my experimental project: Orbital Forecast 🛰️ Every day, An agent is forecast the Bitcoin’s closing price. The project combines AI forecasting with a futuristic orbital-space interface. 🎥 Promotional video created with Seedance 2.5. 🔗 Check out Orbital Forecast (https://btc-forecast.vercel.app/?utm_source=chatgpt.com)
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Cover image for Whitepaper Finder
Whitepaper Finder
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Cover image for Post your ideas, or random
Post your ideas, or random thoughts anonymously. Get ratings and opinions from people everywhere, no sign-up. https://rateidea.us/
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Cover image for https://dealy.figma.site/ Latest Updates 🚀 📍 Improved
https://dealy.figma.site/ Latest Updates 🚀 📍 Improved User Location Accuracy The map now centers precisely on the user's current location. Increased zoom levels make it much easier to identify the exact position and nearby points of interest. 🌐 Domain Update Renamed the domain to better reflect the application's purpose and branding. Tech Stack Supabase Next.js Skills & Focus Areas UI/UX Design Next.js Best Practices Geolocation & Mapping Frontend Performance Optimization Small improvements like these can make a significant difference in usability and overall user experience.
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LangChain(2)
Alberto Guinda's avatar
Alberto Guinda
Zaragoza, Spain
AI Agent & Full-Stack Engineer · LangGraph · RAG · AI Apps
New to Contra
AI Agent & Full-Stack Engineer · LangGraph · RAG · AI Apps
Cover image for A conversational AI copilot that
A conversational AI copilot that takes a buyer from "I want to reserve" to contract and receipt. A LangGraph agent orchestrates 19 tools with human-in-the-loop confirmation before any payment, session memory, and RAG — over a transactional backend with hard guarantees (no double-booking, Stripe + webhook reconciliation, full audit) and a 3D/BIM viewer with a first-person walkthrough. Stack: TypeScript, LangGraph.js, Hono, PostgreSQL/Drizzle, React 19, react-three-fiber. LangSmith observability with GDPR PII redaction.
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Cover image for 🚀 A multi-tenant booking SaaS
🚀 A multi-tenant booking SaaS shown end-to-end From the product site that sells it, to the white-label site each business gets, straight to the dashboard that runs it all. 🤖 How it works: Customers book by chatting with an AI assistant in a messaging app. Every booking carries the channel it arrived through, turning chat conversations into a highly visible, organized agenda for the business owner. 📊 The Admin Panel: Live Analytics: Complete with an occupancy heatmap. Full Agenda Management: Create, reschedule, confirm, complete, or mark as no-show. Smart Operations: An auto-built customer CRM (generated directly from bookings), catalogue & staff management, and bot-collected reviews. Real-World Flexibility: The owner can force an appointment on top of another. Barbershops squeeze people in every day, and software that forbids it simply gets abandoned. 🛠 The Tech Stack: Frontend: Vite + React 19 + HeroUI v3 Backend: FastAPI (Python 3.14) on PostgreSQL Static Sites: Astro hosted on Cloudflare Monetization: Stripe subscriptions with a 30-day trial Data Integrity: No double-booking is guaranteed by a robust database exclusion constraint, rather than just relying on the interface. 🌍 Status: Live in production and successfully taking real appointments for a barbershop in Zaragoza.
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Cover image for Recruiters sit on hundreds of
Recruiters sit on hundreds of CVs they can't search. Keyword search misses the obvious — the candidate wrote "React", you searched "frontend", they never surface — and every AI tool that fixes that ships candidate data to a third-party API. I built RecruitSecure AI to do the semantic search without the API call. The embedding model runs server-side through Transformers.js (quantized multilingual-e5-small, 384 dimensions, 100+ languages), so you can ask in Spanish and it finds CVs written in English — and nothing ever leaves the deployment. Drop in PDFs or DOCX and it extracts name, email, skills and experience, indexes each CV as a vector, and answers plain-language queries like "frontend lead who has built a design system": ranked by relevance, each result showing the exact CV fragments behind the match, so the recruiter sees the reason and not just a score. Retrieval is hybrid — pgvector HNSW cosine fused with BM25 full-text through Reciprocal Rank Fusion, then re-ranked from the recruiter's own thumbs up/down. Next.js 16, React 19, strict TypeScript, PostgreSQL + pgvector through Drizzle ORM, Auth.js, Stripe subscriptions, multi-tenant with AES-256-GCM encryption at rest and GDPR export and deletion. Built solo, end to end. The video is the real application: ten synthetic CVs uploaded through the actual pipeline, extracted, embedded and searched. Nothing staged.
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Cover image for A health-tracking Android app for
A health-tracking Android app for logging blood pressure, oxygen, pulse, glucose and weight — designed for elderly users first. Oversized one-digit-at-a-time keypad, high-contrast readings, and a one-tap printable report their doctor can actually read. Medication list that keeps withdrawn drugs visible in red instead of hiding them, so nobody takes one by mistake. Offline-first: the database lives on the device and never leaves it unless the user syncs it to their own LAN server. Built with Expo/React Native in strict TypeScript across 15 languages, with speech input, a marketing site in Astro on Cloudflare Pages behind a hash-pinned CSP, and a Hono + SQLite sync server with last-write-wins reconciliation.
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LangChain(1)
Alex Mamaev's avatar
Alex Mamaev
Madrid, Spain
AI & SaaS Developer with Product Expertise
AI & SaaS Developer with Product Expertise
Cover image for Developing a Chatbot and Widget for Property Managers
Developing a Chatbot and Widget for Property Managers
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Cover image for Developing an AI-Powered Tool for Gathering Customer Feedback
Developing an AI-Powered Tool for Gathering Customer Feedback
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Cover image for Chrome Extension for Scheduling Meetings Globally
Chrome Extension for Scheduling Meetings Globally
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Javier Martinez's avatar
Javier Martinez
Spain
24/7 AI Automation | Receptionist + Sales + Workflows
11
Followers
24/7 AI Automation | Receptionist + Sales + Workflows
Cover image for From prospect list to personalized
From prospect list to personalized proposal — fully automated, no manual research required.
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Cover image for I built my own lead
I built my own lead generation machine because I moved to Denmark with zero network. I moved to Denmark. I didn't know anyone. I had no network. And I needed to find companies I could sell automation services to. I could spend hours manually searching for companies, finding emails, cleaning data, and sending messages one by one. Or I could automate the process. So I built a complete lead generation pipeline: → 🐍 Python scraper connected to Denmark's public government CVR database → 📊 Google Sheets synchronization to normalize and deduplicate companies → ✉️ Automated cold email workflow with 10 sector-specific templates using n8n → 📱 Daily Telegram reports with full pipeline statistics → 💼 Assisted LinkedIn outreach: 10 leads per day + a ready-to-send message And these are the numbers: 📈 19,334 leads in the database 📧 71% email coverage 🏭 10 sectors 🔢 34 industry codes ✉️ 25 automated emails per day ♾️ ~534 days of outreach without repeating a lead 💰 $5/month total cost — basically just the VPS. The interesting part isn't the scraper. It's not n8n. It's not Telegram. The interesting part is that a process that used to take hours of manual work can now run almost entirely on its own. And that's exactly what I want to build for companies: Find repetitive processes → design the system → automate them → measure the results. In the video, I break down the entire pipeline, piece by piece. If you're building something similar, let me know which part you'd like me to break down next. 👇 #Automation #AI #LeadGeneration #n8n #Python #ColdEmail #SalesAutomation #B2BAutomation #Denmark #BuildInPublic
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Cover image for How to replace a $2,000/mo
How to replace a $2,000/mo manual reporting task with a $3/mo AI workflow. ⚡️ Here is a look under the hood of our automated CEO Daily Briefing system: • Orchestration: n8n scheduled trigger at 08:00 AM. • Data Sources: Google Sheets (Parallel execution) + Industry RSS feeds. • AI Layer: GPT-4o mini analyzing metrics and writing executive insights (< $0.01 per run). • Delivery: Formatted Telegram notification.
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Cover image for Building custom AI architectures that
Building custom AI architectures that actually scale. 💻⚡️ Here is a peek under the hood of a production-ready AI system: • Terminal: Claude Code as the primary engine. • Harness: Gentleman AI for memory persistence (Engram) and spec-driven development (SDD). • Automation Server: Self-hosted n8n running on a dedicated Hostinger VPS.
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LangChain(1)
Dimitri Appel's avatar
Dimitri Appel
Barcelona, Spain
AI Workflow Architect & Automation Expert
8
Followers
AI Workflow Architect & Automation Expert
Cover image for AI Invoice Automation via Telegram, OCR & SAP Integration
AI Invoice Automation via Telegram, OCR & SAP Integration
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Cover image for AI Appointment Scheduler (n8n + Twilio + Cal.com + Airtable)
AI Appointment Scheduler (n8n + Twilio + Cal.com + Airtable)
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Cover image for Customer Onboarding Automation with n8n
Customer Onboarding Automation with n8n
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Ivan Skachek's avatar
Ivan Skachek
Barcelona, Spain
Applied AI Engineer. I ship live LLM agents, RAG, evals.
New to Contra
Applied AI Engineer. I ship live LLM agents, RAG, evals.
Cover image for Live AI agent that recovers
Live AI agent that recovers failed subscription payments. LangGraph, RAG, FastAPI, 726 automated checks · GDPR-ready · rules decide money, the model only drafts. deployed on Fly.io (http://Fly.io). Try it at paypilot.fly.dev (http://paypilot.fly.dev)
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Cover image for Streamflow Solutions: AI Revenue-Recovery Systems AI and
Streamflow Solutions: AI Revenue-Recovery Systems AI and automation systems that recover lost revenue: failed-payment, abandoned-cart, and reorder flows for e-commerce and fintech, deterministic by design.
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Cover image for AML Alert Triage - deterministic scoring, AI at the edge
AML Alert Triage - deterministic scoring, AI at the edge
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Cover image for  Revend: AI Cart-Recovery App for
 Revend: AI Cart-Recovery App for Shopify AI-driven abandoned-cart recovery for Shopify: a different message per shopper matched to their cart and hesitation, instead of one blanket discount.
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LangChain(2)
Andrés Lage's avatar
Andrés Lage
Ferrol, Spain
Independent AI Architect&Strategist EU AI ACT&OWASP
New to Contra
Independent AI Architect&Strategist EU AI ACT&OWASP
Cover image for Production-ready multi-agent system demonstrating Agentic
Production-ready multi-agent system demonstrating Agentic AI principles. Built with LangGraph for workflow orchestration, FastAPI for the API, and OpenAI GPT-4. Features autonomous decision-making, tool use, and multi-agent collaboration for intelligent data analysis and strategic recommendations.
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Cover image for This production-ready Hotel Voice Assistant
This production-ready Hotel Voice Assistant integrates Google Gemini 2.0 with a scalable Flask/Waitress backend to power fluid, context-aware conversational booking experiences in Spanish. It leverages a distributed Redis session store for stateful multi-turn memory, backed by native Function Calling to stream live availability and real-time pricing directly from the Amadeus GDS API. Engineered with an "auditability-by-design" framework, the architecture implements pluggable callback hooks and strictly aligns with the OWASP Agentic Top 10 (2026) to mitigate multi-agent risks and secure user data.
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Cover image for Industrial AI replaces opaque, high-cost
Industrial AI replaces opaque, high-cost LLMs with deterministic, hybrid architectures built specifically for high-risk, heavily regulated enterprise environments. It leverages glass-box explainability (like EBMs) and cascading NLP pipelines to resolve up to 80% of operational traffic at zero token cost and sub-millisecond latency on CPU. By embedding continuous statistical drift monitoring (KS-test/PSI), it translates raw telemetry into audit-ready assets, guaranteeing strict compliance with the EU AI Act and ISO 42001.
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Cover image for Most ML projects that win
Most ML projects that win Kaggle would not survive a regulatory audit in 2026. This one is built specifically to do both competitive performance AND audit-ready by design. Insurance claim prediction (Porto Seguro dataset, 3.6% positive class, highly imbalanced) implemented end-to-end with EU AI Act, Solvency II and ISO 42001 compliance as the architectural starting point not as a documentation afterthought. Four pillars: MLOps & Shadow Monitor Architecture. Vendor-agnostic monitoring layer that reads inference logs independently from the production model (Azure ML / SageMaker / Vertex AI). KS-test drift detection in real time. Zero vendor lock-in. The Shadow Monitor is the answer to "how do you audit a black-box cloud ML service?" Explainability vs Performance trade-off, decided with evidence. EBM (Explainable Boosting Machine) chosen over XGBoost/LightGBM. ROC-AUC 0.608 vs 0.64-0.65 for XGBoost a 4% performance cost in exchange for native glass-box explainability that regulators accept without SHAP post-hoc workarounds. The right call for regulated industries, the wrong call for tech. Threshold optimization on imbalanced data. Default scikit-learn 0.5 threshold yields F1 ≈ 0 on this dataset a model that "performs at 96.4% accuracy" is in fact useless. Custom F1-Score curve finds the optimal decision boundary at 0.091. The difference between a Kaggle submission and a production system. Automated Compliance Dashboard. Fairness (demographic parity, equalized odds, protected-attribute analysis), Transparency (feature-level contributions, full documentation), Accountability (model card, ADRs, governance framework, human-in-the-loop). Maps directly to EU AI Act high-risk requirements, Solvency II model validation, and ISO 42001 controls. Why Polars over Pandas? Built in Rust, 5-12x faster, lazy evaluation, native multi-threading. For production ML under EU AI Act, processing speed on inference logs is not a nice-to-have it's an audit requirement. Template replicable for banks, insurers, healthcare, and any organization where ML decisions need to defend themselves in front of a regulator.
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LangChain(1)