Hire the Best Alexa Skill Kit Specialists in London, UK

Most AI projects fail between the demo and production. I build the version that survives contact with real data, real users, and real business processes. I'm an AI Engineer who builds, ships production AI, not prototypes. Three years of building LLM systems for enterprises, growing businesses, and my own AI products: customer service platforms, agentic assistants, content and data pipelines, ERP and CRM integrations. Different industries, same standard. Working remotely with businesses all around the globe. Everything runs continuously with monitoring, guardrails, and fallbacks built in. 𝗪𝗛𝗔𝗧 𝗜'𝗩𝗘 𝗦𝗛𝗜𝗣𝗣𝗘𝗗 → AI Chatbots and AI-assisted customer service: platforms where AI handles 80%+ of inbound enquiries end to end, with intelligent routing, structured validation, and human-in-the-loop escalation for everything that genuinely needs a person. Built for high-volume environments where a wrong answer costs money. → AI Assistant (voice + chat): Agentic system on a custom TypeScript orchestration layer. Always-on realtime voice (OpenAI Realtime API over WebRTC) with full Microsoft 365 tool calling: reads and writes email, manages calendar and tasks, generates pre-meeting briefs, places outbound calls. Sub-1.5s voice round-trip, 99%+ tool-call success. → AI content platform, built end to end: learns each user's writing voice, then generates and publishes channel-correct content for LinkedIn, Meta, X, YouTube, and long-form through a five-stage pipeline (router, retriever, generator, critic, polisher), with semantic search, multi-variant scoring, and a pipeline-to-calendar workflow. → RAG and knowledge systems: Pinecone-backed retrieval over large data and internal repositories, powering semantic search, chatbots, and copilots. Evals and structured output validation holding malformed responses under 2%, with ~40% cost efficiency gains. → AI Content Automation: multi-agent pipelines that ingest data, enrich, validate, and publish across ecommerce platforms. In production: 98%+ schema-valid outputs, error rates below 3%, ~95% taxonomy accuracy via RAG, onboarding time cut by over 90%. → Workflow, CRM, and ERP automation for retail, wholesale, and pharmacy clients: n8n, Make, Zapier, and Power Automate pipelines integrating 8+ systems. → Open-source builds: a multi-provider LLM proxy, an AI ops monitoring dashboard (FastAPI/Postgres/Grafana), an AI-native CRM, and a voice-to-task manager. Code on my GitHub. 𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗 𝗙𝗢𝗥 𝗬𝗢𝗨 - AI agents and multi-agent systems: tool calling, orchestration, memory, human-in-the-loop controls - AI chatbots and customer service automation: 80%+ AI-handled enquiries with escalation that works - RAG systems: document Q&A, semantic search, knowledge bases that don't hallucinate - Voice AI: realtime voice agents that take actions, not just answer questions - Ecommerce AI automation: product content generation and catalogue pipelines for Magento (Adobe Commerce), Shopify, WooCommerce, BigCommerce - Microsoft 365 automation and apps: Power Automate, Power Apps, SharePoint, Teams, Microsoft - Graph API, including AI agents wired into Outlook, Calendar, and Tasks - CRM automation: self-enriching records, automated follow-ups, cross-system data sync - Internal tools and ops automation: dashboards, approval workflows, ERP integrations - Full-stack AI apps: Python/FastAPI backends, React/Next.js frontends, Docker on Azure, AWS, or GCP 𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞 I scope before I build. Most AI projects fail because the problem was never defined tightly enough for an LLM to deliver value, so first I tell you honestly what will work, what won't, and what it costs to run. You get working software early, structured updates, and documentation at handover. Everything ships with logging, evals, and cost tracking. Privacy-first by default: PII redaction before anything touches an external LLM provider, full audit trails, and builds aligned to GDPR, CCPA, HIPAA, SOC 2, and your industry's compliance requirements. Send me the problem, not a job spec. I'll reply with exactly how I'd build it, what it costs to run, and whether AI is even the right tool for it. 𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞 Python, TypeScript, FastAPI, React, Next.js, Node.js · OpenAI, Anthropic Claude, Azure OpenAI, Gemini, LangChain, custom agent orchestration, MCP, OpenAI Realtime API · Pinecone, pgvector, embeddings · Microsoft 365, Microsoft Graph, Power Automate, Power Apps, SharePoint, Business Central · n8n, Make, Zapier · Magento 2, Shopify, WooCommerce, BigCommerce · Azure, AWS, GCP, Docker, CI/CD · PostgreSQL, SQL · Grafana, LLM observability Keywords: AI Agent Developer, AI Automation Expert, AI Chatbot Development, Customer Service Automation, RAG, Voice AI, Agentic AI, LLM Integration, n8n Automation, Power Automate, Power Apps, Microsoft 365 Automation, Ecommerce Automation, Magento, Shopify Automation, WooCommerce, CRM Automation, ERP Integration, Workflow Automation, AI Consultant, Document AI

Added $200k+ in revenue in 6 months by fixing one thing: how fast and how consistently leads were responded to. Most businesses do not have a lead problem. They have a speed-to-lead, follow-up, and operational execution problem. Leads come in, responses are delayed, follow-ups are inconsistent, dispatching is manual, and opportunities slip through the cracks. I build the systems that fix that, and then I operate and manage them so the business never has to think about it. This is not just automation for automation's sake. Every system I build has a human layer behind it. When the AI handles it, it handles it. When a situation needs judgment, escalation, or someone who actually picks up the phone and takes care of the client, I step in. Recent results: • $200,000+ generated in 6 months for a car wrap shop using an AI SMS system that instantly replies, educates and qualifies leads, handles objections, gives pricing guidance, and follows up automatically • Built a voice AI intake system for a law firm that secured 3 new clients in the first month from missed and after-hours calls • Implemented voice AI call handling and dispatching for service businesses that reduced missed calls to effectively zero and ensured every enquiry was captured • Built lead response systems that reply within seconds and continue following up until the lead converts or opts out What this focuses on: • Speed-to-lead systems (instant response within seconds) • AI SMS, chat, and voice systems that qualify and guide leads • Automated follow-up that keeps conversations alive without being spammy • CRM integrations to keep everything organised and trackable • Turning inbound enquiries into booked appointments and revenue If you’re generating inbound leads but feel like you’re leaving money on the table, it’s usually a response and follow-up issue. Let me help you fix that! Tools & platforms I live and breath: GoHighLevel, Vapi, Retell AI, Housecall Pro, Hubspot, ClickUp, MyCase, Clio, Airtable, Pipedrive, Make, n8n, Zapier, Claude Code, and more

I’m a London-based AI voice automation engineer building proprietary products and operational systems. I ship finely-tuned agents integrated via TypeScript/Node.js backends (Fastify & Serverless Functions) to your APIs, CRMs, booking systems, databases, messaging platforms and internal tools, with professional deployments, production-grade observability, monitoring, structured logging and analytics on solution performance. Retell, Vapi or ElevenLabs are utilised as the voice infra and conversational layer. Currently delivering for SMBs, agency partners and service businesses across the 🇬🇧 UK · 🇺🇸 USA · 🇦🇺 Australia · 🇳🇿 New Zealand. 👇 The reasons why my voice automation products are the best on the market for SMBs 👇 Scoped entirely around your business. Every build starts with your operations, your business logic, your existing software and your compliance requirements. Delivery is end-to-end and direct with me, from strategy and process design through to deployment and ongoing optimisation. Real code, no n8n/Make/Zapier daisy chains. The backend is Fastify API services and serverless functions performing real actions across your business tools via APIs, webhooks and direct integrations. Use cases include direct sales, scheduling, lead qualification, CRM updates, ticket creation, administrative tasks, internal routing, notifications and data sync. Agents on low-latency voice infrastructure. Configured on Retell, Vapi and ElevenLabs with telephony and SIP via Twilio. Systems support phone calls, web calls, web chat, SMS and in-app calls, with call routing, human handoff, departmental separation and escalation logic where required. You own the system. I deploy entire systems inside your own accounts so your team retains ownership and operational control. AWS, Google Cloud and Vercel deployment split dependant on the nature of the build. Every deployment follows professional practices across secrets management, structured logging, observability and error alerting. Custom internal software where needed. Ticketing dashboards, operator tools, alert systems and internal workflow interfaces, built to fit your operations. Tested before launch, monitored after. Real call scenarios, edge cases, failed inputs, broken handoffs, integration failures, workflow issues and operator-side failure points, all covered before launch. 7 days monitoring and optimisation post-delivery, with optional ongoing transcript review, performance monitoring, workflow refinement, agent optimisation, continued development and support. How I engage: 1. Discovery & scoping (your systems, compliance, integrations) 2. Architecture & logic design 3. Build (agent, backend, integrations, custom tooling) 4. Live testing & iteration 5. Deployment & launch 6. Handoff with optional continued development, monitoring & optimisation 📩 DM for live product demonstrations. 🎥 Portfolio below for full breakdowns of complete systems including the logic, architecture, backend software, integrations and implementation behind each build.

I build AI infrastructure that turns manual operations into automated revenue machines. Through my venture studio, I've shipped six production platforms across fitness, healthcare, recruitment, and analytics. What I build: • Custom agentic AI systems in TypeScript with Mastra - tool-using agents, multi-step reasoning • SaaS platforms with Stripe Connect, multi-tenant Supabase architecture, and role-based access • AI voice agents and receptionists handling inbound calls, lead qualification, and 24/7 booking • Native iOS apps in Swift/SwiftUI with production Node.js backends • Internal portals and analytics dashboards pulling real-time data from multiple APIs • Workflow automation with n8n, Make, and custom integrations when reliability matters Results from recent projects: • Cut client operating costs by 73% for a $108M business • Generated $17K+ MRR in 21 days through automated sales workflows • Reduced client onboarding from 45 minutes to under 2 minutes • Increased lead conversion by 400% with an AI receptionist In early 2024, I founded AI Fulfilment Organisation, a global AI venture studio with 10+ products in production and a team of 6. I also serve as CTO of TrainFlow, where I architected a white-label GoHighLevel CRM platform for online fitness coaching agencies. On the product side, I built TallCall AI (AI receptionist SaaS), Headhunt AI (recruitment platform), Flint (iOS AI agent app), and LifeOS (personal analytics dashboard). I care about shipping software that works in production, not prototypes that sit in staging. Every build comes with proper error handling, mobile-responsive design, and documentation your team can actually use. Send me a message with a brief overview of your project and I'll reply within a few hours.