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arcloops

AI Solutions

We build the AI your strategy calls for.

The solutions loop is where strategy becomes working software. Once the audit and consulting have identified the right opportunities, we design and build the AI systems that capture them — whether that's a workflow automation, an LLM integration, an AI agent, or a full product.

We build to hand over. Every system we deliver comes with documentation, training, and a team on your side that can maintain it without us.

What Arcloops builds

Workflow Automation

We automate the manual, high-volume processes that slow your organisation down — document processing, data entry, reporting, approval chains, compliance checks, invoice handling.

Primarily on n8n and Make.com for process-level automation, with custom code where complexity demands it. We connect across your existing systems — email, CRM, ERP, HR tools — without requiring you to replace your stack.

Best for: Operations, HR, finance, and compliance teams with high-volume manual processes.

Custom LLM Integrations

We integrate large language models — OpenAI GPT-4o, Anthropic Claude, Google Gemini — into your existing systems and workflows. AI-powered document generation, contract analysis, report summarisation, customer communication drafting, and question-answering within the tools your team already uses.

Via API integration, custom prompt engineering, and retrieval-augmented generation (RAG) for organisations that need the AI to understand their specific documents, policies, and data.

Best for: Organisations with large volumes of text that currently require significant manual processing time.

AI Agents and Copilots

AI agents that handle multi-step tasks autonomously — researching information, triggering actions across systems, generating outputs, and escalating to humans when required. Custom copilots that sit alongside your team and assist with complex, information-heavy work.

Using LangChain, LangGraph, and n8n orchestration. We design the agent logic, the escalation rules, and the human-in-the-loop structures that make agents safe to deploy in enterprise environments.

Best for: Organisations with complex, repeatable processes that involve multiple data sources, decision points, and system interactions.

Data Pipelines and AI-Ready Infrastructure

Before you can use AI effectively, your data needs to be in a shape that AI can work with. We build the pipelines, databases, and infrastructure that make your data AI-accessible — and that make future AI initiatives significantly faster and cheaper to deploy.

PostgreSQL and Supabase for structured data. Pinecone for vector databases. ETL pipelines to unify data from legacy systems, spreadsheets, and disconnected databases.

Best for: Organisations that want to use AI at scale but know their data infrastructure isn't ready.

AI-Powered Products

We build AI-native software products — internal tools, client-facing products, operational dashboards, SaaS applications with AI embedded throughout.

Next.js and TypeScript for the frontend, FastAPI or Node.js on the backend, Supabase or PostgreSQL for data, and LangChain or direct API integrations for AI capabilities.

Best for: Companies that want to embed AI into their product offering — not just their internal operations.

How we build

We work across the modern AI engineering stack. We select tools based on your requirements and existing stack. If you've already invested in a specific platform or technology, we build to fit it — we don't ask you to start from scratch.

PythonTypeScriptJavaScriptNext.jsFastAPILangChainLangGraphn8nMake.comOpenAIAnthropicGoogle GeminiMistralPostgreSQLSupabasePineconeAWSVercelCloudflare

From brief to deployment

  1. 01

    Scope definition

    We begin with a detailed brief — either from the audit and consulting output, or your own specification if you're coming to us with a defined requirement. We agree on scope, timeline, cost, and success criteria before any work begins. No ambiguity.

  2. 02

    Architecture and design

    We design the system before we build it. You review and approve the architecture. This step prevents the most common cause of AI project failure: building the wrong thing because the brief wasn't specific enough.

  3. 03

    Development in sprints

    We build in two-week sprints with a review at the end of each sprint. You see the product being built in real time and can course-correct early. Changes at sprint two are cheap. Changes at sprint eight are not.

  4. 04

    Testing against real data

    We test against real organisational data and real workflows before handover. Where possible, we run a controlled pilot with a subset of your team before full deployment. This removes the surprises that usually appear at launch.

  5. 05

    Handover and team training

    We hand over full documentation, code (where applicable), and deliver a training session for the team who will operate and maintain the system. Handover is not optional. We don't build systems that only we can maintain.

  6. 06

    Optional support retainer

    Ongoing maintenance, iteration, and priority support available on a monthly retainer for organisations that want continued engineering support post-launch.

Have something specific in mind?

Tell us what you need built. We'll assess feasibility, give you an honest timeline and cost estimate, and propose a scope. If we can't build it well, we'll tell you that too.