FlowMind is an AI automation agency for US and UK businesses. We replace repetitive work with reliable AI systems — workflows, agents, retrieval, and integrations — engineered with the same rigour as production software. Measurable ROI, documented evals, and zero LLM hype. Scope a workflow or see our AI automation service detail.
High-volume, repetitive workflows with unstructured inputs (text, images, PDFs); decision points that need reasoning, not rules; tasks that scale poorly with headcount; processes where 70–90% automation creates clear ROI even with a human in the loop.
Workflows that are already fully deterministic (use code or no-code, not LLMs); ultra-low-tolerance tasks where 99.9%+ accuracy is required without human review; edge cases that change daily and outpace eval rigour.
Half of the use cases we get asked about are better served by Zapier, a custom script, or a process redesign. We tell you that on the discovery call — not after you've paid for a build.
| Phase | Deliverable | Investment |
|---|---|---|
| Discovery (1–2 weeks) | Use-case validation, ROI model, technical architecture, eval plan | $2,500–$6,000 |
| Proof of value (2–4 weeks) | Working prototype, eval scorecard, ROI projection | $4,000–$12,000 |
| Production build (4–12 weeks) | Deployed automation, observability, runbooks, training | $25,000–$120,000+ |
| Ongoing operate | Monitoring, eval drift, prompt updates, model upgrades | $1,500–$6,000/mo |
Working systems — not slide decks. We design, build, and operate AI-powered automations that integrate with your stack: workflows that classify and route, agents that summarise and reason, retrieval systems over your internal data, and integrations that close the loop with CRM, billing, or operations tools.
Zapier and Make are excellent for deterministic, glue-code automation. They start to crack when you need reasoning over unstructured inputs, agentic decisions, or robust evals. Most production AI automations are a hybrid: low-code where it works, custom code where reliability matters. See our deep-dive comparing n8n, Zapier, Make, and custom-built AI.
A single workflow proof-of-value typically runs $4,000–$12,000 over 2–4 weeks. A production-grade multi-workflow programme runs $25,000–$120,000+ depending on integrations, eval rigour, and ongoing support. We will tell you when no-code is cheaper and better — and when it isn’t.
LLMs from OpenAI, Anthropic, Mistral, Google, or self-hosted via Ollama / vLLM where data residency demands it. Orchestration via LangGraph, custom code, or hybrid n8n + code. Vector storage on pgvector, Pinecone, or Qdrant. Eval and observability via Langfuse, Braintrust, or internal harnesses. We choose the simplest stack that meets reliability and cost targets.
Before-and-after time studies on the target workflow, error-rate reduction, throughput gains, and CX or NPS impact where applicable. We baseline the human-only path and report monthly savings — not "AI score" vanity metrics. Most engagements pay back inside 6 months.
No. We default to data-processing-only API tiers (OpenAI/Anthropic/Azure OpenAI/Bedrock) that do not train on customer data. For sensitive use cases — finance, health, legal — we build with self-hosted models or VPC-isolated deployments and document the data path so your compliance team can audit it.
Our team works across time zones to serve clients in the United States, United Kingdom, UAE, Canada, and Australia. We offer EST morning calls, GMT afternoon calls, and async communication via Slack. English is our primary working language. Fill in the form and we'll respond within 24 hours — guaranteed.