ChatGPT is not a strategy. It's a tool. The businesses getting real value from generative AI aren't the ones with the best AI strategy document — they're the ones that built something, put it in front of real users, and iterated. LoopStack builds those things.
Beyond the hype, there are concrete, reliable use cases where LLMs like ChatGPT deliver measurable value. Here's what we see working consistently in production.
A GPT-powered chatbot trained on your documentation and FAQs. Handles tier-1 questions instantly, 24/7. Routes anything complex to a human with full context.
Feed in invoices, contracts, or emails and get structured data out. LLMs understand context that templates miss — variable formats, scanned documents, mixed layouts.
Staff ask a question in plain English, the system searches your documentation, policies, and procedures, and returns a direct answer. Better than search.
Inbound emails classified, prioritised, and responded to with AI-drafted replies for human review. Cuts email handling time significantly.
Inbound enquiries analysed and scored automatically. High-value leads routed immediately. Everyone else gets a personalised, accurate response.
Transcripts turned into structured summaries, action items extracted, owners assigned, and tasks created in your project management tool automatically.
We don't have a preferred vendor. We use whichever model performs best for your specific task. Here's the honest breakdown:
Best for conversational tasks, coding assistance, and broad general reasoning. Mid-range API pricing. Slightly smaller context window than Claude.
Best for long document analysis, following complex multi-step instructions, and compliance-sensitive contexts. Large context window. Comparable pricing to GPT-4.
Best for high-volume classification, triage, and summarisation where cost matters. Less capable for complex reasoning, but excellent value at scale.
Best for sensitive data that can't leave your infrastructure, or high-volume use cases where API costs are prohibitive. Requires hosting setup.
GPT-4 powered support bot trained on the client's documentation library. Handles 70% of inbound support without human involvement.
LLM-powered extraction from supplier invoices — variable formats, scanned PDFs, mixed layouts. 3 hours of daily admin eliminated.
LLM agent that processes inbound enquiries, enriches with company data, scores and categorises, then auto-responds or escalates. Deployed in under two weeks.
Tell us what you're trying to build — chatbot, document processing, email automation — and we'll recommend the right model, the right approach, and a fixed price before you commit.
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