❓ Problem

Businesses are paying frontier prices for jobs that don’t need a frontier brain. The default move in 2026 is to send everything to the biggest model available, from drafting board memos to tagging invoices. That’s like hiring a Nobel laureate to sort the post.

The bills are starting to show it. Coinbase CEO Brian Armstrong says the company is routing prompts to cheaper models and has, in some cases, kept AI costs roughly flat while token usage grows exponentially. His prediction: 80% of workloads will run on 99% cheaper models within 12 to 18 months. Even sceptics agree on the direction. Box CEO Aaron Levie called the numbers a bit extreme, but said high-volume work will move to cheap models while leading models keep the high-end stuff.

Here’s the kicker. A cheaper model isn’t automatically a worse one. Coinbase post-trained Qwen3.5-9B, a small open-weight model, on its own fraud data, and against an Opus 4.5 agent it improved F1 by 9.6%, with precision up 7.5% and recall up 12%, while cutting latency by 55%.

Most companies are sitting on exactly the kind of data that made this possible: years of resolved support tickets, approved invoices, flagged transactions. What they lack is a team that can turn that data into a model. Coinbase had in-house ML engineers and a three-part engineering programme. Everyone else has a spreadsheet and a growing OpenAI invoice.

✅ Solution

A done-for-you platform that turns a company’s historical data into a specialised AI model for one specific job, and proves it works before anyone switches it on.

• Upload examples, define “good”. The customer uploads labelled history (say, 50,000 support tickets and where they ended up) and sets the success metric: accuracy, escalation rate, cost per ticket.
• The platform does the ML. It cleans and splits the data, picks a suitable open-weight base model, fine-tunes it, and holds back unseen examples for testing.
• A head-to-head scorecard before deployment. The custom model is tested against whatever the customer uses today (a frontier API, a rules engine, a human queue) on accuracy, response time and running cost. No vibes, just a scorecard.
• Then it runs and stays sharp. Managed hosting, drift monitoring and periodic retraining as new data comes in. As Coinbase puts it, owning intelligence means owning the benchmark, the training process and the serving system, not just the weights.

The wedge is deliberately boring: support ticket classification. High volume, clearly labelled history, easy-to-measure outcomes. Once one use case works, the same pipeline extends to invoice coding, document extraction, fraud flags, lead scoring and compliance review.

📊 Key Numbers

Market size

• TAM: enterprise generative AI spending hit $37 billion in 2025, up from $11.5 billion in 2024 and $1.7 billion in 2023. That’s the total pool of AI budget a cheaper, better model can claw back.
• SAM: the model API slice is where the savings live. Enterprise model API spending jumped from $3.5 billion to $8.4 billion in roughly six months. If even a fifth of that goes on repetitive, narrow tasks (classification, extraction, routing), that’s a $1.7 billion-plus annual pool on 2025 figures alone, and growing fast. (Our estimate.)
• SOM: mid-market companies with high-volume workflows but no ML team. Think fintechs, insurers, e-commerce support teams, logistics firms and BPOs. A few thousand realistic buyers in the US alone.

ARR potential

• Bottom-up: 300 customers running an average of 1.5 models each, at ~$4k/month for hosting, monitoring and retraining, is ~$21.6M ARR.
• Add a setup fee of ~$15-30k per use case and that’s another ~$7-13M a year at that customer count.