The Pilot Purgatory
If you run operations at a mid-sized company, you've likely seen this play out: An agency comes in, builds a slick "Proof of Concept" (PoC) using an off-the-shelf LLM, and presents a nice slide deck showing how it works on perfectly clean, curated dummy data.
You pay the invoice, everyone claps, and the system is never used again.
Why? Because production isn't clean.
In production, documents are scanned sideways. End-users mistype their emails. APIs rate-limit you. The edge cases aren't 1% of the work—they are 80% of the work.
What building for production actually means
At Buteforce, we refuse to do pilot projects or strategy consulting, because they reward the wrong behavior. They reward making something that looks like it works, rather than something that actually works.
When you build a production-grade AI system, you have to account for:
- Error Handling & Fallbacks: If the primary OCR engine fails to read a smudged receipt, does the system crash, or does it seamlessly route to a fallback Vision Language Model?
- Human-in-the-Loop (HITL): Autonomous agents are great, but for high-stakes decisions, you need a deterministic pause where a human verifies the output before it proceeds.
- Data Residency & Security: You can't just pass PII to a public API endpoint. You need secure, compliant infrastructure.
How we do it differently
We start by identifying the exact, measurable operational drain. If it's a team spending 20 hours a week extracting invoice data, that's our target.
We don't build a generic "AI assistant." We build a deterministic pipeline wrapped around an AI inference engine (like Mistral 7B) that specifically reads those invoices, structuring the data exactly as your ERP requires.
The deliverable isn't a deck. The deliverable is 20 hours back in your team's week.