Field notes from a team
that ships production AI.
This is the working log behind softnict.com — how we actually built the automations, chatbots, and platforms in our portfolio, plus what we're learning about AI engineering along the way.
$ softnict status --live
→ ai_requests_per_day500K+
→ avg_time_saved80%
→ ocr_field_accuracy98%
→ projects_shipped150+
→ client_satisfaction5.0 / 5
$
git log --oneline --work
Recent builds
A running log of shipped projects — the problem, what we built, and the number that moved.
AI Email Automation for Customer Support
Replaced a manual support inbox handling hundreds of daily emails with an AI system that reads, classifies, and drafts replies automatically.
AI SEO Content Automation Platform
One topic in, a full SEO report out — keyword research, a ready-to-publish article, competitor analysis, and a ranking strategy, delivered automatically.
OCR Document Processing System
AI-powered document processing that extracts structured data from invoices, contracts, and customs forms at 98% accuracy.
from the blog
Latest writing
Notes on AI engineering, automation, and building software that survives contact with production.
RAG vs. Fine-Tuning: Choosing the Right Approach for Your AI Product
Both let a language model answer from your own data — but they solve different problems, cost differently, and fail differently. A practical framework for picking one.
Jul 10, 2026What "98% OCR Accuracy" Actually Means (and When It Isn't Good Enough)
Accuracy numbers in document AI get thrown around without context. Here's how to read them, and which fields actually need to hit that number.
Jun 22, 2026AI Agents vs. Chatbots: What's Actually Different
The terms get used interchangeably, but they describe systems with very different capabilities, risks, and engineering effort. Here's the real distinction.
May 30, 2026Have a project that looks like these?
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