GPT Is Not the Right Tool for Every Data Problem
Why large tabular forecasting tasks often need LightGBM or CatBoost instead of a GPT model, and what to do before choosing an AI approach.
Perfectory AI
AI Automation Insights for Operations, Procurement, Support, and Reporting
Practical AI automation articles from Perfectory AI for teams researching ERP automation, support triage, procurement intelligence, reporting automation, and agentic decision systems.
AI Automation POC: What to Prove Before You Scale
A useful AI automation POC tests one real workflow, with controlled data access, human review, and agreed business metrics. It should answer whether to scale, not pretend to be production-ready.
Why we are moving from GPT-5.1 to GPT-5.6
A founder-led read on why we tested GPT-5.6 against GPT-5.1, what improved, what got worse, and why prompt tightening still matters before migration.
When an AI Assistant Blocks the Customer, the Workflow Is Broken
A founder-led take on a bank support flow where an AI assistant kept repeating the app path and delayed a simple credit limit request until the customer got angry.
GPT-5.6 Production Migration Checklist: Evaluate Before You Switch
A production migration is earned through task-level evidence. Use this checklist to evaluate GPT-5.6, Luna, Tera, and Sol for quality, cost, observability, and workflow fit before rollout.
Why restaurant booking assistants fail on the real user flow
A founder’s note on restaurant booking assistants: good voice is not enough if the system cannot handle language switches, party-size changes, and real rejection paths.
Vibe coding is useful. It is not a substitute for expertise.
A founder’s view on vibe coding: it can help with basic sites and quick drafts, but complex apps still need technical judgment, debugging, architecture, and review.
AI can make developers faster. It can also make them less sharp.
A founder-led take on the tradeoff between using AI for speed and keeping real coding skill, systems thinking, and judgment intact.
Human-in-the-Loop AI: Review Rules for Business Automation
AI can draft and triage fast, but business workflows still need context, review, and clear approval rules before an output reaches a customer or system.
Build for Data, Not for a Single Model
Founder guidance on why durable AI products should be built around data, process, and workflow—not around one model or one library.
AI Is Changing Work, But It Is Not Replacing Experts
AI can speed up routine work, but human direction, review, and business context still matter. Here is where it helps, where it fails, and what teams should do next.
AI Projects Fail More Often on Bad Data Than Bad Prompts
Most AI projects fail because of weak data, unclear workflows, and unrealistic expectations. Start with the problem, clean the inputs, then choose the right tool.
Research Log: Benchmarking Recommendation Quality in Agent Systems
We are building a compact evaluation set to compare recommendation quality across scenarios, confidence levels, and policy constraints.
New Module: Procurement Policy Simulator for Fast What-If Testing
We added scenario simulation so pricing and procurement teams can test policy changes before rollout and estimate impact on margin and competitiveness.
Research Note: From Forecasts to Actions in One AI Loop
We are testing a pipeline that combines forecasting with recommendation logic, so teams can move from prediction to decision in one step.
How We Turn a Successful POC Into a 90-Day Delivery Roadmap
After validation, we move quickly into production planning with milestones for reliability, integration depth, and measurable KPI tracking.
From Brief to Working POC: Automated Procurement Intelligence
We delivered an end-to-end POC that compares competitor pricing with internal data, generates pricing recommendations, and adds forecasting for policy planning.
Our Rule for POCs: Prove Value, Then Scale
For us, a POC is not a slide deck. It is a measurable test with real data, real integrations, and clear go/no-go criteria for business impact.
