AI-Driven Data Quality & Data Enrichment Platform

Quickly and easily identify duplicate records in CSV files by discovering inconsistent company names, individual names, or street addresses. Generate match reports in real-time for higher quality data and "Data ROI": match-wizard.interzoid.com - uses algorithms, knowledge bases, and applied AI to discover data redundacy issues.

You don't just and watch any more than you would sit and watch an LLM write a book. It's more like submitting an outline, describing the major plot, writing chapter by chapter summaries, ensuring certain major points are made, providing detailed main characters, and then editing when it's finished before any kind of first draft publication.

@BrainyMarsupial I'm curious about how this works logistically. Does the company pay for the tokens or do the engineers? Do they double-check the work or just commit it as long as it pasts the tests. Do they even write/read the tests? What do they do in the mean time? Pretend to work?

I keep hearing that software engineers don’t write much code anymore and it’s mostly AI now. Can any software engineers confirm how true this is? Do you just drink coffee and watch Claude code all day now?

@ivanburazin Curating AI use through an IDE is for now a good approach

A former PM at Meta recently posted: "If you're still using an IDE, you'll be laid off next" Aggressive? A bit, yeah. It is, however, more directionally correct than rage bait. Half our engineers still use IDEs (but with Claude Code running inside). The others don't use them at all. Just agents and orchestration. IDEs aren't dead yet, but they're about to be.

Découvrez Interzoid Data Matching Wizard — une solution rapide et simple pour faire correspondre et regrouper des enregistrements similaires sans configuration complexe. Idéal pour éliminer les doublons et améliorer la qualité de vos données. match-wizard.interzoid.com

AI-powered fuzzy matching for company names, people, and addresses: no code, just go. match-wizard.interzoid.com

Identifique rápida y fácilmente registros duplicados en archivos CSV mediante la detección de nombres de empresas, nombres de personas o direcciones inconsistentes. Genere informes de coincidencia en tiempo real para una mayor calidad de los datos y un mejor "ROI de datos": match-wizard.interzoid.com - utiliza algoritmos, bases de conocimientos e IA aplicada para descubrir problemas de redundancia de datos.

Identifizieren Sie schnell und einfach Dubletten in CSV-Dateien, indem Sie uneinheitliche Firmennamen, Personennamen oder Straßenadressen aufspüren. Erstellen Sie Abgleichberichte in Echtzeit für eine höhere Datenqualität und einen besseren „Data ROI“: match-wizard.interzoid.com – nutzt Algorithmen, Wissensdatenbanken und angewandte KI, um Datenredundanzen aufzudecken.

Identifiez rapidement et facilement les doublons dans vos fichiers CSV en détectant les incohérences dans les noms d'entreprises, de personnes ou les adresses postales. Générez des rapports de correspondance en temps réel pour une meilleure qualité de données et un meilleur « ROI des données » : match-wizard.interzoid.com - utilise des algorithmes, des bases de connaissances et l'IA appliquée pour découvrir les problèmes de redondance de données.

IBM, I.B.M. Corp, and International Business Machines are the same company - can you identify instances of this in your data? Quick and easy way to discover -> match-wizard.interzoid.com

@denicmarko interzoid.com = better data ROI

Share your websites. Let's get you some traffic! 🚀

blog.interzoid.com/entries/combin… Single-field matching has been the standard approach in data quality for years. You match on company name, or on address, or on a person's name and hope for the best. But anyone who has worked with real-world data knows the challenge: matching on one field alone is not enough for common data. "James Kelly" appears dozens of times in a CRM. "IBM" shows up at hundreds of addresses. "100 Main Street" could be anywhere. Interzoid's Combination Similarity Key APIs solve this by generating a single AI-powered hashed similarity key from two data fields simultaneously. The resulting key only matches when both fields are similar—giving you dramatically higher precision for deduplication, record linkage, and fuzzy matching across messy, inconsistent datasets.

@Bencera 10x engineers are more adept and knowing *what* to build... that won't change, and will likely increase their distance.

How to build a team in 2026: Hire for 3 things only: - AI-pilled (AI is the smartest person in the room now. "10x engineer" is dead.) - High ownership (every person owns a KPI, not tasks) - Highly motivated (if they're not pumped they're dragging everyone down) Then 2 rules: - Ship fast. Bugs in prod are fine. - Monitor for 1-2 hrs after every deploy. That's it. No juniors. No managers. No layers. Just killers who obsess over metrics. The companies we partner with at Polsia are built this way. In a year you will just need one person + AI. That's the future I'm building now.

@Hadley And have we defined AGI yet?

I’ve heard from 2 people in the last 2 days that internally Anthropic expects to have AGI in 6-12 months. That’s faster than Dario has stated publicly. Plan your business and personal finances appropriately.

Buying Signals API - Identify companies actively in-market to buy by tracking leadership changes, funding events, hiring patterns, technology shifts, and strategic initiatives — all from a single API call. interzoid.com/apis/get-buyin… This API tracks and identifies organizational changes that indicate a company is likely in-market to purchase products or services. It analyzes leadership changes, funding events, technology shifts, hiring patterns, and strategic initiatives to surface actionable buying signals for sales and business development teams. Companies using buying signal data typically see 2-3x improvement in pipeline conversion rates by enabling sellers to prioritize accounts showing genuine propensity to buy. See it in action: buying-signals.interzoid.com

This API identifies the technology stack used by any website based on a domain name lookup. It returns comprehensive technical infrastructure details including frontend frameworks, backend platforms, CMS systems, hosting providers, CDN services, analytics tools, security technologies, and e-commerce platforms. This saves hours of manual research by instantly revealing a site's tech stack, enabling better sales targeting, competitive analysis, and technology market research at scale. -> interzoid.com/apis/get-tech-… See it in action here: tech-stack.interzoid.com

Get buying signals for any company or organization: buying-signals.interzoid.com

Find coffee shops by address programatically - add to CRM or Marketing Apps - interactively or via API coffee-finder.interzoid.com

Get the tech stack for any Website -> tech-stack.interzoid.com

@garrytan Is there anything inherent to Markdown files that make this so? Or were they just in the right place at the right time?

In the AI revolution, low status and useful is where the alpha is. Markdown files look like shit and have basically zero status. But they are insanely useful. Humans can read them, models can read them, agents can write them, diff them, transform them, chain them.

@rpreeti198 Nice medallion architecture! The Bronze→Silver layer is perfect for adding data quality checks. We see dbt users pipe raw data through our matching APIs there to catch dupes before they hit Gold tables. Clean foundations = better analytics.

🚀 Just shipped a layered data pipeline project Built a full **Bronze → Silver → Gold** architecture using dbt — raw staging data flows through cleansing and transformation layers before landing in clean, analytics-ready dimension tables.