you@inventive:~$ pipeline --status check
# Data pipelines, integration, and analytics infrastructure for companies that need their data to actually work.
// the problem
A business leader gets a BI tool - Tableau, Power BI, something that cost a lot in a procurement meeting and now lives on fourteen people's desktops, opened twice a month. The pitch was clean: unified reporting, real-time visibility, data-driven decisions. Everyone clapped. Six months in, someone asks a question that matters - revenue by region, broken down by product line, filtered to Q3 - and the answer is "we'd have to pull that manually." Or worse: "the numbers don't match between those two reports and we're not sure why."
That's not a dashboard problem or a BI-tool problem. It's a data infrastructure problem - because nobody fixed the foundation before building the reporting on top of it. And now your AI vendor is promising transformative insights from data that lives in 47 spreadsheets, 3 databases, and someone's email inbox. The gap between "we have data" and "we can do something with this data" is almost entirely an engineering problem. We fix it.
// free data assessment
A real look at what's broken in your pipelines - yours to keep, whether or not we build it.
// what we build
Pick the one that's keeping you up at night. We do all six - usually on the same engagement.
Automated ETL/ELT that moves, transforms, and loads data reliably - without a manual export every Monday or a job silently failing at 3am that nobody notices until noon. Data arrives where it needs to be, in the format it needs, on schedule. Not "usually." Every time.
Your CRM doesn't talk to your ERP. Your ERP doesn't talk to your warehouse. And three systems each define "customer" slightly differently. We map, reconcile, and connect them so your data reflects reality instead of whichever source happened to load last.
The difference between a data warehouse and a data swamp is architecture. We design storage layers that are organized, queryable, and built for how your team actually accesses data. Snowflake, Databricks, PostgreSQL, SQL Server - the platform follows the use case.
The reason your dashboards are wrong isn't the BI tool - it's the data underneath it. We build the infrastructure layer that makes numbers consistent, queries fast, and answers reliable. So "I'll have to get back to you on that" stops being the board-meeting answer.
Raw data is not AI-ready data. Schema inconsistencies, unlabeled records, historical data that doesn't match today's schema, fields that mean different things per system - this is the most common reason AI projects stall before producing anything. We close that gap.
Not every use case needs real-time data. But when it does - fraud detection, operational monitoring, live personalization - four-hour batch processing isn't good enough. We build streaming architectures with Kafka and similar tools, and tell you honestly when you don't need them.
// the part nobody tells you about AI
80%
of AI work is data engineering
Ten million rows where "customer ID" means three things depending on the acquisition cohort. A product catalog nobody has systematically cleaned since 2021. Operations data in a system never designed to be queried. The AI project that fails isn't failing because of the model - it's failing because the data feeding it is inconsistent, incomplete, or impossible to train on. You don't need a better AI vendor. You need better data.
// the proof
We built on Azure Data Factory to connect field operations data across oil-and-gas infrastructure. When your operational data lives in disconnected field systems, you can't run the business on gut instinct and phone calls forever. The pipeline made real-time operational visibility possible - not a nice-to-have in an industry with that much at stake.
We built a real-time monitoring dashboard on Grafana, Prometheus, and Next.js for TAMU's network infrastructure. The data was already being generated - it just needed infrastructure to make it actionable. The result: live visibility into a network serving a major university system, in the hands of the people who needed to act on it.
Across both: before we built anything useful, we had to fix the data. The product came second. The infrastructure that made the product possible came first. That order isn't a preference - it's the only order that works.
// the stack
ready when you are
Let's figure out what it would take to make it useful. No slide decks, no pressure - just a real conversation about what you're trying to do with your data.
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