How Tasq fits your existing stack
One of the first questions we get from operators is some version of: "We already have Pi. We already have WellView. We already have Snowflake. Where does Tasq fit?"
Fair question. The short answer is that Tasq does not replace any of those systems. It sits between them and your team.
The problem is not data. It is what happens after the data.
Most production operations teams have plenty of data infrastructure. SCADA is collecting readings every few seconds. Pi Historian is storing it. WellView has the well records. Snowflake or Databricks might be warehousing everything for analytics.
The gap is not on the collection side. The gap is on the intelligence side. Someone still has to look at the data, spot the pattern, figure out the root cause, create a work order, assign it, follow up, and report on it. That chain is almost entirely manual at most operators today.
Where Tasq sits
Tasq connects to the data systems you already run. On the input side, that means:
- SCADA and RTU data feeds
- Pi Historian for time series history
- WellView for well configuration and records
- Snowflake or Databricks if you have a centralized warehouse
- CygNet, Xspoc, and other field data systems
- Accounting and ERP systems for cost and production data
On the output side, Tasq produces things your team actually needs to act on:
- Detected events with root cause already attached
- Work orders created and assigned automatically
- Vendor bids surfaced for intervention decisions
- Reliability reports generated per event
- Downtime logs with hours, volume, and reason filled in
- Budget forecasts updated as events are resolved
The layer in the middle is where Tasq does the work. Signal search scans your SCADA for patterns. Build Your Own Model lets your engineers turn what they know into live AI models without writing code. Agentic workflows handle the full chain from detection to resolution. Root cause classification tags every event. Automated downtime logs the production impact. AI data entry handles the field capture side.
What this means in practice
Take a concrete example. An engineer at an operator we work with wanted to catch liquid loading events earlier. Before Tasq, the process was: pull data from Pi, open it in Excel, eyeball the casing and tubing pressure trends, try to spot the pattern, then go well by well across 400+ assets. That is a full time job for one person on a good day.
With Tasq, the engineer dragged over a known liquid loading pattern in the SCADA viewer. Tasq searched 12 months of data across every well in under 30 seconds. It found 37 matches. Each one had a root cause attached, a severity score, and a recommended action. Work orders were created for the high priority wells. Vendor bids for velocity string were surfaced inline for the ones that needed intervention.
That is what the layer does. The data was already in Pi. The well records were already in WellView. What was missing was the intelligence that connects detection to action without a human doing every step by hand.
No rip and replace
We designed Tasq specifically so you do not have to change your existing infrastructure. If you run Pi, keep running Pi. If you are on Snowflake, great. Tasq reads from those systems. It does not compete with them. It makes them more useful by doing something with the data they are already collecting.
Most deployments take about two weeks. We connect to your data sources, configure the models, and start scanning. There is no six month integration project. No data migration. No retraining your team on a new platform.
The takeaway
Your stack already collects and stores the data. Tasq is the layer that turns it into detected events, classified root causes, work orders, vendor bids, and reliability reports. Automatically, continuously, and across your entire asset base.
If you want to see how it connects to your specific stack, reach out. We will show you on your real data.