From Telemetry to AI-Ready Data Products

In the last post, we showed how Prompt Feature Engineering (PromptFE) turns raw telemetry into decision-ready signals.

But that raises the next question:

What are we actually creating?

The answer is simple:

AI-ready data products.

The Missing Layer in AIOps

Most AIOps strategies assume this:

Collect enough data → feed it into a platform → get insight.

In reality, something breaks in the middle.

You have:

• Telemetry pipelines

• Platforms like InfluxDB and Prometheus

• Dashboards and alerts

But still:

• Teams interpret data manually

• AI outputs lack clarity

• Automation is unreliable

This is not a tooling problem.

It’s a data quality problem.

Why Raw Telemetry Doesn’t Scale

Telemetry is detailed, but not meaningful.

A system might show:

• Latency = 23ms

• Packet loss = 0.2%

• Jitter = 4ms

But the real question is:

“Do I need to act?”

That answer is not in the data.

It comes from experience.

And today, that experience sits:

• In engineers’ heads

• In runbooks

• In scattered tools

This is why AIOps struggles.

The data has no built-in meaning.

Step Change: From Metrics to Data Products

PromptFE solves this by changing the output.

Not more dashboards.
Not more alerts.

Better data.

Instead of raw metrics, you create a data product:

Service State: Amber
Confidence: High
Context: Latency trending above baseline across shared path

This is:

• Structured

• Consistent

• Explainable

• Actionable

How PromptFE Works (In Practice)

There’s nothing mysterious about it, it’s a system of work.

1. Ingest telemetry from existing systems
Data flows in from platforms like InfluxDB, Prometheus, network devices, and APIs.

No rip-and-replace. Just integration.

2. Subject matter experts define meaning
Using a sandbox GUI, engineers describe:

• What normal looks like

• What patterns matter

• What indicates degradation or failure

No coding required.

This is where expertise is captured.

3. Feature scoring profiles add context
Each feature is shaped using:

• Thresholds

• Trends

• Correlations

• Domain logic

This becomes a scoring profile—a reusable model of judgement.

4. Outputs are assembled into data products
Each output combines:

• The feature (score/state)

• Context (why it matters)

• Provenance (how it was derived)

The result is a consistent, explainable unit of meaning.

5. Data products feed AI and automation
These outputs are then consumed by:

• AIOps platforms

• Automation workflows

• Agentic AI systems

The AI does not guess.

It receives structured truth.

The Critical Point: This Enables Engineers

There’s a fear with AI:

“Does this replace me?”

PromptFE does the opposite.

It takes what engineers already do—interpretation, judgement, pattern recognition—

and turns it into a system.

The SME is no longer:

• Manually interpreting dashboards

• Repeating the same analysis

• Acting as a human API

Instead, they:

• Define the logic once

• Scale it across the network

• Improve it over time

AI doesn’t replace the expert.

It runs on their expertise.

Why This Improves AIOps Data Quality

Without PromptFE:

• Inputs are ambiguous

• AI must infer meaning

• Results are inconsistent

With PromptFE:

• Inputs are deterministic

• Meaning is embedded

• Outputs are reliable

You are no longer asking AI to interpret telemetry.

You are giving it engineered truth.

Where This Fits in the Stack

The architecture becomes:

Telemetry → PromptFE → Data Products → AIOps / AI / Humans

PromptFE sits between data and consumption.

It enhances tools like Grafana and ServiceNow.

The Bigger Shift

This is the real transition:

From telemetry → to insight
From insight → to action
From action → to automation

The Bottom Line

If you want better AIOps outcomes:

Don’t start with more data.
Don’t start with new tools.

Start with data quality.

Turn telemetry into structured, explainable, AI-ready data products.

And make your experts part of the system—not the bottleneck.