Maestro by Oteligence · Java telemetry, done right

Better traces.
Smarter agents.
Lower bills.

Maestro scores every method in your Java services and instruments deep where the code is complex, light where it isn’t — so the traces feeding your dashboards, your data platform, and your AI agents are complete, consistent, and governed at the source.

Java 11–21 · no card required OTel-native · any OTLP backend ~18 KB agent · zero code changes
maestro analyze · banking-app
$ maestro analyze --jars target/*.jar --goals latency,errors
Read 4 JARs · 247 methods across 4 services
Built cross-service graph · 7 edges (Feign + Kafka)
Scored vs goals · debug latency, track errors
Selected 18 of 247 methods — deep where it matters
Governance check · 0 sensitive fields exposed
→ 4 OTel extension JARs · clean OTLP to any backend
0
lines of code changed
~18 KB
agent footprint
< 5 min
to first cross-service trace
Any
OTLP backend or AI agent
The real problem

Observability’s problem was never the dashboard. It’s the data.

The OpenTelemetry agent sees the edges of your service — the request in, the database call out — but not the logic in between. So teams turn on auto-instrument-everything, drown in spans, and watch the bill climb. Then that same noisy, half-blind trace data becomes what feeds your analytics store and your AI agents.

Garbage in, garbage out. No dashboard, query, or model fixes telemetry that was already wrong before it left the JVM. And as traces become observability’s source of truth — with logs and metrics increasingly derived from them — the quality of your traces is the quality of your observability.

How it works

JARs in. Clean, governed traces out. Under five minutes.

No source access. No annotations. No rewrites. You hand Maestro your compiled application, tell it what you care about, review what it proposes, and ship.

01

Drop your JARs

One or many compiled JARs. Maestro works from bytecode — it never reads, rewrites, or sees your source.

02

Maestro maps & scores

It builds the cross-service call graph, then scores every method against the observability goals you pick.

03

You review & govern

See exactly what gets traced and captured. Toggle methods, set policy, lock it in. Nothing reaches prod unseen.

04

Deploy anywhere

Maestro generates an OTel extension JAR per service. Standard OTLP flows to whatever backend or agent you run.

Why Maestro

Three outcomes. One root cause fixed.

Fix the trace data at the source and the downstream problems dissolve — the ones you can prove today, and the one the whole industry is racing toward.

01 · The foundation

Better traces

Complete across services, deep where it counts, consistent everywhere, and governed before deploy. The signal is right because it’s right at the source — not patched up after the fact.

Provable today
02 · The payoff

Smarter agents

Your data platform and AI / SRE agents are only as good as what you feed them. Clean, complete, labeled traces turn “the model guessed” into “the model knew.” We make the new stack work — we don’t compete with it.

The frontier we’re building toward
03 · The bottom line

Lower bills

Stop paying to store noise. Maestro keeps full fidelity on the paths that matter and trims the rest — and our pricing never scales with your data volume, so we don’t profit when your bill grows.

Provable today
Where it fits

The clean-data layer for the new observability stack.

Teams are leaving closed APM behind for open standards, their own data platforms, and AI-driven analysis. Maestro is the layer that makes that move pay off — sitting between your Java services and whatever you’ve chosen downstream.

Your code
Java services
Spring / JVM, unchanged
The clean-data layer
Oteligence Maestro
scores · selects · governs
Your stack
Any OTLP destination
Grafana · Honeycomb · ClickHouse · Snowflake · AI / SRE agents

Vendor-neutral by design. Same OpenTelemetry rails, clean signal, zero lock-in. The agent layer legacy APM made proprietary — now open, governed, and yours.

Goal-driven

Tell Maestro what you care about. The engine scores from there.

The same application should be instrumented differently depending on what you’re trying to do. Your priorities set the depth — not a one-size-fits-all default.

Debug latency Track errors Watch business flows Secure sensitive paths Control cost
Outputs

Plain files. No proprietary formats. Nothing hidden.

Everything Maestro produces is inspectable, diff-able, and yours. Read it, review it in a pull request, check it into your repo.

extension.jar

An OpenTelemetry extension JAR per service — the instrumentation itself.

plan.json

A readable plan of every method, its tier, and what it captures.

graph.json

The cross-service call graph Maestro built from your JARs.

otel.config

The run configuration — standard OTel, ready to launch.

For the enterprise

The governance OpenTelemetry doesn’t give you out of the box.

Open-source OTel hands you a standard and a firehose. Maestro is the control plane that makes it safe and economical to run at scale — across every team, on every push.

Policy. Instrument these classes of methods, never those — enforced, not hoped for.
Cost budgets. Hold a service to a telemetry-volume ceiling and let scoring fit within it.
Sensitive-data visibility. See exactly which fields a method would capture before anything deploys.
One standard, every team. Consistent spans and attributes so cross-service and AI analysis actually holds together.
CI/CD drift control. The GitHub Action re-applies your decisions on every push, so instrumentation never goes stale.
Open core. The agent harness is open and OTel-native; the scoring and governance plane is yours to run.
User stories

SRE. Developer. Platform. Manager.

What changes on the ground when the trace data is finally right — told from each seat at the table.

What’s live

Available today. Java first.

Java is where enterprise observability cost and complexity bite hardest, so that’s where Maestro starts — ship-ready now. The same engine extends across the JVM and beyond next.

Java ✓ Live JavaScript On the roadmap Python On the roadmap .NET On the roadmap
Straight answers

Engineer questions. Direct answers.

Do I have to change my code?
No. Maestro works from your compiled JARs and produces a separate extension JAR plus config. Your source is never read or rewritten. What changes is your deployment — typically a launch flag — and it reverts cleanly.
Will it slow my application down?
At everyday settings the overhead is low, and you control it directly: depth is set per method and you can hold a whole service to an overhead budget. The heaviest depth is reserved for test and staging.
Does it replace my backend or APM?
No — it feeds them. Maestro emits standard OpenTelemetry, so the richer signal flows into whatever you already run, and into your data platform or AI agents alongside it.
Does it work with my data platform or AI/SRE agent?
If it ingests OTLP, yes. Maestro is vendor-neutral by design — Grafana, Honeycomb, ClickHouse, Snowflake, and agentic tools all consume the same clean traces.
How is this different from just tuning OTel sampling myself?
Hand-tuning sampling and attributes doesn’t scale across thousands of methods, and it drifts the moment code changes. Scoring does it automatically and consistently, and re-applies on every push.
What about sensitive data?
You see exactly which fields each method would capture before anything deploys, so sensitive values stay out of your traces unless you choose to include them.

Better traces. Smarter agents. Lower bills.

Start free on your own stack, or talk to us about a design-partner benchmark on your before-and-after — measured on your own services.

OTel config · all repos free · no card required