// AI App Development · End-to-End Observability · LLM Observability · Agentic Systems
20+ years building distributed systems at scale — now applying that same rigour to agentic AI application development, end-to-end observability, and the kind of deep troubleshooting that turns production incidents into solved problems.
Not wrappers around LLMs. These are production applications built with architectural discipline — structured data models, durable state, and real integrations.
// Plan · Track · Brief · Adjust — all from one place
An AI agent that connects to the tools you already use — task managers, calendars, project trackers, document stores, and more — to give you a single unified planning cockpit. Upload a spec or planning document and it extracts structured tasks, proposes a timeline, and routes work to the right destination. Every morning: a personalized briefing. Fall behind — it proposes a revised plan and waits for your approval before changing anything.
// Define intent. Configure sources. Receive signal.
A self-hosted automation platform that turns your information needs into recurring, structured outputs — delivered where and when you need them. Tell the platform what you want to track, where to look, and how often to run. It handles the rest: fetching, filtering, ranking, summarizing, and delivering a curated result on your schedule. Built for teams who want signal without noise, and full control without vendor lock-in.
From instrumentation with OpenTelemetry or vendor SDKs (Datadog, New Relic, and others) to multi-backend routing and LLM cost monitoring — we help teams build observability that scales with their systems, not their vendor contracts.
We start by understanding your system's actual observability needs: scale, latency requirements, existing tooling, team maturity, and budget constraints. Then we propose the right architecture — not the most expensive one.
Our expertise spans the full telemetry stack: logs, metrics, traces, and RUM. We implement using OpenTelemetry as the universal collection layer and have validated integrations with Datadog, New Relic, Dynatrace, Prometheus, Grafana, Jaeger, and Loki. Switching backends should be a YAML change — not a re-instrumentation project.
We're also building out our LLM observability practice — cost attribution, token usage monitoring, latency tracking, and multi-model orchestration visibility for teams running production AI workloads.
Instrument your services using OpenTelemetry SDKs or vendor agents (Datadog, New Relic, and others — any language). Covers batching, tagging, sampling, and PII scrubbing before export.
Design and implement a vendor-neutral pipeline — OpenTelemetry Collector, Datadog Agent, or a hybrid — that routes to your backends of choice, switchable by config with no code changes.
Correlated Grafana dashboards linking trace IDs to logs and metrics. Context propagation that makes debugging a distributed system actually tractable.
Cost attribution per model/feature, token usage monitoring, latency SLOs for AI endpoints, and multi-model orchestration tracing for production AI systems.
Built a complete, self-hosted observability stack to validate zero-code backend switching at scale. A fully instrumented distributed application — multiple services, real traffic patterns, and simulated failure modes — routed through a single collection layer to multiple backends simultaneously, with no application code changes required between them.
✓ Result: Switch between self-hosted and SaaS observability backends by editing a single config file — no re-instrumentation, no code changes, no downtime. Full correlated trace-to-log visibility. Entire environment reproducible from a single bootstrap script using IaC.
AppInsight.dev is a team of experienced architects and engineers with deep domain expertise across distributed systems, observability, end-to-end troubleshooting, and applied AI. We bring 20+ years of hands-on engineering to every engagement — from the architecture board to the production deployment.
Our work is grounded in one consistent conviction: systems that operate at scale need to be correct under pressure, and that requires investing deeply in instrumentation, tracing, and the kind of end-to-end visibility that turns production incidents from guesswork into root-cause analysis. The modern observability toolchain — OpenTelemetry, Datadog, and the broader ecosystem — is not something we've read about. It's something we've built with, in anger, on live systems.
In parallel, we've been building production AI applications — not demos, but tools with real data models, real integrations, and real architectural discipline. The AI Chief of Staff and Signal Flow Orchestrator came out of that practice. They're built with the same rigor we apply to any distributed system: durable state, explicit data contracts, human-in-the-loop controls, and no silent failures.
AppInsight.dev is where the consulting practice and the product work live together — because the best advice comes from teams still shipping the code.
20+ years in workload management, resource governance, and systems design at scale.
End-to-end instrumentation using OpenTelemetry or vendor SDKs (Datadog, New Relic, and others), Collector architecture, multi-backend routing, and context propagation.
Production AI tools built with architectural discipline — structured data models and real integration depth.
Cost attribution, token monitoring, latency SLOs, and multi-model orchestration tracing.
Whether you're exploring observability architecture, looking to build an AI productivity tool, or just want to talk distributed systems — drop a message or book time directly.