CASE STUDY / 02 · INFRASTRUCTURE INTELLIGENCE

GRIDPULSE / Evidence before inference.

An evidence-first infrastructure intelligence control plane that separates observed telemetry, validation, prediction and explanation. The core design keeps the evidence record distinct from optional AI-derived analysis.

Next.js App RouterReactTypeScriptPostgreSQLZodAzure MLMicrosoft FoundryVercel

01 / Problem statement

Why it exists.

Infrastructure reports can be fragmented, noisy or delayed. A useful control plane needs to represent what was observed separately from what a model predicts, and must not turn a missing or invalid model result into a fabricated operational answer.

02 / System path

Architecture at a glance.

  1. NODE / 01Human telemetry
  2. NODE / 02Next.js API + Zod
  3. NODE / 03PostgreSQL evidence
  4. NODE / 04Azure ML
  5. NODE / 05Foundry explanation

STEP / 01

Accept structured telemetry with server-side validation.

STEP / 02

Normalize reports into a PostgreSQL-backed evidence layer and correlate reports within a bounded context.

STEP / 03

When configured, send structured features to Azure Machine Learning for a bounded score.

STEP / 04

When configured, use Microsoft Foundry / Azure OpenAI to produce an explanation grounded in supplied evidence and prediction.

03 / Engineering decisions

Trade-offs are part of the system.

DECISION / 01

Observed is not predicted

A model prediction is not proof that a disruption occurred. The validation layer and prediction layer are deliberately separate.

DECISION / 02

Fail closed on missing intelligence

If required optional AI services are missing, unavailable or return invalid output, the intelligence path should withhold the result instead of inventing one.

DECISION / 03

Bounded external services

Model version, confidence, generation time, validation state and evidence are distinct fields rather than a single unexplained verdict.

04 / Failure modes & limits

Where it can fail.

05 / Source map

Where to inspect.

src/app/api/

README-documented routes for dashboard data, health, telemetry ingestion and intelligence.

src/lib/

Database access, deterministic demo helpers and intelligence-service integration.

db/schema.sql

Database schema reference; incremental changes are under db/migrations/.

ml/ and docs/

Model training/scoring contracts and integration, deployment and validation playbooks.

The map identifies only paths confirmed by available project documentation. Follow the repository link for the full current tree.

06 / External evidence