Infrastructure

The infrastructure behind the signal.

earlysignal isn't a thin layer over a single API call. It's a pipeline that ingests, reasons over and explains business data end to end.

Architecture

From raw data to decision room.

Data ingestion

CRM, ERP, CSV, APIs and documents are pulled into a unified event stream.

Normalisation

Records from different systems are reconciled into a shared business schema.

Context engine

Events are linked to accounts, workflows, locations and time periods.

AI reasoning

Azure OpenAI interprets the business question and reasons over the context.

Pattern detection

Time-series and anomaly models identify statistically meaningful movement.

Signal engine

Meaningful changes are ranked, scored and surfaced above the noise.

Evidence engine

Supporting records are retrieved so every signal can be traced to its source.

Decision room

The AI proposes options with expected impact and risk for the team to weigh.

Azure stack

Built on Azure's AI infrastructure.

Azure OpenAI

Reasoning, natural language understanding and business question interpretation.

Azure Machine Learning

Machine-learning workflows and predictive / pattern models.

Azure AI Search

Retrieval across connected business documents and knowledge sources.

Azure Data Lake / Blob Storage

Scalable storage for raw and processed business events.

Azure Functions

Event-driven processing as new business data arrives.

Azure Kubernetes Service

Scalable AI workloads and application services.

Microsoft Entra ID

Identity and access management.

Azure Key Vault

Secrets and credential management.

Azure Monitor

Application and infrastructure monitoring.

Under the hood

A heavier workload than a single API call.

A single business question triggers retrieval, reasoning and modelling across several systems — not one prompt to one model.

Large-scale event processing

Every connected business event is ingested and normalised continuously.

Document understanding

Reports, contracts and internal documents are parsed for relevant context.

Embeddings & semantic search

Business concepts are indexed so related events can be retrieved by meaning.

Anomaly detection

Statistical models flag movement that falls outside expected ranges.

Time-series analysis

Trends are compared against prior periods to separate noise from signal.

Pattern recognition

Recurring structures across workflows, segments and locations are identified.

LLM reasoning

Azure OpenAI interprets business questions and reasons over retrieved context.

Evidence retrieval

Supporting records are pulled so every explanation can be traced to its source.

Decision modelling

Candidate actions are scored for expected impact and risk.

Performance, honestly stated

Realistic numbers from an early-stage MVP.

18s

Average investigation time

79%

Signal review rate

<3s

Evidence retrieval