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Xfactr.ai

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data engineering 

Pipelines that feed AI.
Not just reports.

ETL/ELT · streaming · feature engineering · observability
AI-native from ingest to inference. No retrofitting.

 

Talk to Our Team

Three views

Panel view. Analytics. Alerts.

Source

IoT · ERP · APIs

Legacy · SaaS · Files

→
Ingest

Kafka · Kinesis

Event Hubs · Pub/Sub

→

✦ AI: Anomaly Detect

→
Process

Spark · Flink · dbt

Clean · Validate · Join

→

✦ AI: Schema Map · Classify

→
Enrich

Feature Store

Embeddings · Rollups

→
Store

Lakehouse

Snowflake · Databricks

→
Serve

Models · RAG · APIs

Training · Inference

Core capabilities

What we build. No filler.

pipeline.ingest

–mode=stream –src=iot,erp,api –cdc=true

Kafka, Kinesis, Event Hubs batch and streaming from any source. Change data capture. Zero data loss. Handles messy, multi-protocol data.

pipeline.transform

–engine=spark,dbt –quality=enforced

Distributed transformation at scale. dbt for SQL-layer logic. Schema validation, deduplication, merge. Clean data not shuffled data.

features.engineer

–store=feast –embed=true –for=llm,ml

Feature pipelines that produce model-ready inputs: aggregates, embeddings, time-series rollups. LLM-assisted schema mapping for complex sources.

ai.detect

–target=pipeline –mode=anomaly,drift –alert=pagerduty

AI running inside the pipeline catching bad data before it reaches a model. Drift detection. Auto-classification of unstructured content at ingest.

lakehouse.provision

–fmt=delta,iceberg –engine=snowflake,databricks

Lakehouse architecture: compute/storage separated, open table formats, vector-ready. Designed for AI workloads, not legacy BI query patterns.

pipeline.observe

–sla=true –lineage=openlineage –heal=auto

Great Expectations, Monte Carlo, dbt tests every run. Lineage tracked. AI-driven SLA monitoring with self-healing on known failure modes.

What we connect to.

The exact stack. Production proven.

Ingest
KafkaKinesisEvent HubsPub/Sub Debezium CDCAirbyteAWS GlueADF
Process
Apache SparkApache Flinkdbt DataFlowTrinoEMR · Dataproc
Orchestrate
AirflowPrefectDagster Step Functionsdbt Cloud
Store
SnowflakeDatabricksBigQueryDelta LakeIcebergRedshift
Features
FeastTectonPineconepgvectorWeaviateEmbedding Pipelines
Quality
Great ExpectationsSoda CoreMonte Carlodbt testsOpenLineage

<90s

Sensor-to-model latency on production IoT streaming pipelines replacing overnight batch

15K+

Wells automated AI-driven ingestion replaced manual engineer review per well
 

Self-heal

AI anomaly detection quarantines bad data before it corrupts a model or dashboard

Where these capabilities apply

Edge-to-Cloud AI across our platforms and services.

Platform
Edge Foundry
Buildings, datacenter, grid, cloud and security the complete edge platform.
→ edge-foundry
Service
IoT Application Development
End-to-end IoT applications across edge devices, protocols, software and cloud platforms.
→ iot-application-development
Service
AI Applications
Predictive analytics, computer vision and intelligent AI solutions built on edge telemetry.
→ ai-applications
Service
Data & Analytics
Cloud-native data platform that makes edge telemetry searchable, actionable and scalable.
→ data-analytics
Service
AIOps
AI-driven monitoring and anomaly detection from edge signals to cloud incident resolution.
→ aiops
Service
DevOps & MLOps
Firmware pipelines, CI/CD automation, model lifecycle management and deployment.
→ devops-mlops
Capability
AI Agentic AI
Agent and LLM architecture that acts autonomously on edge telemetry and enterprise workflows.
→ ai-agentic-ai
Service
Smart Enterprise Solutions
Enterprise-grade AI solutions connecting edge, cloud and business applications seamlessly.
→ smart-enterprise-solutions

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Your AI is only as good as its data pipeline.

 Tell us your sources, destinations, and latency requirements.

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