v2.0 — Spark 3.5 · Hadoop 3.4 · ml-intern integration

AI analytics for
every domain, every team

NexusIQ unifies data pipelines, ML models, GenAI, and observability into one production-grade platform. Finance, healthcare, retail, and ops teams get the intelligence they need without managing ten separate tools.

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2.4B+
records/day
99.97%
uptime SLA
<200ms
inference p99
6 clouds
AWS·Azure·GCP+
4 domains
multi-vertical
nexusiq — analytics workspace · finance domain
model accuracy
94.7%
▲ +1.2% vs last week
predictions today
1.24M
▲ +18% vs yesterday
drift score
0.03
● stable · no retrain
spark jobs
12 / 12
▲ all green
prediction volume — 7 days
active domains
finance
38%
healthcare
27%
retail
21%
ops
14%
Continuous ML + big data

Models that train themselves. Pipelines that never sleep.

End-to-end ML from raw data to deployed model — fully automated. Spark on EMR processes petabytes. Airflow orchestrates. MLflow tracks everything. SageMaker deploys the winner.

  • Apache Spark 3.5 + Hadoop HDFS on AWS EMR — petabyte-scale PySpark ETL
  • Airflow DAGs orchestrate ingestion → features → training → eval → deploy
  • Evidently AI drift detection triggers automatic SageMaker retraining
  • ml-intern (HuggingFace) automates paper reading, model search, experiments
→ build this pipeline now
pyspark_etl.py · emr cluster
$ spark-submit --master yarn etl_job.py
INFO SparkContext: Spark 3.5.0 · YARN mode
INFO Reading HDFS: s3a://nexusiq-lake/raw/
INFO 24 executors · 96 cores · 384GB RAM
INFO Stage[1]: Parsing 2.4B rows, 180 partitions
INFO Stage[2]: PySpark feature transforms (14 cols)
INFO Stage[3]: Writing Delta Lake → S3
SUCCESS 2.41B rows written · 4m 32s
SUCCESS Features pushed → Feast online store
What the market is missing

Three capabilities no platform does well.
NexusIQ ships all three.

We studied 40+ ML platforms. These three gaps show up everywhere. We built them in from day one.

Explainable AI

Why did the model decide that?

Every prediction comes with a SHAP explanation, audit trail, and human-readable reason. Required for finance, healthcare, and legal compliance. Most platforms treat it as an afterthought.

prediction: DENY loan · conf 91.3%
top driver: debt_ratio +0.41 SHAP
audit: model_v3 · logged + exportable
Live A/B model testing

Split traffic. Pick the winner. Automatically.

Route live traffic between Champion and Challenger models in real time. NexusIQ measures statistical significance and auto-promotes the winner — no engineer needed for rollout decisions.

champion model_v3 · 70% traffic · AUC 0.931
challenger model_v4 · 30% traffic · AUC 0.947
significance: p=0.003 · promoting v4...
NL → Spark / SQL

Write queries in plain English.

Type a question. NexusIQ generates the PySpark or SQL, runs it on your data lake, and returns results. No data engineer needed for ad-hoc analysis.

you: "top 10 stores by revenue growth last 90 days"
nexusiq: SELECT store_id, ... → running on Spark
result: 10 rows · 0.8s · 2.4B rows scanned
// full stack — every tool a senior ai/ml engineer uses
apache spark 3.5hadoop hdfsaws sagemakerazure openaikubernetes eksapache kafkamlflowapache airflowlangchainpineconepower bi embeddedgrafanaterraformgithub actionsfeastevidently aigcp bigqueryvertex aidockerdelta lakeaws emrredispostgresqllangsmithhuggingfaceml-internredshiftshap
get started

Build the platform.
Ship the models.

Chat with NexusIQ's AI configurator — describe what you need, get a full repo scaffold in minutes.

Open build configurator →