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Nexus Focal
BI • AI • Data Engineering
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From data to decisions

Ship analytics faster. Unlock AI safely. Engineer data that scales.


Nexus Focal partners with teams to design reliable data platforms, actionable BI, and pragmatic AI solutions that drive measurable business outcomes.


BI
Self‑serve insights & KPIs
AI
Copilots, NLP, forecasting
Data
Warehouses, pipelines, quality

Services

End‑to‑end support across analytics strategy, data foundations, and applied AI—delivered with governance, security, and change management in mind.

BI & Analytics

Build a trustworthy analytics layer and empower your teams.

  • Executive dashboards & KPI frameworks (Power BI / Tableau)
  • Semantic models, row‑level security, governance
  • Self‑service enablement & data literacy
  • Embedded analytics and decision workflows

AI Solutions

Practical AI to augment processes where it truly adds value.

  • Forecasting & optimization for demand and inventory
  • Copilots & chat for documents, CRM, and support
  • NLP for classification, summarization, and search
  • Responsible AI reviews, evaluation, and guardrails

Data Engineering

Reliable data platforms that scale with your business.

  • Data lakes & warehouses (Snowflake, BigQuery, Synapse)
  • ETL/ELT pipelines (dbt, Airflow, Fabric, ADF)
  • Real‑time streaming (Kafka, Event Hubs)
  • Data quality, lineage & master data management

Our Approach

Transparent milestones, short iterations, and measurable outcomes—so value lands early and often.

01

Discover

Stakeholder interviews, use‑case mapping, data assessment, and a prioritized roadmap.

  • Workshops to align outcomes, constraints, and success metrics
  • Audit of data sources, access, and BI/ML maturity
  • Quick wins identified for a 30–60 day horizon
02

Design

Reference architecture, security/governance, and a pragmatic delivery plan.

  • Target state diagrams and runbooks for operations
  • Data contracts, lineage, and quality checks defined
  • PoC/MVP scope with acceptance criteria and KPIs
03

Build

Ship pipelines, models, and BI with CI/CD and documentation.

  • Infrastructure‑as‑code, environments, and deployment pipelines
  • Incremental ELT, semantic models, and governed access
  • AI features prototyped with evaluation and guardrails
04

Value

Enablement and improvement cycles with clear ROI tracking.

  • Change management: training, adoption, and playbooks
  • Operational dashboards and SLAs for reliability
  • Backlog grooming for the next wins

Industries

Most‑requested BI and AI features by domain.

Retail & eCommerce

BI

  • Conversion funnels, AOV, cohort retention
  • Merchandising & promo performance by segment
  • Channel ROI and attribution views

AI

  • Demand forecasting & price optimization
  • Recommendations and personalized offers
  • Churn propensity; NLP on reviews & support

Manufacturing

BI

  • OEE, throughput, downtime, scrap and rework
  • Line balance and capacity utilization
  • Supplier quality and yield tracking

AI

  • Predictive maintenance & anomaly detection
  • Quality defect prediction (vision & sensors)
  • Scheduling & inventory optimization

Supply Chain & Logistics

BI

  • OTIF, fill rate, lead times by lane
  • Cost‑to‑serve and carrier performance
  • Inventory turns, DC heatmaps, SLA alerts

AI

  • ETA prediction and route optimization
  • Dynamic safety stock & replenishment
  • Anomaly detection on sensor/telematics

Financial Services

BI

  • Portfolio risk, NIM, charge‑offs
  • Delinquency buckets and collections KPIs
  • CLV, cross‑sell and segment profitability

AI

  • Fraud detection and alert triage
  • Credit scoring & income verification
  • Document extraction (KYC/AML) and RAG search

B2B Services

BI

  • Pipeline velocity, win rates, ARR/MRR
  • Utilization, margin by project, backlog
  • CSAT/NPS and SLA compliance

AI

  • Lead scoring and next‑best action
  • Call/email summarization and CRM updates
  • Support copilots and knowledge search

Public Sector

BI

  • Budget vs actuals, service levels, outcomes
  • Program intake, backlog, throughput
  • Geospatial insights and community impact

AI

  • Case triage & routing; wait‑time prediction
  • Text classification, PII redaction, summarization
  • Resource allocation & scheduling optimization

Technology

We are tool‑agnostic. Here are the most‑used services and tools we deploy across common platforms.

Azure Azure

Cloud platform for data, analytics, and AI.

  • Storage & compute: ADLS Gen2, Azure SQL, AKS, Functions
  • Data & analytics: Synapse, Fabric, Databricks, Event Hubs
  • Integration & governance: Data Factory, Purview, Key Vault
  • AI: Azure ML, Cognitive Search, Azure OpenAI

AWS AWS

Scalable compute, storage, streaming, and ML services.

  • Storage & compute: S3, Lambda, ECS/EKS, RDS
  • Data & analytics: Redshift, Athena, Glue, EMR
  • Streaming & integration: Kinesis, MSK (Managed Kafka), Step Functions
  • AI: SageMaker, Bedrock, Comprehend

Google Cloud GCP

Google’s data and AI platform with BigQuery.

  • Storage & compute: Cloud Storage, GCE/GKE, Cloud SQL
  • Data & analytics: BigQuery, Dataproc, Dataform
  • Pipelines & events: Dataflow, Pub/Sub, Composer (Airflow)
  • AI: Vertex AI, Vision/NLP APIs

Microsoft Fabric Microsoft Fabric

Unified lakehouse, pipelines, warehousing, and BI.

  • OneLake & Lakehouse; Delta tables, shortcuts
  • Data Engineering (Spark), Data Factory pipelines
  • Synapse Data Warehouse & SQL Endpoints
  • Power BI semantic models, Direct Lake

Databricks Databricks

Lakehouse for data engineering, ML, and governance.

  • Delta Lake, Auto Loader, Workflows
  • Unity Catalog, lineage, fine‑grained ACLs
  • MLflow, Feature Store, Model Serving
  • Photon, Optimized Autoscaling, Serverless SQL

Snowflake Snowflake

Cloud data platform for warehousing and apps.

  • Virtual Warehouses, Tasks, Streams, Snowpipe
  • Dynamic Tables, Time Travel, Zero‑Copy Clones
  • Snowpark (Python/Java), UDFs, External Functions
  • Marketplace, Native Apps, Iceberg Tables

SQL Server SQL Server

Relational database with SQL, SSIS/SSAS/SSRS.

  • Engine + In‑Memory OLTP, Columnstore
  • Integration Services (SSIS), Agent jobs
  • Analysis Services (tabular), Row‑level Security
  • Reporting Services, PolyBase / External Tables

PostgreSQL PostgreSQL

Open‑source database with rich extensions.

  • Extensions: PostGIS, pgvector, pg_partman
  • Logical replication, FDW (foreign data wrappers)
  • Partitioning, BRIN/GIN indexes, materialized views
  • Managed: Azure Flexible Server, Cloud SQL, RDS

BigQuery BigQuery

Serverless, massively parallel data warehouse.

  • BI Engine, Reservations, Table Partitions/Clusters
  • BigQuery ML, Remote Functions, UDFs
  • Data Transfer Service, Federation (GCS/Sheets)
  • Authorized Views, Row‑level & Column‑level security

dbt dbt

Analytics engineering for SQL‑based ELT.

  • Models, seeds, snapshots; Jinja + macros
  • Tests (schema/data), exposures, docs site
  • Packages, semantic layer, metrics
  • dbt Cloud jobs & CI, or Core with orchestrators

Airflow Airflow

Workflow orchestration for data pipelines.

  • DAGs with Operators/Sensors; TaskFlow API
  • Providers for AWS/Azure/GCP/Snowflake/Databricks
  • MWAA / Composer managed deployments
  • Deferrable operators, SLA, retries & alerting

Kafka Kafka

Event streaming, connectors, and real‑time processing.

  • Producers/Consumers, Partitions, Retention
  • Kafka Connect, Schema Registry (Avro/JSON/Protobuf)
  • ksqlDB & Streams API for real‑time transforms
  • Managed: MSK, Confluent Cloud, Azure Event Hubs

Power BI Power BI

BI platform for models, reports, and governance.

  • Dataflows Gen2, Semantic Models, Direct Lake/DirectQuery
  • RLS/OLS, Composite models, Calculation groups
  • Premium/Fabric capacities, Deployment pipelines
  • Embedded analytics, Custom visuals, M query

Tableau Tableau

Visual analytics, dashboards, and data exploration.

  • Extracts, Hyper, Prep flows, Data Server
  • LOD expressions, Parameters, Extensions
  • Tableau Server/Cloud governance & permissions
  • Accelerators, Performance recording & tuning

Looker Looker

Semantic modeling and governed BI delivery.

  • LookML models, Views, Explores, PDTs
  • Git‑based versioning, Content validation
  • Looker Blocks, System Activity, Extensions
  • Looker Studio federation and embedding

About Nexus Focal

We started Nexus Focal to help organizations gain leverage from their data with a clear, outcome‑first mindset. Our team blends analytics strategy, engineering rigor, and applied AI expertise.

Principles

  • Value first, tools second
  • Secure by design
  • Open standards where possible
  • Knowledge transfer & enablement

Engagements

  • Discovery & roadmap
  • Pilot / MVP
  • Scale‑up & enablement

Outcomes

  • Reliable metrics, faster decisions
  • Automated workflows with AI assist
  • Lower time‑to‑insight & cost‑to‑serve