BI • AI • Data Engineering
Turn your data into your biggest competitive advantage.
We help teams build reliable data platforms, actionable BI, and AI that actually works in production.
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
Approach
Transparent milestones, short iterations, and measurable outcomes—so value lands early and often.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 was founded to help organizations get real leverage from their data — not shelf-ware that gathers dust. We're a boutique consultancy operating across the Americas with deep expertise in analytics strategy, data engineering, and applied AI. Our outcome-first mindset means we measure success by what your team does differently after we leave, not by the size of the deliverable we hand over.
How We Think
- Business outcomes before technology choices
- Security and governance built in, not bolted on
- Open standards and portability by default
- We leave your team more capable than we found it
How We Work
- Fixed-scope discovery sprints — no open-ended retainers
- Iterative builds with weekly demos and checkpoints
- Embedded enablement so your team owns what we build
What You Get
- Dashboards your team actually uses every day
- Pipelines that run reliably without babysitting
- AI features with clear, measurable ROI
Data & AI practitioner passionate about helping organizations cut through the noise and build data capabilities that create real, measurable impact. Combines analytics strategy, cloud engineering, and applied AI to deliver solutions that stick.
LinkedInFAQ
Common questions from teams evaluating a data and AI partner.
Discovery sprints run 2–4 weeks. Implementation phases typically span 6–16 weeks depending on scope. We work in fixed increments so you always know what you're getting and when — no open-ended retainers.
Primarily remote, which keeps costs low and allows us to work with teams across the Americas and beyond. For key workshops or critical go-live moments, we're happy to be on-site when it genuinely adds value.
Mid-market companies that have outgrown spreadsheets but don't yet have a full data team in-house — typically 50–2,000 employees. We're also a good fit for larger enterprises that need a focused, senior resource for a specific initiative.
We price by fixed-scope project phases. Each phase has a defined deliverable, timeline, and cost — no surprises. Ongoing advisory or support arrangements are available after an initial engagement.
Yes. We are deliberately tool-agnostic. Whether you're on Azure, AWS, or GCP — Power BI, Tableau, or Looker — Snowflake, Databricks, or BigQuery — we work within your existing environment and only recommend changes when there's a clear, justified reason.
Yes. Many clients retain us for a lightweight advisory arrangement after delivery — to review new work, unblock the team, and help plan the next initiative. This is always optional and scoped per-month.
Ready to get real value from your data?
Tell us where you are and what you're trying to achieve — we'll take it from there.