Services
Data engineering, analytics and AI
Reliable data comes first. Analytics and AI depend on pipelines that are tested, documented and owned. We build in that order.
What organizations bring us
Most stalled AI and analytics programs are data programs in disguise. Sources disagree, definitions differ between teams, pipelines fail silently and nobody trusts the dashboard. Adding a model to that makes the problem harder to see, not easier to solve.
We organize the work into five offerings you can buy separately: data engineering, data modernization, business intelligence, AI and machine learning, and generative AI. Each has its own scope, deliverables and quality gates.
Five offerings
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Offering 1
Data engineering
Ingestion, ELT, pipelines, change data capture, lakes, warehouses and lakehouses, orchestration, quality, governance and master data.
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Offering 2
Data modernization
Move from legacy warehouses to cloud platforms with schema mapping, reconciliation and cutover validation.
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Offering 3
Business intelligence
Executive dashboards, operational reporting and self-service analytics on a governed semantic layer.
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Offering 4
AI and machine learning
Predictive models, NLP, computer vision, MLOps, evaluation and responsible AI.
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Offering 5
Generative AI
Copilots, retrieval-augmented generation, document processing and agent workflows, with security and evaluation.
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Tool
AI opportunity assessment
Answer a short set of questions about your processes and receive an illustrative roadmap. No ROI figures.
What each offering covers
Data engineering
Pipelines that run on schedule, fail loudly and can be traced.
ING Ingestion and CDC Batch and streaming ingestion from databases, SaaS applications, files and APIs. Change data capture where full reloads…
- What it covers
- Batch and streaming ingestion from databases, SaaS applications, files and APIs. Change data capture where full reloads are impractical.
- What we do on projects
- Connector selection, incremental load design, schema change handling, late-arriving data.
- Where projects go wrong
- Source systems that change schemas without notice. Soft deletes that never reach the warehouse.
- Roles we staff
- Data engineer, integration developer
PIPE ETL, ELT and orchestration Transformation in SQL and Spark, orchestration with schedulers, dependency management and retries.
- What it covers
- Transformation in SQL and Spark, orchestration with schedulers, dependency management and retries.
- What we do on projects
- dbt models and tests, Airflow DAGs, Spark jobs, idempotent loads, backfills.
- Where projects go wrong
- Jobs that are not safe to rerun. Transformations hidden in BI tools.
- Roles we staff
- Analytics engineer, data engineer
STORE Lake, warehouse and lakehouse Storage and compute design for analytics and machine learning.
- What it covers
- Storage and compute design for analytics and machine learning.
- What we do on projects
- Layer design (raw, cleaned, curated), table formats, partitioning, cost controls, access policies.
- Where projects go wrong
- A lake with no catalog turns into a swamp. Duplicate copies of the same data.
- Roles we staff
- Data architect, platform engineer
GOV Quality, governance, MDM and lineage Data quality checks, ownership, catalogs, master data and lineage.
- What it covers
- Data quality checks, ownership, catalogs, master data and lineage.
- What we do on projects
- Quality rules and thresholds, stewardship roles, golden record logic, column-level lineage.
- Where projects go wrong
- Governance without owners. Rules that nobody acts on.
- Roles we staff
- Data governance lead, data steward, MDM specialist
Data modernization
Replace legacy warehouses and move analytics without losing trust in the numbers.
ASSESS Legacy warehouse assessment Inventory of sources, jobs, tables, reports and users. Usage analysis to find what can be retired.
- What it covers
- Inventory of sources, jobs, tables, reports and users. Usage analysis to find what can be retired.
- What we do on projects
- Job and query analysis, dependency graph, report rationalization, complexity scoring.
- Where projects go wrong
- Business logic buried in stored procedures and report formulas.
- Roles we staff
- Data architect, BI analyst
MAP Schema mapping and transformation Source-to-target mapping for tables, keys, types and business rules.
- What it covers
- Source-to-target mapping for tables, keys, types and business rules.
- What we do on projects
- Mapping documents, code translation, data type decisions, surrogate keys.
- Where projects go wrong
- Subtle type and rounding differences between platforms.
- Roles we staff
- Data engineer, data analyst
RECON Reconciliation and quality thresholds Controls that show the new platform produces the same answers.
- What it covers
- Controls that show the new platform produces the same answers.
- What we do on projects
- Row counts, aggregate checks, balance reconciliation, sample comparisons, agreed tolerances.
- Where projects go wrong
- Agreeing on a tolerance after seeing the results.
- Roles we staff
- Data engineer, QA analyst
CUT Lineage and cutover validation A cutover plan that covers pipelines, reports, access and user adoption.
- What it covers
- A cutover plan that covers pipelines, reports, access and user adoption.
- What we do on projects
- Parallel run, report comparison, user acceptance, rollback criteria, decommissioning.
- Where projects go wrong
- Switching off the old platform before the last quarterly report has run.
- Roles we staff
- Project lead, data engineer
Business intelligence
Dashboards people use because the numbers are trusted.
EXEC Executive and operational reporting Management dashboards and operational reports tied to defined metrics.
- What it covers
- Management dashboards and operational reports tied to defined metrics.
- What we do on projects
- Metric definitions, semantic models, dashboard design, refresh schedules, row-level security.
- Where projects go wrong
- Two dashboards that show different revenue for the same month.
- Roles we staff
- BI developer, BI architect
SELF Self-service analytics Governed data sets that analysts can use without writing extraction jobs.
- What it covers
- Governed data sets that analysts can use without writing extraction jobs.
- What we do on projects
- Certified data sets, naming standards, training, usage monitoring.
- Where projects go wrong
- Unmanaged copies of data in personal workbooks.
- Roles we staff
- BI developer, analytics engineer
TOOLS Tools Power BI, Tableau, Looker, Oracle Analytics and SAP Analytics Cloud.
- What it covers
- Power BI, Tableau, Looker, Oracle Analytics and SAP Analytics Cloud.
- What we do on projects
- Migration between tools, performance tuning, embedding, governance.
- Where projects go wrong
- Tool choice driven by licensing alone.
- Roles we staff
- BI developer
AI and machine learning
Models that are evaluated before they are deployed and monitored after.
STRAT Enterprise AI strategy Use case selection, data readiness, build or buy decisions, governance.
- What it covers
- Use case selection, data readiness, build or buy decisions, governance.
- What we do on projects
- Use case scoring, data audit, operating model, policy.
- Where projects go wrong
- Starting with the technology rather than the decision it should improve.
- Roles we staff
- AI strategist, product manager
ML Model development Predictive analytics, NLP and computer vision.
- What it covers
- Predictive analytics, NLP and computer vision.
- What we do on projects
- Feature engineering, training, validation against a baseline, explainability where needed.
- Where projects go wrong
- A model that beats the baseline in a notebook and fails on production data.
- Roles we staff
- Data scientist, ML engineer
OPS MLOps and infrastructure Training pipelines, model registry, deployment, monitoring and drift detection.
- What it covers
- Training pipelines, model registry, deployment, monitoring and drift detection.
- What we do on projects
- Feature stores, CI/CD for models, GPU or managed endpoints, rollback.
- Where projects go wrong
- Retraining without evaluation gates.
- Roles we staff
- MLOps engineer, platform engineer
RAI Evaluation and responsible AI Measuring accuracy, bias and failure modes. Policy and human oversight.
- What it covers
- Measuring accuracy, bias and failure modes. Policy and human oversight.
- What we do on projects
- Evaluation data sets, fairness checks, documentation, review gates.
- Where projects go wrong
- Evaluating on the data the model was trained on.
- Roles we staff
- Data scientist, AI governance lead
How we work
- 1
Pick a decision
Start from a business decision or process, not a dataset.
- 2
Check the data
Profile sources, owners, quality and access.
- 3
Build the pipeline
Tested, documented and monitored.
- 4
Deliver the first use
A dashboard or model in production, then widen.
- 5
Operate
Monitoring, ownership and a backlog.
Data and AI professionals we place
Staffing is a separate engagement from project delivery. You can use either, or both.
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Data engineer
Builds and maintains pipelines.
- SQL
- Python or Scala
- Spark
- Airflow
- a cloud warehouse
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Analytics engineer
Models data for analysis.
- dbt
- SQL
- testing
- documentation
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Data architect
Designs platforms and models.
- Lakehouse
- modeling
- governance
- cost
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Data scientist or ML engineer
Builds and deploys models.
- Python
- ML frameworks
- evaluation
- MLOps
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BI developer
Builds reports and semantic models.
- Power BI
- Tableau or Looker
- data modeling
Related technology
Listed as technologies we work with. This is not a statement of vendor partnership, certification or reseller status.
Questions people ask
Do you guarantee ROI from AI?
No. The assessment tool produces an illustrative roadmap, not a forecast. Returns depend on data, adoption and process change we do not control.
Where do we start if our data is a mess?
With one decision or report you care about and the three sources behind it. Fix those, then widen the scope.
Can you work inside our platform?
Yes. We work with your existing warehouse or lakehouse where it fits. A platform move needs its own business case.
Related
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Generative AI
Enterprise copilots, retrieval-augmented generation, document processing and agent workflows, designed with permissions, evaluation and security from the start.
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AI security
Security assessments for LLM applications, RAG pipelines, AI agents and MCP servers, plus AI red teaming and architecture.
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Data migration
Plan, extract, cleanse, map, load and reconcile data between systems, with business owners signing off every object.
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