Data Engineering & Architecture
- Source inventory, ingestion design and ETL/ELT pipelines
- SQL transformations, data warehouse or lakehouse models
- Data quality checks, lineage and access roles
ALL SYSTEMS PLUS / SERVICES
AI Integration, Data Engineering & Business Intelligence
Build the governed data pipelines and KPI definitions that support AI-assisted analytics, forecasting, anomaly detection, natural-language exploration and operational workflows.
WHAT WE DELIVER
Tools, deliverables and implementation scope
Power BI · Tableau · SQL · Snowflake · Python · AI APIs
Business Intelligence
- Power BI or Tableau dashboards and semantic models
- Executive scorecards, operational reporting and drill-down views
- KPI dictionary, validation against source systems and user training
AI Integration
- Use-case prioritization, data readiness and risk review
- Forecasting, anomaly detection or document/workflow assistance pilots
- Evaluation dataset, output review process and human approval points
Engagement & Handoff
- Data architecture diagram and source-to-target mappings
- Documented deployment, refresh schedule and ownership
- Backlog, test results and runbook
ILLUSTRATIVE DELIVERY PLAN
Example milestones, not a fixed commitment
The actual schedule depends on system access, requirements, integrations, approvals and available client resources.
Discovery & KPI definition
Confirm source access, KPI owners, report users and acceptance criteria.
Build & validate
Create data models, reports and tests; hold weekly demonstrations.
Launch & handoff
Train users, reconcile outputs and transfer documentation.
TEAM & RESULTS
Staffing examples and how to measure progress
Example team
1 data engineer + 1 BI developer + part-time delivery lead; add an AI specialist for AI pilots.
Typical timeline
4–8 weeks for a constrained dashboard pilot; AI pilots scoped separately after data review.
Success measures
KPI reconciliation rate, manual reporting hours, refresh reliability, dashboard adoption and AI evaluation accuracy.
Before work starts we agree a baseline and how it will be measured, so results can be checked.
AI Integration & Measurable Outcomes
AI use cases built on governed business data
Solutions we scope and evaluate with your team, shaped by your data access and security requirements.
Natural-language BI
Explore KPI definitions and approved datasets through natural-language questions, with traceable sources and user access controls.
Forecasting & anomaly detection
Pilot demand, volume or performance forecasting and exception alerts; compare model output against historical baselines.
Knowledge retrieval & AI assistants
Connect approved documents and structured data to retrieval-augmented experiences, with citations, permissions and review paths.
AI quality gates
Define evaluation datasets, response-quality criteria, latency, cost, access rules and human escalation before rollout.
Sample timelines and team shapes. Your proposal confirms scope, team and schedule.