Case Study — AtlasIQ
Building a Real-Time Financial Intelligence Layer for a 14-Subsidiary Enterprise
Unified multi-entity financial analytics replacing 14 separate Excel workflows
Industry
Enterprise Finance
Timeline
12 weeks
Team
5 engineers
Tech
PostgreSQL + dbt + React
The Challenge
A multi-entity enterprise with 14 subsidiaries had finance teams running separate Excel-based reporting stacks. Monthly consolidation took 3 days, involved 22 manual steps, and produced results immediately questioned by stakeholders due to inconsistent accounting treatments across entities.
Our Approach
How We Solved It
Unified Financial Data Model
Designed a canonical chart of accounts that normalized 14 different entity schemas into a single GAAP-compliant data model without forcing entities to change their local systems.
Automated Reconciliation Engine
Built dbt-powered transformation pipelines that auto-reconcile intercompany eliminations, currency translations, and allocation logic on every data refresh.
Variance Analysis Automation
Replaced manual variance commentary with AI-generated narrative that identifies the top 5 drivers of month-over-month changes across P&L, balance sheet, and cash flow.
Real-Time Drill-Down Dashboards
Delivered a React dashboard with full drill-down from consolidated group P&L to individual transaction level in under 200ms, live-updating as entities post data.
Engineering Process
How We Built It
Incremental dbt Models
Used incremental dbt materialization strategies to process only changed records on each run, reducing full-refresh time from 4 hours to under 8 minutes.
Entity-Level Permission Model
RBAC configuration ensures subsidiary controllers see only their entity's underlying data while group finance has full consolidated visibility.
Audit Trail Architecture
Every transformation is lineage-tracked through dbt's DAG, giving auditors a complete, reproducible path from source system to reported figure.
Architecture Decisions
Key Technical Choices
dbt Over Stored Procedures
Chose dbt transformations over database stored procedures for version control, testing, and documentation — critical for audit readiness at the entity level.
Semantic Layer for Metric Consistency
Implemented a dbt semantic layer so 'revenue' means exactly the same thing across every dashboard, report, and ad-hoc query.
Snapshot Tables for Point-in-Time Reporting
dbt snapshot tables capture daily balance states, enabling accurate historical comparisons and regulatory reporting without relying on system timestamps.
Results
What We Delivered
Solution Blueprint
How It All Fits Together
- 14 ERP connectors
- CDC replication
- Schema normalization
- dbt semantic models
- Intercompany elimination
- Currency translation engine
- Executive P&L dashboard
- Drill-down explorer
- AI variance narrative
Lessons Learned
What We Improved
Agree on the Canonical Model First
Two weeks of finance stakeholder alignment on the unified chart of accounts saved us from a rewrite at week 8. Semantic disagreements are harder to fix than code.
Incremental Builds From Day One
Designing for incremental processing from the start meant we never had to refactor the pipeline when data volumes grew 4x during the project.
Show Value in Week 4
We delivered the first consolidated P&L view at week 4 with just 3 entities. That early win maintained executive sponsorship through the harder integration work.
More From AtlasIQ
Related Case Studies
Enterprise SaaS / Finance
Revenue Intelligence Platform
Predictive revenue analytics for a $200M ARR SaaS business
Enterprise / SaaS
Executive Reporting Platform
From static PDF decks to real-time C-suite intelligence in 10 weeks
E-Commerce / Consumer
Customer Insights Engine
Real-time behavioral analytics and personalization for high-volume e-commerce
Related Research
Research Reports for This Industry
Enterprise AI Adoption Trends 2026
Enterprise AI has crossed the operational threshold. Seventy-two percent of Fortune 500 organizations now run at least one AI system in production — and the average enterprise manages 3.4 concurrent AI initiatives. This report maps the state of enterprise AI across healthcare, manufacturing, financial services, retail, and beyond.
Read reportFintech AI Adoption Report 2026
Financial services organizations are navigating a pivotal transition in AI adoption — moving from exploratory pilots toward enterprise-scale deployments that are becoming load-bearing infrastructure within core business processes. The 2026 landscape is defined not by whether to adopt AI, but by how to deploy it responsibly, at what pace, and within which governance architecture. Incumbent banks, c...
Read reportBanking Automation Trends 2026
Banking automation has moved well past the proof-of-concept phase. The institutions that have captured the most value are not those that deployed the most bots or launched the most AI pilots — they are the ones that built automation as a strategic capability, with deliberate governance, disciplined sequencing, and organizational structures that treat process intelligence as a core competency. In 2...
Read reportFraud Detection Market Analysis 2026
Fraud detection has entered a structural transformation driven by the convergence of real-time payment rails, AI-native decisioning architectures, and increasingly sophisticated adversarial fraud operations. For financial institutions, payment processors, and fintech platforms, the ability to detect and prevent financial crime in real time is no longer a compliance checkbox — it is a core operatio...
Read reportExplore Further
Work With Halkwinds
Build Something Exceptional
Partner with the team that built AtlasIQ.