PROBLEM × SOLUTION
Five pains. Five capabilities.
Side by side.
Every company with fragmented data lives the same friction between what sits in the database and what becomes a decision in the meeting. BlueDecision answers each one with a direct capability, same order, same weight.
WHAT HURTS TODAY
WHAT BLUEDECISION DOES
Dashboards no one uses
Nice-looking panel, correct data, nobody looks. The question the manager has today isn't in that filter, so they give up before asking for a change.
Inconsistent metrics across teams
Marketing's CAC doesn't match Finance's. Recognized revenue doesn't tie out to the CRM. Every meeting starts by arguing about the number, not the decision.
Slow decisions because the data isn't trusted
The data is there, current and complete. But the doubt from last time still hangs around, so the decision-maker asks for "one more cross-check" before moving.
Total dependency on IT
Every new analysis becomes a ticket, a queue, a sprint. By the time the answer ships, the question has changed. The analyst became IT's customer.
Idle data, missed opportunity
The model predicted it, the anomaly appeared, nobody saw it. Without an actionable alert, data turns into a dead archive.
Natural language queries
Anyone in the operation asks the data directly, in text. NLQ returns the answer with a table, a chart, and the source.
Standardized KPIs (semantic layer)
Revenue, churn, CAC, margin: one definition, calculated once, exposed to every team. The argument over whose number is right drops off the agenda.
Single source of truth with lineage
Every metric carries its source, the transformation applied, and its last update. Trust becomes a property of the data, not of the source.
Self-service analytics
Drill-down, comparison, simulation: the manager builds the analysis without a ticket. IT designs the platform; the people who decide operate it.
Actionable recommendation with AI
When an anomaly shows up, the system flags it with context. When a KPI turns, the next action reaches the decision-maker with the reasoning behind it.
USE CASES
Where BlueDecision lands first.
Four recurring fronts where the payoff shows up in weeks, not quarters.
01
Executive
360° view per area, real-time OKRs, alerts on critical metrics.
02
Sales & marketing
CAC, LTV, funnel, multi-touch attribution, account propensity.
03
Finance
P&L by dimension, margin per SKU, cash flow projection, faster close.
04
Operations
Team productivity, stage-level bottleneck, capacity × demand, quality.
TYPICAL IMPACT
The numbers the pack
tends to deliver.
Ranges observed in BlueDecision projects running in production. They vary by area and data maturity.
ARCHITECTURE
Four technical capabilities.
One platform.
Lakehouse plus semantic layer plus generative AI on top of the same day-to-day KPIs. Embedded analytics when it makes sense, a native dashboard when it doesn't.
Consolidation
Source ingestion (ERP, CRM, marketing, ops) into a lakehouse with an incremental layer and governance.
- ready-made connectors
- incremental pipeline
- automated quality
live preview
Consolidation
Standardization
One semantic layer (dbt + metrics) with end-to-end lineage and versioning.
- semantic layer
- traceable lineage
- per-metric versioning
live preview
Standardization
Self-service
Sisense or QuickSight dashboards plus generative NLQ. Drill-down, comparison, direct simulation.
- embedded analytics
- multimodal NLQ
- drill-down on any dimension
live preview
Self-service
Intelligence
Forecasting, anomaly detection, and actionable recommendations on the same day-to-day KPIs.
- hierarchical forecast
- anomaly with root cause
- contextual recommendation
live preview
Intelligence
HOW IT WORKS IN RUNTIME
From question to decision, with no ticket.
Data arrives
ERP, CRM, marketing, ops, sensors. The lakehouse consolidates and standardizes it.
Question arrives
in plain text or through a dashboard. NLQ translates it, runs it, and returns the answer.
AI recommends
forecast, anomaly, contextual next action. Data turns into action, not an archive.
Decision logged
structured output to the operational system, an audit log, and feedback so the model keeps learning.
ENGAGEMENT
Three phases.
Gradual entry, clear cycle.
This isn't a pricing table. It's the project journey. It starts with the priority dashboard and expands into self-service and AI.
Pilot
5-7 weeks
2-3 consolidated sources, priority dashboards, a basic semantic layer, baseline metrics.
Growth
10-14 weeks
Self-service in production, NLQ live, forecasting and anomaly detection on the main KPIs.
Operation
Recurring
Ongoing governance, new KPIs, model evolution, expansion into new areas.
FOR WHOM
TECH STACK
Built on recognized foundations.
Sisense (Gold Partner)
Embedded analytics and product dashboards, with governance and role-based personalization.
Amazon QuickSight + Databricks
A lakehouse for operational sources, with an incremental pipeline and semantic layer.
AWS Bedrock
Frontier models for NLQ, forecasting, anomaly detection, and actionable recommendations on your KPIs.
COVERAGE
Areas covered by the pack.
Pre-modeled KPIs and sources that cover the most common flows across your business areas. Additional metrics come in through the configurable semantic layer.
Executive
- North star
- OKRs
- KPI alerts
- Area drill-down
- Period comparison
- Executive summary
Commercial
- CAC
- LTV
- Funnel
- Multi-touch attribution
- Account propensity
- Pipeline forecast
Finance
- P&L by dimension
- Margin per SKU
- Cash flow projection
- Spend by category
- Faster close
- Concentration
Operations
- Team productivity
- Stage-level bottleneck
- Capacity × demand
- OEE
- Quality
- Anomalies
CLIENTS USING IT TODAY
Real results, in production.
LOBBYCRE
Generative-AI revenue forecasting cut the error below 5% at a US real-estate asset manager. Natural language over a time series: the manager asks, the model answers.
FREQUENTLY ASKED
FAQ
It can replace or extend. If you already run Sisense, QuickSight, or similar, BlueDecision adds the semantic layer, NLQ, and generative AI on top. If you don't have structured BI, the pack lands as the primary layer. That choice is part of the diagnostic.
The pilot ships 2-3 priority dashboards and the basic semantic layer in 5-7 weeks. NLQ and self-service come in during the Growth phase, once the pilot has validated sources and KPIs.
The model translates the natural-language question into SQL, runs it in the lakehouse, and returns a table, a chart, and the source. The system only answers on modeled metrics. It doesn't invent columns or reach data outside your permissions.
Yes. BlueDecision's semantic layer can take a metric as pre-calculated data from an authoritative source, such as recognized revenue from the ERP, and expose it to other teams while keeping the single definition.
Your call. The client can run it internally with the runbooks we hand over, or contract AI as a Service and have BlueMetrics handle monitoring, retraining, and evolution.
BlueDecision is horizontal: it organizes data, standardizes KPIs, and enables self-service plus AI across the whole set. BlueOps is vertical in physical operations (forecasting, optimization, maintenance). BlueRisk is vertical in credit and fraud. BlueDecision delivers visibility; BlueOps and BlueRisk deliver automated decisions in a specific domain. They often run side by side in the same operation.
BEYOND THE PACK
Decision model needs a custom
vertical platform? Calls for a custom project.
When the problem outgrows the Solution Pack frame, Custom Projects take over: open scope, a senior multidisciplinary team, the same engineering standard.