Deliverables
- Data source audit and cleanup plan
- Data model, transformations, and quality checks
- Dashboard or reporting interface
- Optional natural-language data assistant
- Documentation, training, and maintenance recommendations
ANALYTICS AND DATA AI
Data Intelligence
Turn scattered data into useful decision support: clean datasets, dashboards, automated reporting, and private AI analytics.
Built for
Executives, finance, sales, operations, and product teams working from spreadsheets, inconsistent KPIs, manual reports, or disconnected databases.
Outcomes we measure
- Less spreadsheet work
- 50-80%
- Source of truth
- 1
- Decision visibility
- Real-time
Target reduction when manual reporting is replaced by cleaned pipelines and dashboards.
Unify definitions so teams stop arguing over conflicting KPI logic.
Dashboards and alerts can expose changes faster than monthly manual reports.
Analytics command center
LiveRevenue
+24.6%50-80%
Active users
+12.1%1
Pipeline value
+8.4%Real-time
Anomalies
−2.3%7
Q1 → Q4 · weekly
auto-refreshWhat were our top 3 revenue drivers last quarter?
Answer
Repeat orders from enterprise accounts grew 38% QoQ
source: orders.csv · finance.xlsx
The new premium tier accounted for 22% of total revenue
source: stripe · hubspot
Average order value in Ulaanbaatar region lifted 14%
source: postgres · sheets
Connected data sources
PostgreSQLSupabaseSheetsFormsCRMERP
Live stream
Last sync · 12 min agoSnapshot
Price range
$3,000 - $100,000+
Timeline
6-20 weeks
Engagement
Data audit, dashboard MVP, or private data AI system
Best for
Teams with messy spreadsheets or duplicated data
Businesses needing dashboards and KPIs
Organizations exploring private analytics assistants
Service details
Deliverables, outcomes, and technology stack
Business outcomes
- Create trusted datasets from messy files, forms, databases, and business tools.
- Build dashboards for leadership, operations, finance, sales, or product teams.
- Add AI question-answering over business data when privacy and structure allow it.
- Make decision-making faster with clearer KPIs, alerts, and trend explanations.
Technology stack
- PostgreSQL
- Supabase
- Python
- Pandas
- Vector databases
- Next.js
- BI dashboards
Buying notes
- Data quality and access are the main drivers of timeline and risk.
- Private RAG and natural-language analytics require careful permission and source controls.
- A data audit is often the safest first step before committing to a large analytics build.
What we build
What is included
Executives, finance, sales, operations, and product teams working from spreadsheets, inconsistent KPIs, manual reports, or disconnected databases.
01
Data cleanup
Normalize, deduplicate, validate, and document messy spreadsheets, forms, exports, and database records.
02
Executive dashboards
Revenue, operations, finance, customer, and team performance dashboards built for scanning and decisions.
03
Private Data AI
Secure AI assistants that answer questions from approved company data without exposing sensitive information.
04
Automated reporting
Pipelines that replace recurring Excel, PowerPoint, and manual report assembly.
Try data system estimation calculator
Estimate your data-to-decision layer
Scope data sources, dashboard depth, private AI, governance, and reporting automation.
What data intelligence system do you need?
Connected data sources
4
115
Useful add-ons
Timeline
Oyu advantage
Why we work this way
Foundation before AI
We clean metrics, ownership, and source logic before adding natural-language analytics.
Clear access rules
Roles, source traceability, freshness indicators, and audit-friendly logic help teams trust the data.
Automation beyond dashboards
Insights can trigger alerts, approval workflows, Digital Employee tasks, or operating reports.
Business-readable UX
Dashboards are designed for daily use with clear hierarchy, drill-downs, and role-specific views.
Risk removal
Risks we reduce
Conflicting spreadsheets
Metric definitions and reusable data models create one trusted interpretation.
Sensitive data in public AI
Private AI patterns keep approved data sources and access rules under control.
Dashboards that do not answer decisions
Discovery starts from business questions, not chart decoration.
AI project without foundation
Data audit and validation reduce failure before advanced analytics are added.
Delivery system
How we deliver
Each module is planned around the business workflow, not as a one-off deliverable.
01
Audit
Map sources, owners, reporting pain points, KPI conflicts, and decision questions.
02
Model
Define trusted metrics, dashboard structure, data ownership, and access levels.
03
Clean
Normalize data, remove duplicate logic, connect systems, and prepare reusable models.
04
Build
Create dashboards, automated reports, alerts, and private analytics assistant surfaces.
05
Validate
Compare outputs against known reports, train users, and expand once trust is established.
Next step
Plan this module around your business
Oyu Intelligence does not just make dashboards. We build decision infrastructure that makes data trustworthy, searchable, secure, and useful every day.
Questions
Common questions
Useful answers about pricing, delivery timelines, and regional implementation expectations.
What is the starting price for Data Intelligence?
Data Intelligence starts from From $3,000. The full project range is typically $3,000 - $100,000+, depending on scope, integrations, timeline, and governance needs.
How long does Data Intelligence take?
The usual delivery timeline is 6-20 weeks, including discovery, architecture, implementation, testing, and launch support.
Does Oyu Intelligence support Mongolian businesses?
Yes. Oyu Intelligence supports Mongolian businesses with local-language collaboration, flexible payment discussion, AI automation, web development, mobile apps, SaaS systems, and data intelligence.