Data Cleanup
$3,000 - $12,000
Clean, normalize, deduplicate, and document one or more important business datasets.
Data audit
Cleanup scripts
Quality rules
Handover documentation
Core Module 06
ANALYTICS AND DATA AI
Data Intelligence
Clean messy business data, build dashboards, detect patterns, and let teams ask questions in natural language.
This module helps businesses move from scattered spreadsheets and reports to structured data, dashboards, AI analysis, and decision support.
Best for
Best-fit use cases
Teams with messy spreadsheets or duplicated data
Businesses needing dashboards and KPIs
Organizations exploring private analytics assistants
Pricing
Package ranges
These ranges are decision-making references. Final scope and price should be confirmed after discovery.
Dashboard MVP
$8,000 - $25,000
A focused dashboard for KPIs, reports, filters, and decision visibility.
Data model
Dashboard UI
Charts and filters
Deployment
Private Data AI
$25,000 - $100,000+
A private analytics assistant or data intelligence product connected to internal knowledge and databases.
Private RAG
Natural-language querying
Access controls
Monitoring and optimization
Scope
What is delivered
Each module includes planning, architecture, implementation, launch support, and practical handover instead of code alone.
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
Outcomes
Useful 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.
Process
Delivery process
01 / 1-2 weeks
Data discovery
Map data sources, owners, fields, quality issues, access rules, and decision questions.
02 / 2-5 weeks
Modeling and cleanup
Clean data, create schemas, define KPIs, and establish validation rules.
03 / 3-10 weeks
Dashboard or AI build
Build dashboards, reports, data assistant workflows, and access-controlled interfaces.
04 / 1-3 weeks
Validation and adoption
Compare outputs to known truth, train users, and document maintenance routines.
Useful information
Notes and technology
PostgreSQLSupabasePythonPandasVector databasesNext.jsBI dashboardsPrivate RAG
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.
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