Building a Unified Data & Intelligence Platform
project
27fcff8ae0648ba3
Normalized: building unified data and intelligence platform
moreland_contracts
Entity Properties (gold project table)
project_id
27fcff8ae0648ba3
name
Building a Unified Data & Intelligence Platform
client_id
—
canonical_metadata
—
created_at
2026-04-29 02:33:42.193466+00:00
updated_at
2026-04-29 02:33:42.193466+00:00
status
—
billable
—
recurring
—
squad
—
qa_partner
—
project_manager
—
lead_dev
—
description
Establishing the Data FOUNDATION & Profitability Intelligence through a unified data lakehouse environment and AI interface.
state
—
team_name
—
closedate
—
dealstage
—
deal_amount
—
contract_type
fixed_fee
contract_hourly_rate
—
contract_estimated_hours
—
contract_total_fee
60000.0
44%
Separation Confidence
How distinct this entity is from others. Higher means no close matches existed when it was created. Lower means a near-match was rejected just below the 80% threshold — worth reviewing.
Moderate — a somewhat similar entity exists
100%
Avg Match Confidence
The average confidence score across all active source mappings. Shows overall quality of linkage between source records and this canonical entity.
Strong source linkage
Source Mappings (2)
| Source | Source ID | Display Name | Confidence | Method | Status | Actions | |
|---|---|---|---|---|---|---|---|
| moreland_contracts | Ey8onL46mrLDVg | Building a Unified Data & Intelligence Platform | 1.00 | fuzzy+embedding | Active |
|
|
|
Source Record
bronze_moreland_contracts_ma_project_contracts
file_hash
fb9239d9dbcfe0e27521b81fb6e0486c
file_name
Extera FOUNDATION Proposal.docx
file_path
https://morelandpartners.sharepoint.com/sites/MorelandConnect-BusinessDevelopment/_layouts/15/Doc.aspx?sourcedoc=%7B23C02EFF-70ED-471F-9599-60AF6F0EA025%7D&file=Extera%20FOUNDATION%20Proposal.docx&action=default&mobileredirect=true
company
FOUNDATION
project_name
Building a Unified Data & Intelligence Platform
contact_person
—
contract_type
fixed_fee
hourly_rate
—
estimated_hours
—
total_fee_estimate
60000.0
brief_project_description
Establishing the Data FOUNDATION & Profitability Intelligence through a unified data lakehouse environment and AI interface.
start_date
2026-04-29
extracted_at
2026-04-29T02:02:33.227831+00:00
raw_text_preview
FOUNDATION Proposal – 29 April 2026
Building a Unified Data & Intelligence Platform
Phase 1: Establishing the Data FOUNDATION & Profitability Intelligence
Timeline: 4–6 weeks | Cost: $60,000 + Platform License
(Initial scope focused on rapid ROI via Gain & Fade automation)
Weeks 1–3: Data Lakehouse + Medallion Architecture
• Build a unified data lakehouse environment on Azure to consolidate Foundation ERP data
• Ingest raw Foundation data into bronze layer (full-fidelity snapshots for auditability)
• Develop ETL pipelines to transform and normalize data into clean, structured formats
• Create silver layer models (projects, invoices, time, costs) with deduplication and consistency
• Build gold layer business objects (job-level profitability, gain/fade, financial summaries)
• Implement a semantic layer (Cube) with:
Business-friendly definitions
Role-based security
Consistent naming and relationships
• Establish foundation for ERP-agnostic expansion (future systems like KBR)
Weeks 3–6: AI Interface + Profitability Use Case Deployment
• Launch a conversational AI interface for natural language data access
• Deploy initial AI “skills” focused on:
Gain & Fade reporting
Job profitability by month
Cost vs. revenue tracking
Variance analysis
• Automate the Gain & Fade report:
Eliminate manual Excel workflows
Provide real-time, queryable profitability insights
Enable drill-down by job, region, and timeframe
• Enable on-demand dashboards + AI-generated analysis
• Implement audit logging + secure access controls
• Optimize AI token usage and model selection for cost efficiency
What the Extera Team Will Experience
Instead of manually pulling data from Foundation, cleaning it in Excel, and rebuilding reports every month…
Extera teams will engage directly with FOUNDATION:
“Show me gain and fade by job for the last 6 months.”
→ FOUNDATION automatically compiles job-level profitability across all projects, highlights trends, and flags jobs with margin erosion.
“Which
file_created_at
2026-04-28T18:00:50+00:00
file_modified_at
2026-04-28T19:33:26+00:00
created_by
Jeff Kavlick
modified_by
Jeff Kavlick
_dlt_meta
—
|
|||||||
| moreland_contracts | fb9239d9dbcfe0e27521b81fb6e0486c | Building a Unified Data & Intelligence Platform | 1.00 | exact | Active |
|
|
|
Source Record
bronze_moreland_contracts_ma_project_contracts
file_hash
fb9239d9dbcfe0e27521b81fb6e0486c
file_name
Extera FOUNDATION Proposal.docx
file_path
https://morelandpartners.sharepoint.com/sites/MorelandConnect-BusinessDevelopment/_layouts/15/Doc.aspx?sourcedoc=%7B23C02EFF-70ED-471F-9599-60AF6F0EA025%7D&file=Extera%20FOUNDATION%20Proposal.docx&action=default&mobileredirect=true
company
FOUNDATION
project_name
Building a Unified Data & Intelligence Platform
contact_person
—
contract_type
fixed_fee
hourly_rate
—
estimated_hours
—
total_fee_estimate
60000.0
brief_project_description
Establishing the Data FOUNDATION & Profitability Intelligence through a unified data lakehouse environment and AI interface.
start_date
2026-04-29
extracted_at
2026-04-29T02:02:33.227831+00:00
raw_text_preview
FOUNDATION Proposal – 29 April 2026
Building a Unified Data & Intelligence Platform
Phase 1: Establishing the Data FOUNDATION & Profitability Intelligence
Timeline: 4–6 weeks | Cost: $60,000 + Platform License
(Initial scope focused on rapid ROI via Gain & Fade automation)
Weeks 1–3: Data Lakehouse + Medallion Architecture
• Build a unified data lakehouse environment on Azure to consolidate Foundation ERP data
• Ingest raw Foundation data into bronze layer (full-fidelity snapshots for auditability)
• Develop ETL pipelines to transform and normalize data into clean, structured formats
• Create silver layer models (projects, invoices, time, costs) with deduplication and consistency
• Build gold layer business objects (job-level profitability, gain/fade, financial summaries)
• Implement a semantic layer (Cube) with:
Business-friendly definitions
Role-based security
Consistent naming and relationships
• Establish foundation for ERP-agnostic expansion (future systems like KBR)
Weeks 3–6: AI Interface + Profitability Use Case Deployment
• Launch a conversational AI interface for natural language data access
• Deploy initial AI “skills” focused on:
Gain & Fade reporting
Job profitability by month
Cost vs. revenue tracking
Variance analysis
• Automate the Gain & Fade report:
Eliminate manual Excel workflows
Provide real-time, queryable profitability insights
Enable drill-down by job, region, and timeframe
• Enable on-demand dashboards + AI-generated analysis
• Implement audit logging + secure access controls
• Optimize AI token usage and model selection for cost efficiency
What the Extera Team Will Experience
Instead of manually pulling data from Foundation, cleaning it in Excel, and rebuilding reports every month…
Extera teams will engage directly with FOUNDATION:
“Show me gain and fade by job for the last 6 months.”
→ FOUNDATION automatically compiles job-level profitability across all projects, highlights trends, and flags jobs with margin erosion.
“Which
file_created_at
2026-04-28T18:00:50+00:00
file_modified_at
2026-04-28T19:33:26+00:00
created_by
Jeff Kavlick
modified_by
Jeff Kavlick
_dlt_meta
—
|
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