Seven layers. One clear answer.
Production AI inside federal boundaries: grounded assistants, document workflows, computer vision and decision analytics.
- $1,200 Chief-of-staff setup, done with you
- $600 One data story, ready to post
- $12,500 Pre-launch test of an AI assistant
Founder's prior work for NASA, FPS, FAA, VA, Census and DHS, 2016 to 2024. Not Seven Sky past performance. The record
Founder's record
- NASA: workforce analytics, 17,800 staff
- FPS: risk scoring, ~9,000 facilities
- FAA: attrition forecasting, two workforces
- VA: position descriptions and skill matching
- Census: Data Science Training Program
- Census: SAS-to-Python production move
- DHS: analysis behind the CWMD Office
The founder's prior work through LMI, Grant Thornton, a Census contract and DHS, 2016 to 2024. Not Seven Sky past performance.
Every engagement is a stack of seven.
Seven layers of heaven, down to the cloud: every engagement is a stack of seven, and every layer has a check and a training.
Every layer has a check and a training. Read it bottom-up: the data stays in its boundary, the decision comes out the top.
- Decide · OverseeThe decision, measured; evaluate, monitor, review.Training · AI oversight and evaluation
- Answer · ActGrounded in your documents; agentic workflows.Training · Building grounded assistants
- Model · GroundTested on unseen data; a source passage on every answer.Training · Machine learning in practice
- Understand · ReasonOne definition per number; models inside the boundary.Training · Statistics for decisions
- Govern · OrganiseAccess and lineage; lakehouse, index, catalog.Training · Data engineering foundations
- Move · RunPipelines that fail loudly; compute, storage, network.Training · Python for analysts
- Source · BoundaryNamed and owned; where the data must stay.Training · Data and AI literacy for leaders
Decide
The decision, measured.
Oversee
Evaluate, monitor, review.
AI oversight and evaluation
Answer
Grounded in your documents.
Act
Agentic workflows.
Building grounded assistants
Model
Tested on unseen data.
Ground
Source passage on every answer.
Machine learning in practice
Understand
One definition per number.
Reason
Models inside the boundary.
Statistics for decisions
Govern
Access and lineage.
Organise
Lakehouse, index, catalog.
Data engineering foundations
Move
Pipelines that fail loudly.
Run
Compute, storage, network.
Python for analysts
Source
Named and owned.
Boundary
Where the data must stay.
Data and AI literacy for leaders
How we work
- Stay in the boundary: data stays put.
- Ground every answer in its source.
- Measure on held-out sets before launch.
- Hand over code, standards and training.
What we build
- Grounded assistants on agency data
- Agentic document workflows (LangGraph)
- Computer vision: SeeDeep.ai analytics
- Decision analytics: Python, Plotly Dash
- Advisory: AI strategy and model risk