ContextREC-114
Repeated repository discovery detected
Several sessions repeatedly reconstruct the same architecture and build context.
Recommendation
Create reusable repository-level AI context and instructions.
AI Engineering Intelligence
Understand how your engineering organization consumes AI.
VECTORIA connects AI tools, models and engineering systems to understand adoption, quality of usage, cost and engineering impact — and identify how to improve.
VECTORIA
UNDERSTANDDetect patterns across tools, teams and engineering workflows.
Claude Code · Cursor · Copilot · Codex · Agents
↓VECTORIA
UNDERSTANDDetect patterns across tools, teams and engineering workflows.
Repositories · Issues · CI / Tests · Deployments · Models
The Blind Spot
Engineering has adopted AI faster than organizations can understand it.
Engineers are increasingly working through coding assistants, agents and models. But most organizations see only fragments: licenses, token spend and isolated vendor dashboards.
They cannot easily answer:
- Who is actually using AI effectively?
- Which workflows are working?
- Where is expensive intelligence being wasted?
- Are engineers developing better AI-native practices?
- Is AI actually improving software delivery?
VECTORIA creates a common intelligence layer across engineering AI consumption.
Engineering AI Command Center
One view of how engineering consumes intelligence.
Engineering AI Overview
Weekly AI Adoption
78%
+6 ptsvs prior period
AI adoption over Last 30 days: from 71% (W31) to 78% (W35). Use the arrow keys to read individual points.
AI-Assisted PRs
46%
of merged pull requests
AI-Assisted PRs169AI-assisted367merged PRsLast 30 daysVerification Rate
71%
sessions running tests
Verification Rate2,415sessions analyzed1,715observed running testsLast 30 daysAI Cost / Engineer
€96
monthly average, all providers
AI Cost€7,680total80active engineersAcross approved providersAgentic AI Users
+11 ptsof active engineers
34%
- Autonomous sessions1,352Autonomous sessionsSessions where the coding agent executes multiple tool actions without requiring a human instruction between every step.56% of 2,415 sessions
- Multi-step workflows62%Multi-step workflowsSessions involving multiple distinct engineering operations such as analysis → edit → test → iteration.3+ distinct operations per session
- Reusable context42%Reusable contextSessions consuming persistent repository or organizational context rather than reconstructing it from scratch.sessions loading shared context
Detected Opportunities
Live4 opportunities · all teams
Team AI Engineering Fitness
4 teams · 80 engineers · select a team to filterTeamAdoptionAgenticAI PRsMaturity
- 24 engineersAdoption94%Agentic52%AI PRs66%AdvancedAI Engineering FitnessComposite organizational signal based on:· AI adoption· agentic workflows· context discipline· verification· model economics· engineering outcomesTeam-level patterns, not individual performance.
- 18 engineersAdoption86%Agentic38%AI PRs54%StrongAI Engineering FitnessComposite organizational signal based on:· AI adoption· agentic workflows· context discipline· verification· model economics· engineering outcomesTeam-level patterns, not individual performance.
- 26 engineersAdoption71%Agentic21%AI PRs35%DevelopingAI Engineering FitnessComposite organizational signal based on:· AI adoption· agentic workflows· context discipline· verification· model economics· engineering outcomesTeam-level patterns, not individual performance.
- 12 engineersAdoption48%Agentic22%AI PRs18%EmergingAI Engineering FitnessComposite organizational signal based on:· AI adoption· agentic workflows· context discipline· verification· model economics· engineering outcomesTeam-level patterns, not individual performance.
AI Engineering Fitness
Measure more than adoption.
01
Adoption
Understand active users, tools, model usage, agent usage and AI-assisted workflows.
Example signals
- Active engineers
- AI sessions
- Agent delegation
- Tools and models
02
Proficiency
Understand how effectively engineers work with AI.
Example signals
- Context quality
- Task decomposition
- Agentic workflows
- Verification discipline
- Reusable instructions and skills
03
Economics
Understand what intelligence costs and where spend can be optimized.
Example signals
- Cost per engineer
- Cost per task
- Model mix
- Repeated calls
- Avoidable spend
04
Engineering Impact
Connect AI activity with actual software delivery.
Example signals
- Commits
- Pull requests
- Cycle time
- Review iterations
- Tests
- Rework
- Defects
Continuous Enablement
VECTORIA doesn’t just show what is happening. It identifies what to improve.
VECTORIA analyzes engineering AI behavior, detects recurring usage patterns and turns them into actionable recommendations for engineers, teams and engineering leadership.
Model EconomicsREC-127
High-cost models used for routine tasks
Routine refactoring workloads are consistently being sent to the highest-cost model tier.
Recommendation
Evaluate a smaller approved model for this task class.
VerificationREC-139
High agent usage, low verification
Agent delegation is strong, but automated test execution is below the team baseline.
Recommendation
Add test execution and validation to the standard agent workflow.
Organizational Learning
Turn AI power users into organizational capability.
The best AI engineers develop workflows, context patterns and agent practices that often remain invisible to the rest of the organization.
VECTORIA identifies successful patterns, compares them across teams and helps turn individual practice into repeatable engineering capability.
- Power Users
- Successful Patterns
- Reusable Workflows
- Team Adoption
- Measured Outcomes
Top AI-native engineers
- more agent delegation
- 2.3×
- more reusable context
- 1.7×
- fewer retries
- 31%
- verification discipline
- Higher
Illustrative — not a VECTORIA benchmark
Engineering Intelligence Layer
AI activity, understood in engineering context.
VECTORIA connects the tools engineers use with the systems where software is planned, verified and delivered.
AI ENGINEERING
ENGINEERING INTELLIGENCE LAYER
VECTORIA
UNDERSTAND
Detect patterns across tools, teams and engineering workflows.ENGINEERING SYSTEMS
AI ENGINEERING
Claude Code · Cursor · Copilot · Codex · Agents
ENGINEERING INTELLIGENCE LAYER
VECTORIA
UNDERSTAND
Detect patterns across tools, teams and engineering workflows.ENGINEERING SYSTEMS
Repositories · Issues · CI / Tests · Deployments · Models
Control Plane
Observe intelligence today. Control it tomorrow.
As VECTORIA becomes the common intelligence layer for engineering AI, the same infrastructure can govern how models and providers are consumed.
01
Govern
Approved models, providers, users, policies, budgets and data boundaries.
02
Route
Select an approved model based on task, quality, cost, latency and policy.
03
Optimize
Continuously compare models and configurations against real engineering workloads.
04
Measure
Connect AI consumption with cost, behavior and engineering outcomes.
- Telemetry
- Intelligence
- Policy
- Routing
- Optimization
One Intelligence Layer
Across the engineering AI stack.
AI Tools
- Claude Code
- Cursor
- GitHub Copilot
- Codex
- Agents
Engineering
- GitHub
- GitLab
- Jira
- Linear
- CI/CD
- Testing
Models
- Anthropic
- OpenAI
- Open models
- Private inference
VECTORIA is designed to connect signals across AI tools, engineering systems and model providers.
VECTORIA
The intelligence layer between engineering organizations and AI.
- 01See how AI is used.
- 02Understand what works.
- 03Improve how engineers use it.
- 04Control how intelligence is consumed.