— Regulus · AI Agent Company

From AI-generated code
to production software.

Development automation for the post-vibe-coding era.

Regulus analyzes AI-built MVPs and complex codebases to find where they break, repair the right parts, verify the result, and automate post-deploy bug monitoring and load anomaly detection.

Request a project diagnosis See Asterism

— Company Thesis

Codebases or markets,
complexity becomes flow.

“Regulus builds agents that read, reason through, and act on complex systems.”

Regulus is an AI agent company building development automation and quant intelligence. Our first pillar is Asterism: an engine for diagnosing and repairing the places where AI-built MVPs get stuck across auth, databases, deployment, bugs, and agent-led operations.

Our second pillar is Merlu, an internal quant intelligence engine. By studying market data and predictive signals, we sharpen how our agents operate in uncertain systems.

— Where Builders Get Stuck

Vibe coding accelerates the start.
Production is a different problem.

AI can generate code quickly, but real products still need auth, data, deployment, error handling, logs, security, performance, bug monitoring, and load detection. Regulus narrows that gap with agent automation.

Deploy Failure

It works locally, then breaks in production

We trace environment variables, build settings, API paths, and runtime differences until the project can ship.

Auth · DB · Payment

Auth, databases, and payments block the build

We inspect the integration layer that turns prototypes into services: Supabase, OAuth, Stripe, APIs, and more.

AI Fix Loop

AI keeps fixing symptoms and creating new bugs

We read structure, state flow, and dependencies before patching, so the repair scope stays controlled.

No Verification

There is no reliable test or validation loop

We establish build, lint, key-flow, and regression checks so changes can be trusted.

Agent Operations

The operating layer is still manual

Agents take over bug monitoring, error classification, load anomaly detection, alerts, and response flows.

Codebase Drift

The codebase grows until nobody knows where to change it

We reconnect features, data flow, and responsibility boundaries so the next fix is possible.

Asterism Asterism
Analyze → Verify → Operate

From scattered code to working systems.

pipeline · live
running
▸ 9 nodes scanned ▸ constellation resolved

— Asterism · Development Automation

We turn stuck MVPs
into repairable execution flows.

Asterism handles the gap after AI coding. It reads the repository, connects errors to structure, creates a repair plan, and carries the project through tests, deployment, bug monitoring, and load detection.

  • ▸ Repository map · Files, APIs, state, and data flow connected like a constellation
  • ▸ Targeted fix · Root causes and repair scope narrowed before patching
  • ▸ Operate by agents · Build, test, deploy, monitor, detect, and respond loops for service readiness

— Automation Flow

Analyze, Fix, Verify,
Deploy, Monitor,
Detect, Respond.

Development automation does not end with code generation. Regulus carries fixes through verification and deployment, then lets agents monitor bugs, detect load anomalies, alert the right channel, and trigger response flows.

01ANALYZE
02FIX
03VERIFY
04DEPLOY
05OPERATE

Codebase Intelligence

Analyze

We read file structure, routing, APIs, state management, and data models to map how the system actually works.

  • ▸ Repository structure analysis
  • ▸ Error root-cause tracing
  • ▸ Dependency and environment checks

Autonomous Repair

Fix · Verify

We create a repair plan, apply targeted patches, and use build and test evidence to confirm progress.

  • ▸ Targeted patch generation
  • ▸ Regression risk review
  • ▸ Verification loop automation

Agent Operations

Deploy · Monitor · Detect

After deployment, agents detect bug signals, error patterns, load anomalies, and route them into response flows.

  • ▸ Deployment readiness checks
  • ▸ Automated bug monitoring
  • ▸ Load and incident signal detection
  • ▸ Alert and response loop orchestration

— Merlu · Quant Intelligence

Market data is also
a complex system.

Merlu, our internal AI quant engine, studies crypto markets through quantitative signals and predictive trajectories. Development automation and quant intelligence are different surfaces of the same thesis: read complexity and convert it into executable judgment.

  • ▸ AI price forecasting · Multi-horizon Bitcoin trajectories and confidence bands
  • ▸ Real-time market parsing · Price, volume, and volatility aligned into usable signals
  • ▸ Agent decision research · Quant signals connected to automated decision flows
BTC / USD · Forecast
Merlu Engine

Forecast accuracy

87.4%

Signal

▲LONG

Confidence

78% · 4h