PLAYBOOK · ENGINEERING
Autonomous pull-request review and repo insight.
Engineering bottlenecks happen at review time. VDF AI ships code_review, pr_review_assistant, impact_analysis, security_scan, and the github MCP tool out of the box. Wire them into one Review Network and you have an autonomous senior reviewer.
The problem
Senior reviewers are scarce
The reviewer who spots the subtle race condition is in three other meetings. Public AI tools see the diff but not your codebase's context, your prior incidents, or your security posture.
The VDF AI approach
A reviewer that knows your repo
VDF AI indexes your code into a vector store, calls GitHub directly, runs static heuristics, and reasons over the diff with a model you control — never sending source to the public.
REFERENCE ARCHITECTURE
PR opens → full review in minutes
Webhook
Built-in MCP tools
github_vector_search
Intent: review-pr
PLAYBOOK · STEP BY STEP
Make the review pipeline autonomous
Index your repos with VDF Data
Build the GitHub vector store. Now github_vector_search can ground every review in surrounding code.
Compose the Review Network
Drop the built-in MCP tools onto the canvas. Bind them to an intent template named review-pr.
Hook the GitHub webhook
A small Custom HTTP tool consumes the PR event, calls the Network, and posts the response back as a PR comment.
Tune system prompts per repo
Use domains to keep service-specific norms (e.g., "this repo is regulated, flag any external dependency").
Watch quality compound
SEEMR learns which sub-task each model does best. Energy and cost per review drop quarter over quarter.
OUTCOMES
PR latency drops, quality holds
median time-to-first-review.
defects caught before merge in pilot repos.
source code sent to external SaaS providers.
SEEMR REFERENCE
Routing by code intent
Security-sensitive diffs get your high-capability private model. Doc-only changes get a small fast model. SEEMR picks based on diff signal.
GET IN TOUCH
You Have Questions
Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.