Four products that form a compounding system around your AI coding agents. Understand your code, review every session, find recurring patterns, and enforce what works — automatically.
Works with the coding agents your team already uses.
Every session starts from zero. The agent has no memory of what failed before, no understanding of your codebase's structure, and no mechanism to prevent known failures from recurring.
A team of 20 developers running AI sessions daily accumulates thousands of sessions per quarter. Hidden in those sessions are recurring anti-patterns — the same check missed, the same API misused, the same dependency edited without updating callers. Each costs 15–30 minutes of rework.
And when teams switch between AI coding tools, everything learned evaporates. Knowledge lives inside the tool vendor's platform, not with the team that paid for it in debugging time.
Each product is independently valuable. Together, they form a harness that gets smarter with every session, across every tool.
Code Structure Intelligence
Parses your entire codebase, extracts every function definition and call site, and resolves the complete call graph. The agent gets a structural map upfront instead of exploring by trial and error.
Saves tokens at scale — a single structural query replaces hundreds of exploratory tool calls
Eliminates structural hallucination — signatures, imports, and call chains are parsed from source, not inferred
Always current — runs as a git hook in milliseconds, 10 languages, fully deterministic
Session Intelligence
Captures every AI coding session — every prompt, edit, command, and outcome. An automated reviewer analyzes each session and produces structured assessments with causal chains linking decisions to results.
Accountability — every session is reviewed automatically with consistent, structured assessments
Root cause, not symptoms — causal chains trace from the decision that caused a problem to the error that resulted
Measurable quality — track session quality over time, by developer, by project, by task type
Code Knowledge Engine
Builds a persistent, growing knowledge base from every source of code intelligence. Clusters similar issues across sessions to find recurring patterns invisible at the individual level, validated with evidence chains.
Knowledge compounds — every session makes the system smarter; patterns invisible at 10 sessions become unmistakable at 500
Copilot-agnostic — the canonical model works across Claude Code, Cursor, Copilot, and any other tool
Multi-source — sessions, PR reviews, CI failures, and error logs all feed the same knowledge graph
Adaptive Enforcement
Composes facts from every source into enforcement directives served to agents in real time. Rules aren't written by humans — they emerge from evidence, strengthen with confirmation, and self-prune when no longer needed.
Prevention, not observation — directives fire before the agent writes problematic code, not after
Self-evolving — rules emerge from evidence, evolve with new sessions, and retire when no longer needed
Zero configuration — no rule files to write, no policies to configure; the system bootstraps from usage
Structure informs review. Reviews feed pattern detection. Patterns drive enforcement. Enforcement creates new evidence. The system compounds with every session.
Your Code
“Here's what depends on what”
AI Sessions
“Here's what happened and why”
All Sources
“Here's what keeps going wrong”
Combined Facts
“Don't do that — here's why”
Vokt's call graph makes Lens reviews structurally aware
Lens sessions feed Kramer's pattern detection
Kramer's patterns drive Aegis's enforcement rules
Aegis's outcomes feed back into Kramer's learning
Your AI agent learns from your past sessions. It stops making the same mistakes. It knows your codebase structure. It gets better every day.
Knowledge flows across team members automatically. One person's hard-won debugging lesson becomes everyone's guardrail. New team members inherit the team's accumulated intelligence from day one.
AI coding quality is measurable for the first time — quality trends, pattern prevalence, enforcement effectiveness, rework rates. Make data-driven decisions about AI coding adoption.
A team using Claude Code for backend, Cursor for frontend, and Copilot for infrastructure builds a unified knowledge base across all three. Switch tools without losing what you've learned.
No AI coding tool vendor will build this — they're incentivized to lock knowledge inside their platform. We're incentivized to make it portable.
Tool-locked knowledge
Switch vendors and start from zero. Months of learnings disappear.
VoktLabs knowledge
Portable, compounding, copilot-agnostic. The knowledge stays with your team.
The longer your team uses the system, the more valuable it becomes.
Six months of accumulated patterns, validated learnings, and proven enforcement rules represent an asset that can't be rebuilt by switching to a competitor.
The moat is time — specifically, your team's investment in making the system smarter.
Understand your code. Review every session. Find the patterns. Enforce what works. Get smarter with every session.
Contact SalesWorks alongside your existing coding-agent workflow.