Give Your AI a Brain
AI coding assistants are the most powerful tools engineers have ever had. But they're flying blind — every conversation starts from zero, every question re-discovers the same architecture. Nomik changes that.
The Observation
We noticed something watching engineers use AI assistants: the AI keeps asking the same questions.
"Which database does this service use?" — the AI figured it out yesterday, but today it starts from scratch. "What calls this function?" — the AI greps the codebase and misses the cron job that triggers it at midnight. "Is this safe to change?" — the AI guesses, because it can't see the 8 downstream callers.
The problem isn't the AI. The problem is that nobody gave it the map.
The Thesis
Code is not a flat collection of files. It's a living graph — functions call functions, routes handle requests, services write to databases, cron jobs trigger workflows, queues pass messages between systems.
Today's AI tools treat code like text. They search by keywords, stuff files into prompts, and hope the LLM connects the dots. When the context window overflows, they truncate. When relationships span multiple files, they miss them.
Nomik's thesis is simple: if you build a persistent knowledge graph of your codebase — every function, every route, every DB table, every dependency chain — and let AI query it directly, the quality of AI-assisted engineering goes from "useful but risky" to "precise and trustworthy."
What Makes This Different
There are tools that search code. There are tools that lint code. There are tools that generate code. Nomik does none of those things — it understands code.
| Approach | What It Does | The Gap |
|---|---|---|
| grep / ripgrep | Text search across files | No understanding of relationships — a comment matches the same as a function call |
| IDE (LSP) | Go-to-definition, find references | Single-language, no cross-system awareness (DB, APIs, queues, crons) |
| RAG / embeddings | Semantic similarity search | Returns similar text, not actual dependencies — misses the cron job |
| Static analyzers | Lint rules, code smells | Rule-based, not queryable — can't answer 'what breaks if I change X?' |
| Nomik | Persistent knowledge graph queried by AI via MCP | No gap — this is the missing layer |
Three Principles
Your code never leaves your machine. Neo4j runs on localhost. No cloud, no telemetry, no data exfiltration. The graph stores metadata — function names, file paths, relationships — never raw source.
Every feature is designed for AI consumption first, human second. The MCP tools return exactly the context an LLM needs — structured, scoped, and ranked by relevance. Not a human dashboard with an API bolted on.
The file watcher keeps the graph in sync with every save. Incremental scans re-parse only changed files. Your AI's knowledge is never stale — it evolves with your code.
What Nomik Sees That Others Don't
Standard tools understand your code at the file level. Nomik understands it at the architecture level:
HTTP Routes
Express, Fastify, NestJS, Flask, Django, ASP.NET — every endpoint mapped to its handler function.
Database Operations
Prisma, Supabase, Knex, raw SQL, Entity Framework — who reads and writes to every table and column.
External APIs
Stripe, AWS SDK, fetch, axios — every outbound call tracked with methods and base URLs.
Events & Queues
Socket.IO, Bull/BullMQ, Kafka, RabbitMQ, SQS — message producers and consumers linked.
Cron Jobs
node-cron, @nestjs/schedule, agenda — scheduled tasks connected to the functions they trigger.
Security Issues
Hardcoded secrets, weak crypto, exposed credentials — flagged with file location and severity.
Where This Is Going
Today, Nomik maps your code. The long-term vision is to map your entire technical organization:
Functions, classes, routes, DB ops, queues, events, security
LiveDocker, Terraform, CloudFormation, K8s, CI/CD pipelines, OpenAPI
LivePrometheus metrics, OpenTelemetry spans, Datadog traces
LiveADRs, runbooks, incident reports, team ownership maps
Research