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How it works

Prism doesn't search
your codebase.
It understands it.

Most tools give AI a file list. Prism gives it structured intelligence. Every function explained, every relationship mapped, every answer grounded in how your system actually behaves.

Try PrismSee how it works

The Problem

The bottleneck isn't the model.
It's the context.

AI agents and developers hit the same wall.

Large codebases don't fit into a prompt.

So both humans and AI:

Read file after file
Miss critical modules
Rebuild context from scratch

Time is lost.

Quality drops.

Decisions are made on incomplete understanding.

This is a context engineering problem — and it's what Prism is built to solve.

From search to understanding

Indexing Pipeline

When you connect a repository, Prism runs a one-time indexing pipeline. The result is a persistent, queryable model of your codebase, updated automatically as your code changes.

  1. 01

    Clone

    Prism pulls your repository at the exact branch and commit you choose. Always current, always in sync, no stale snapshots.

  2. 02

    Scan

    Every file is walked and classified, binaries, generated code, and noise are filtered out automatically. Only meaningful source enters the pipeline.

  3. 03

    Context

    READMEs, architecture docs, and vision files are collected and stored as first-class context. The AI Architect knows why your code exists, not just what it does.

  4. 04

    Rules

    Coding standards and convention files are extracted and indexed. When you ask for guidance, Prism answers with your team's rules, not generic best practices.

  5. 05

    Parse

    Source code is split at the AST level into functions, classes, and modules, then embedded with a code-tuned model. No arbitrary line breaks, no lost context.

  6. 06

    Describe

    Every chunk gets a plain-language summary generated by LLM: what it does, why it matters, and how it connects. Human-readable understanding at machine scale.

  7. 07

    Map

    Modules are ranked by structural importance using PageRank over the import graph. Prism knows which files are core and which are peripheral.

  8. 08

    Conventions

    Frameworks, languages, and project structure are detected automatically. The AI adapts its answers to your stack, not a one-size-fits-all response.

  9. 09

    Edges

    Import and dependency relationships are resolved and persisted as a full graph. Every "who calls what" and "what depends on this" question has a real answer.

  10. 10

    Arch

    Components, modules, and their capabilities are identified and summarized into a structured architecture index. Navigate your system top-down, not grep-and-guess.

  11. 11

    Enrich

    Generated descriptions are embedded into a dedicated semantic layer. This powers description-based search, find code by what it does, not just what it's named.

What happens when you ask a question

“Where is payment validation handled?”

Search

Finds everything related to your question

  • Meaning (semantic)
  • Intent (descriptions)
  • Keywords & exact matches

Rank

Understands what actually matters

  • Merge all results
  • Score by relevance
  • Boost architectural importance

Focus

Builds the smallest useful context (no noise, no guessing)

  • Select top modules only
  • Attach explanations
  • Remove noise

Answer

Returns exactly what's needed for the next decision

  • Return precise context
  • Ready for AI or developer
  • No additional search needed

For AI and developers

Same system.
Two interfaces.

For AI agents

  • Precise context
  • Fewer tokens
  • More reliable output

For developers

  • Instant understanding
  • Faster navigation
  • No dependency on tribal knowledge

Integration

Works where you already work.

Prism speaks MCP and REST. One config block and you're connected.

  • Cursor
  • Claude Code
  • Any MCP-compatible client

No new environment. No workflow changes. Just better context.

Context

The difference is context.

Without context, AI models guess. They scan files, infer relationships, and hope for the best. Errors compound. Suggestions miss the mark.

Prism makes your system understandable — to AI and to you. Every function is explained. Every dependency is mapped. Every query is answered with precision.

The model doesn't change. The context does.

What you get

Output that actually helps.

Instead of raw files, you get:

  • The right code
  • A clear explanation
  • Architectural context

_validate_rs256_sig(token, public_key)

→ Validates a JWT signature using RS256 and raises an error if verification fails.

Works where you already work

Connect in minutes.

Prism speaks MCP and REST. Drop it into Cursor, Claude, or any agent framework with a single config block.

Cursor

Drop Prism's MCP server into your Cursor config and start querying your codebase from the AI chat.

// .cursor/mcp.json
{
  "mcpServers": {
    "prism": {
      "url": "https://mcp.swisperprism.com",
      "apiKey": "YOUR_API_KEY"
    }
  }
}

Claude

Connect Prism to Claude Desktop or Claude API. Your codebase becomes a first-class tool Claude can query mid-conversation.

// claude_desktop_config.json
{
  "mcpServers": {
    "prism": {
      "command": "npx",
      "args": ["-y", "@swisperprism/mcp"],
      "env": { "PRISM_API_KEY": "YOUR_KEY" }
    }
  }
}

REST API

Integrate Prism into your own tooling. Full REST API with search, graph traversal, and symbol lookup endpoints.

curl -X POST https://api.swisperprism.com/v1/search \
  -H "Authorization: Bearer YOUR_KEY" \
  -d '{
    "query": "validate user session",
    "repo": "my-org/backend",
    "top_k": 5
  }'

The difference is context

Give your AI the context
it's been missing.

Prism indexes once. From that point on, every agent query and developer search is grounded in how your system actually works.

Try PrismRead the docs