Swisper Prism

Cut AI input tokens by ~56%. Improve outcomes at the same time.

Prism indexes your codebase into a structured, searchable, explained knowledge graph for coding agents and humans.

Works with Claude Code, Cursor, and any MCP client.

Try Prism

At a glance

~56%

Average Token reduction

-90%

Time searching for context

<1s

Context retrieval latency

48

Languages Supported

12+

Navigation Tools

Your codebase, indexed, explained, searchable.

Every function, class, and module is parsed, embedded, and described in plain language. AI coding agents query it for the exact context they need, so they stop burning input tokens reading file after file.

Prism dashboard showing token usage, agent activity, and context retrieval
Hover the highlighted areas to learn more about each feature of the Prism dashboard.

AI Agents without Prism read too much and still miss what matters.
They retry. You pay.

What's actually happening

AI Agents

01

Read file after file

02

Miss the right module

03

Try again

A 5k token task becomes 15k.

Developers

01

Search

02

Ask

03

Rebuild mental models

Weeks just to understand the system.

Same problem. Different symptoms.

For AI and for people

One intelligence layer. Two channels. Always in sync.

Prism indexes, embeds, and maps every file once. From there, the same knowledge is reachable two ways, so the agent writes and the human reasons from the same source of truth.

For AI agents

Context through MCP

Agents query the layer through MCP tools that plug straight into Claude Code, Cursor, and any MCP client.

  • Precise context, not whole files
  • Fewer input tokens per task
  • More reliable output

For humans

Understanding through the Console

People reach the same layer through the Console: searchable code, a living wiki, and the real dependency graph.

  • Instant understanding of unfamiliar code
  • Faster navigation across the system
  • No dependency on tribal knowledge

Same problem, different symptoms. One layer solves both.

Where Prism creates value

Make your codebase an asset, not a liability.

The same indexing pipeline, pointed at the problems that cost businesses the most: aging cores, due diligence, and onboarding.

Legacy codebase intelligence

Understand systems nobody understands anymore.

  • Turns tribal knowledge into searchable intelligence
  • Shows which modules are critical
  • Built for core banking transformation
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What Prism does

Why AI coding agents need Prism.

Models are not the bottleneck but context is. Prism gives AI coding agents structured, searchable, explained knowledge of your codebase, cutting wasted tokens and improving the quality of what they ship.

Hybrid search, not whole files

  • Four-way retrieval: semantic meaning, intent, keyword, exact match
  • Reranked by relevance and architectural importance
  • Returns the smallest useful slice, not file after file

A dependency graph, not a flat file list

  • Every function, class, and module parsed into a knowledge graph
  • Ranked with PageRank over the import graph
  • A top-down map of how the system actually fits together

Always current, one config block

  • Re-indexes only the files that change on each push
  • Drops into Claude Code, Cursor, or any MCP client in one block
  • REST API available, no SDK to install, no agent to run

How indexing works

Connect a repo. Prism builds a queryable model of it.

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.

See Prism cut your token bill.

We will index a repo and show both your agents and your team working from the right context.