Sound familiar?
You start by just asking. Then you realize the AI needs context, and suddenly you're spending more time managing it than doing actual work.
Things you or your team have probably tried
Each one works until it doesn't.
Copy-paste and markdown files
Where everyone startsYou paste relevant docs into each conversation. Maybe you keep a markdown file with project context that you attach to prompts. Simple and immediate.
The ceiling: Falls apart past ~10 files. Can't share across tools. Goes stale the moment your docs change. You become the sync mechanism.
Project rules and config files
The power user approachCustom instructions, project rules, saved prompts. You invest time writing context tailored to each tool. Smarter than raw copy-paste.
The ceiling: Locked to one tool. Can't share with teammates. Manual to update. Duplicated effort across Claude, Cursor, and ChatGPT.
DIY RAG pipeline
The engineering approachYou build a retrieval pipeline. Embeddings, vector database, chunking strategy, retrieval logic. Full control over everything.
The ceiling: Maintaining it becomes a real job. How context is stored, retrieved, presented, kept current, whether you're surfacing the right pieces. That's engineering time not spent on the product your customers actually use.
How they compare
| Markdown Files | DIY RAG | Wire | |
|---|---|---|---|
| Setup time | Minutes | Days to weeks | Minutes |
| Stays current | Manual updates | You build the pipeline | Upload new files, done |
| Works across tools | Copy-paste per tool | Locked to your stack | MCP |
| Team sharing | Shared drive / git | Custom auth layer | RBAC built in |
| Usage tracking | None | Build your own logging | Per-query tracking |
| Agents can write back | Read-only | Read-only retrieval | Agents add context via MCP |
| Real-time shared state | Each agent gets a copy | Stale until re-indexed | Instant access across agents |
| Context quality | Raw text dump | Depends on your chunking | Extracted concepts and entities |
| Token efficiency | Entire file pasted in | Returns matching chunks | Targeted context, minimal tokens |
| Source tracking | Hope you remember | If you build it in | Every piece tagged to its source |
| Any file type | Text only | Custom parsers per format | PDFs, docs, spreadsheets, data |
| Scales past 10 docs | Hits context limits | If you maintain it | Out of the box |
Setup time
Stays current
Works across tools
Team sharing
Usage tracking
Agents can write back
Real-time shared state
Context quality
Token efficiency
Source tracking
Any file type
Scales past 10 docs
How it works
Get started in minutes, not days.
Create a Container
Organize context by project, team, or use case. Each container is private by default.
Connect Your Agents
Connect via MCP from Claude, Cursor, or any compatible tool. Your agents access shared context instantly.
Add Your Context
Add context through AI agents or file uploads. Wire structures everything for AI consumption.
Explore specific use cases
Wire solves different problems for different people. Dig into what matters most to you.
AI Portability
Try different models without losing your context
Team Context
Stop hoping your team downloaded the latest docs
Token Costs
Stop reprocessing the same context every session
Context Limits
Your context is too big for the agent
Public Distribution
Let anyone learn about your product via MCP
Retrieval Benchmarks: Wire vs RAG
We tested Wire containers against standard RAG on 64 questions across 13,643 entries from 287 real-world files. Wire delivered 25% better answers from the same token budget.
See the dataThe Science Behind Context Management
Recent papers on agent memory hierarchies, active context management, and declarative context frameworks validate the approach. See what the research says and why it matters.
Read the researchSee the difference for yourself
Upload a few files and let Wire transform them into AI-ready context.
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