ContextStream wasn’t a “market opportunity.” It started as a fix for our own sanity, after 18 years of shipping software and one invisible tax we couldn’t ignore anymore.
You know the feeling. You open your IDE, ready to crush a feature. You fire up your AI assistant, paste a snippet, and ask a question.
“I don’t have context on that function. Can you share the file?”
So you find the file. Then the dependency. Then the config. By the time the AI understands what you’re trying to do, you’ve spent 15 minutes context-switching and lost your flow state.
And it’s worse with newer models. They have “huge context windows,” yet they still hallucinate or forget decisions made three messages ago. They miss the why behind the code.
We started tracking it. 10–15 minutes per session just getting the assistant back up to speed. Every. Single. Session.
Then there’s the team. A new developer joins. “How do I set up the environment?” “Why did we pick PostgreSQL over MySQL?” “Where are the docs?”
The answer is usually “It’s in the Slack history” or “Ask Dave.” But Dave is busy. And the docs are outdated.
We realized we were spending more time managing context — for humans and AIs — than actually building product.
So we built ContextStream: a context engine that captures what your project knows and delivers the right slice of it to whichever agent is working, with the permissions to act on it.
Every choice, correction, and lesson is saved and linked to the code it affects.
Open the repo, and the context is already there.
Not tied to one editor or model. Switch agents, not context.
The core insight is simple: storage is cheap — retrieval is hard. If you dump everything into context, token costs explode and the model gets confused. The only thing that works is delivering the right context at the right time.
“JWT or sessions? Which provider? Which database?”
“Last time we chose JWT with refresh tokens. OAuth provider is X. The auth code lives in … Want me to continue with the refresh rotation + middleware?”
That’s the bar: start where you left off, not at square one. We work on this every day because we’re developers too, and we just want to build without the friction.
Intelligence without context is unreliable.
Autonomy without context is chaos.
And context without trust becomes surveillance.
AI was supposed to give people time back. Instead we’re at risk of building a future where people spend their days supervising machines: repeating instructions, correcting lost context, approving every small action.
The problem isn’t that agents are unintelligent. It’s that they don’t know enough about the world they’re acting in. They don’t know what the team already decided, why a system was built the way it was, or which lesson was learned the hard way. So the human stays behind the screen, working as the memory for the machine.
We think the goal of AI is not to remove humans from the loop. It’s to move humans to the right part of the loop. People should be exercising judgment, building relationships, and deciding what matters. Agents should carry those decisions forward and do the work that follows.
That takes a trusted context layer: the right context, reaching the right agent, at the right time, with the right permissions. Today that begins with software projects. The destination is every kind of work.
— Erik, Founder
Bigger context windows are not enough. We optimize for relevance and clarity in every response.
Your knowledge should not fragment by editor, model, or agent. Context travels with the work, not with the vendor.
Decisions, docs, lessons, tasks, and code links need to be shared, searchable, and operationally useful.
After 18 years of shipping products, the invisible tax became unbearable: 10–15 minutes every session re-explaining the same stack, decisions, and patterns to AI assistants that had total amnesia.
We built a context layer that captures decisions as you make them, links them to code, and delivers the right context automatically via MCP, to any AI tool.
Decisions, docs, lessons, tasks, and code links, all held in a knowledge graph with intent-aware retrieval. Storage is cheap. Retrieval is the hard part we solved.
ContextStream now supports multi-project teams who need reliability, speed, and shared context across every editor and AI agent they use. The Rust MCP server is open source.
As of September 2026, ContextStream is bootstrapped, with around 1,400 accounts including paying teams and individual developers.
18 years building and shipping software products across early-stage startups and high-growth teams. Started ContextStream after spending one too many sessions re-briefing AI assistants on the same architecture, decisions, and lessons learned. Building the context layer he wished he had.
linkedin.com/in/erik-scott-0a25b68Stop re-briefing your tools. Start compounding what your team knows.