Turn scattered knowledge into AI-ready context.
Build more accurate, consistent software with Context Engineering.
Context Engineering is the practice of extracting only the context needed right now and delivering it to AI.
As LLMs become more capable, the real value comes from designing the context they work with.
The more information is crammed in, the more unstable AI output becomes. Passing accurate context is the key to improving accuracy.
Information volume directly links to token costs. Accurate context design keeps AI operational costs down while maintaining quality.
Instead of relying on personal prompts, teams can use organized context to get accurate AI results more consistently.
Tacit knowledge remains scattered across individuals instead of spreading through the organization. Dependency grows, and AI adoption slows down.
When you feed AI too much at once, the output gets bloated and takes longer to sort through. Even with AI, the work does not get any faster.
Without enough experience or clear review criteria, people tend to use AI output as-is. Quality becomes inconsistent and harder to control.
When individual struggles go unresolved, the whole organization pays the price.
Specs, ADRs, Slack threads, and PRs are disconnected
Outdated information stays in circulation when it is not maintained
Knowledge gets trapped in teams or individual workflows
Without internal context, AI returns generic answers
AI stalls because it is not useful in day-to-day work
Phennec breaks this chain.
AI feels very different when it has the right organizational context.
Here is how the same troubleshooting question changes with and without Phennec.
Phennec is model- and tool-neutral, so it can be introduced into any development environment.
It connects multiple data sources and delivers the right context to each AI agent.
Passing multiple data sources directly to AI often leads to context bloat and contamination. Phennec sits between your data and AI, delivering only the context needed and turning scattered knowledge into practical skills your team can use.
Syncs data from internal tools in real time to build a reliable source of information AI can reference.
Extracts only the information needed for the task by understanding meaning, then delivers high-quality context to AI.
Safely connects internal knowledge to existing AI tools like Cursor and Claude.
Turns organizational know-how into a form AI can put to use and distributes it across the team, so everyone can work from the latest version.
Works across LLMs, tools, and clouds, so you can switch to the latest models at any time.
Keep using the tools you already have, including Cursor, Claude, and Devin.
Can be tailored to your security requirements and internal policies, reducing friction during rollout.
By separating what people handle from what Phennec and AI handle at each layer, teams can scale AI-driven development across the organization.
Use your existing toolchain while centralizing updates and maintaining reliable version control.
Choose the solution that matches your company's AI adoption challenges.
Using the context organized by Phennec, we support the full workflow from UX design to prototyping and production.
View ServiceWe help you deploy Phennec internally as context infrastructure, creating an environment where AI agents can perform at their best.
View ServiceWith Phennec's measurement and governance in place, we support your team until AI-driven development is embedded across the organization.
View ServiceStart by seeing how Phennec changes AI-driven development using your company's own data.
You can try everything for free, from reviewing the demo environment to validating Phennec with your own data in a bootcamp. Bootcamps are limited to 4 companies per month.
Cursor and Copilot are tools for writing code with AI. Phennec is infrastructure that controls what context gets passed to those tools. It works alongside them rather than competing with them, improving the accuracy of their output.
RAG searches for documents related to a question and uses them to generate an answer. Phennec can use RAG, but its core focus is different: it structures and delivers only the context needed for the current task. RAG focuses on search accuracy; Phennec optimizes the full context pipeline.
No. Phennec is designed independently from any specific LLM, so it supports Claude, GPT, Gemini, local LLMs, and other models. You can switch models freely without vendor lock-in.
Yes. Phennec has been introduced in high-security environments, including financial institutions, and includes access control, audit logs, and guardrails by default. We can design the setup around your company's security policy.
