Cut LLM context costs without changing your AI stack
Compressify removes redundant context before it reaches the model, reducing token costs while keeping AI agents focused and reliable.
The missing compression layer for AI agents
As AI apps move from single prompts to retrieval, tools, memory and multi-step agents, context grows fast. Compressify sits in the middle and keeps only the context that matters, reducing waste before it becomes cost.
AI teams are paying for repeated context, not better intelligence
Modern AI workflows resend large amounts of context across retrieval calls, tool use, memory updates and agent steps. Much of that context is repetitive, duplicated, or irrelevant. Some of it is irrelevant. All of it is billable.
And the cost is only one side of the problem. Bloated context can make agents less precise, harder to control and more likely to drift from the original task.
LLM providers charge per token. More context means more revenue for them, even when that context does not improve the result.
Compress context before it reaches the model
Compressify analyzes the context your app is about to send, removes redundancy, compresses repeated information and preserves the semantic signal needed for the model to answer well.
From bloated context to optimized inference
100K tokens / day
Noisy, duplicated, unstructured context sent directly to the LLM.
55%+ compression
Context is cleaned, ranked and reduced before inference.
45K tokens / day
Less context sent to the model. Lower cost. Sharper signal.
Compressify does not replace your LLM provider. It makes every request more efficient before it gets there.
Context is becoming the new infrastructure bottleneck
AI agents are becoming mainstream. Every reasoning step, tool call and memory update increases the amount of context passed through the system. Bigger context windows make this easier to build, but also easier to overspend.
Teams need a compression layer that works across models, providers and agent frameworks.
Multi-step workflows can consume 10-50x more context than simple prompts.
More room does not mean better signal. It often means more irrelevant tokens.
Compression must work with existing security, deployment and data residency requirements.
Built for the economics of production AI
Average token reduction target
Faster than comparable compression models on benchmarked hardware
Designed for lightweight deployment
Architecture changes required to use as middleware
Compressify targets the part of LLM spend that is easiest to attack first: repeated input context. The product creates immediate ROI because customers keep using the same models, same apps and same workflows, just with fewer paid tokens.
Where Compressify creates immediate leverage
Reduce the cost of multi-step workflows where context is repeatedly passed between planning, tools and memory.
Compress retrieved documents before they enter the prompt, keeping the answer grounded without overloading the model.
Optimize long codebase, issue, log and documentation contexts for coding agents.
Add a cost-control layer across teams, models and internal AI applications.
Cloud first. Enterprise-ready. On-premise path.
Compressify is designed as infrastructure, not a prompt hack. Start with a simple API or MCP deployment, then move toward dedicated infrastructure or on-premise environments as usage and security requirements grow.
Fast path to testing in existing workflows.
For teams with high-volume AI traffic.
For regulated environments where raw context must stay inside the customer boundary.
Built by operators and AI infrastructure engineers
The Compressify team combines company-building experience with deep technical work in NLP, compression algorithms and production AI systems.
Piotr Barbachowski
Co-founder & CEO
Built Dendrite into a 50-person AI company with $40M+ cumulative revenue. Now applying the same execution playbook to Compressify.
Aleksander Muszynski
Co-founder & CPO
Led product development for AI agent systems at Dendrite, combining product velocity with hands-on technical understanding.
Mateusz Zadrozny
CTO
Architect of Compressify's compression pipeline, with experience across NLP, ML systems and agent infrastructure.
Artur Walczak
Lead Algorithm Engineer
Mathematical Olympiad laureate and algorithm specialist focused on the core compression and validation engine.
Want to reduce AI context costs?
Book a technical walkthrough and we will show where compression fits into your stack.