The AI Context Engineering Platform
Engineer context for RAG & agentic AI — make applications more accurate, efficient, and controllable.
Your AI is Drowning in Context
Context chaos breaks tool‑use, planning loops, and multi‑step agents. It's time to engineer pre‑tool and post‑tool context — not just compress.
- âś—Context windows overflow
- âś—Critical information lost
- âś—Hallucinations increase
- âś—Performance degrades
- âś—Costs spiral out of control
- âś“Optimised for target context window
- âś“Critical elements & tool schemas preserved
- âś“Integrity validated
- âś“Measured efficiency gains
- âś“Predictable, controlled costs across agent loops
Engineer, Don't Compress
Context Engineering is a discipline, not a hack. We provide the professional tools you need.
Step‑aware Densification
Progressive, step‑aware refinement for agent loops to reach target density while preserving meaning
Plan & Tool Integrity
Automated checks for plan steps, tool‑call arguments, and preservation of critical information
Role‑aware Profiling
AI‑powered profiling for Planner / Executor / Critic roles to recommend optimal strategies
POST /api/synthesis
{
"context": "...50,000 tokens of documentation...",
"target_tokens": 4000,
"engineering_purpose": "AgentTool", // RAG | AgentTool | PlannerState | MemorySummary
"resolution_standard": "balanced",
"preservation_rules": ["preserve_code_blocks", "maintain_api_signatures"],
"output_schema": "paragraph",
"engineering_strategy": "adaptive",
"integrity_check": true,
"latency_budget_ms": 2500,
"cost_budget_cents": 12,
"tool_schema": {"name": "search", "args": {"query": "string", "top_k": "number"}},
"webhook_url": "https://example.com/hooks/densify"
}
→ Example response:
{
"job_id": "ctx_abc123",
"status": "completed",
"engineered_context": "...precisely engineered context...",
"original_tokens": 50000,
"engineered_tokens": 4000,
"efficiency_ratio": 12.5,
"engineering_rounds": 3,
"integrity_score": 0.95,
"processing_time_ms": 2400,
"engineering_metadata": {
"technique": "multi_round_densification",
"preserved_entities": 47,
"semantic_coverage": 0.94
}
}
Agent tool packs
Pre‑tool context packs preserve schemas and constraints to help reduce tool errors and retries.
Planner memory
Round‑trip summaries keep plans tight across long loops without losing key constraints.
Critic/Guard
Post‑step integrity checks catch drift before the next action, maintaining consistency across multi‑step agents.
The Context Engineering Manifesto
Context Engineering is a discipline. LLM applications benefit from professional context management, not ad‑hoc compression.
Engineering > Compression. We don't just make context smaller—we make it better, preserving what matters for your specific use case.
Integrity is non-negotiable. Context engineering without validation is just lossy compression. We verify every transformation.
Community over competition. We're building the standards, tools, and knowledge base for this emerging field together.
The future is context-aware. As AI systems grow more sophisticated, context engineering becomes the critical bottleneck and opportunity.
Join the Private Beta
Be part of the founding community defining this new discipline
Built for the Modern AI Stack
Integrate with your favourite frameworks and tools
Drop-in pre‑retrieval densification as a transform on document loaders or retrievers.
// typescript (langchain)
const docs = await loader.load();
const engineered = await fetch('/api/synthesis', {
method: 'POST', headers: {'Content-Type':'application/json'},
body: JSON.stringify({ context: docs.map(d=>d.pageContent).join('\n'), target_tokens: 4000, engineering_purpose: 'RAG', resolution_standard: 'balanced' })
}).then(r=>r.json());
retriever.add(engineered.engineered_context);Shape system prompts and tool schemas with engineered context before runs.
// typescript (fetch + openai)
const { engineered_context } = await fetch('/api/synthesis', { /* ... */}).then(r=>r.json());
const run = await client.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: engineered_context },
{ role: 'user', content: 'Answer using only provided context.' }
]
});Expose context operations via MCP for agent tools; feed engineered context into artifacts.
# python (requests)
import requests
payload = {
'context': open('policy.md').read(),
'target_tokens': 2000,
'engineering_purpose': 'AgentTool',
'integrity_check': True
}
resp = requests.post('https://your.app/api/synthesis', json=payload).json()
ctx = resp['engineered_context']
# pass `ctx` into your Claude tool/artifact
Slot a pre‑RAG Context Engineering node into your ingestion or agent loop.
// http request node → function node
const res = await $http.request({
method: 'POST',
url: 'https://your.app/api/synthesis',
body: { context: $json.text, target_tokens: 3000, engineering_purpose: 'PlannerState' }
});
return { engineered: res.engineered_context };Bring your own stack—use Densify.fit wherever your agents plan, call tools, or retrieve context.