context.ai.id — Context Engineering for Frontier LLMs

Core Capabilities

Deterministic context management at silicon speed

Traditional prompt concatenation fails when scaling beyond hundreds of documents. Our engine treats context as an indexed, compile-time memory graph.

Deterministic Token Pruning

Syntactic attention scoring prunes redundant tokens, conversational boilerplate, and irrelevant syntax trees before prompt submission, saving up to 72% of inference costs without losing reasoning precision.

AST-aware pruning

Hierarchical Context Graphs

Transform complex multi-document repositories and long dialogue sessions into structured DAGs. Nodes are selectively expanded into prompt windows on demand using dynamic semantic relevance.

DAG-based indexing

Prefix KV-Cache Synchrony

Automatically guarantees deterministic token ordering across concurrent API calls, maximizing prefix-cache hit rates across Anthropic, OpenAI, and self-hosted vLLM/SGLang clusters.

vLLM • SGLang • Anthropic

Sub-Second RAG Orchestration

Hybrid dense-sparse retrieval coupled with reciprocal rank fusion (RRF) and cross-encoder rerankers, delivering latency-budgeted context payloads in under 12 milliseconds.

Sub-12ms pipeline

Engine Architecture

One unified pipeline for long-horizon context

Integrate context compression and cache-conscious routing directly into your model inference loop.

context_pipeline.py

# Initialize the context engineering runtime
from context_ai import ContextEngine, PrunePolicy, CacheStrategy

engine = ContextEngine(
    cluster_endpoint="grpc://context-infra.internal:9080",
    prune_policy=PrunePolicy.ATTENTION_AWARE,
    target_compression_ratio=0.35,  # Keep 35% most salient tokens
    cache_strategy=CacheStrategy.PREFIX_STABLE
)

# Ingest multi-repository context (2M+ tokens)
pipeline = engine.compile(
    sources=["repo://github.com/org/mono-repo", "s3://docs-corpus/v3"],
    active_scope="packages/billing-core",
    budget_tokens=128_000
)

# Execute deterministic query with KV-cache hit guarantee
payload = pipeline.synthesize(
    query="Trace transactional idempotency across microservices",
    model="claude-3-7-sonnet"
)

print(f"Compiled Context: {payload.token_count} tokens | Cache Hit Rate: {payload.cache_hit_rate:.1%}")
# Output: Compiled Context: 44,800 tokens | Cache Hit Rate: 98.4%

Deterministic AST Splitting

Chunks are strictly partitioned along linguistic and architectural boundaries, ensuring that function scope and variable references remain intact.

Prefix-Cache Alignment

Context segments are sorted by volatility, keeping invariant tokens at the head to maximize provider KV cache re-use across rounds.

Zero Token Leakage

Strict cryptographic isolation between tenant context DAGs. Self-hosted deployments run entirely air-gapped on your private cloud.

Enterprise Workloads

Built for high-dimensional reasoning tasks

Engineered for teams pushing frontier models beyond standard 8k conversational turns.

Codebase Synthesis

500,000+ LOC Monorepos

Feed full codebase contexts to code-generation models without crashing limits or suffering needle-in-the-haystack amnesia during refactors.

Token Reduction 68% Lower Cost

Financial & Legal

Multi-Filing Reconciliation

Cross-reference hundreds of SEC filings, quarterly earnings, and auditor transcripts with deterministic provenance tracking per cited figure.

Fact Precision 99.9% Grounded

Agentic Workflows

Swarm Context Sharing

Allow 20+ specialized agents to read and append to a centralized context graph without repeating shared historical tokens on every sub-call.

Cache Efficiency 94% KV Hit Rate

Engineering the context layer for frontier intelligence.