Context Engineering Technical Expertise

I design and deploy context systems that scale with your AI stack:

  • Dynamic Context Management: Sliding window logic, real-time input optimization, adaptive sizing
  • Token Compression: Summarization, clustering, entity abstraction, token reduction pipelines
  • Context Prioritization: Relevance scoring, decay models, heuristic & learned selection models
  • Memory Systems: Vector DBs, hybrid memory, structured & unstructured long-term memory
  • Performance Optimization: Load balancing, caching strategies, asynchronous streaming
  • Monitoring & Debugging: Context flow tracking, injection auditing, context token analysis
  • Production Frameworks: Custom context pipelines with API-based context formatting and fallback control

Implementation Examples

  • Sliding Window Engine: Dynamically resize input tokens based on recency, relevance, and topic decay
  • Long-Term Memory Stack: Multi-source memory system with structured storage and semantic retrieval
  • Compression-as-a-Service: Middleware summarizer compressing token-heavy inputs into abstracted embeddings
  • Context Ranking Algorithm: Learn-to-rank model for choosing relevant chat history in real-time
  • Embedded Context Diagnostics: Monitor token usage, failure patterns, and LLM context collapse in production
  • Latency Optimization: Pre-fetching and smart caching for sub-100ms API context injection speeds

Context Engineering Process

  1. Context Analysis
    Understand context volume, access patterns, and performance goals.

  2. Design Phase
    Develop architecture with compression, prioritization, and memory strategies.

  3. Optimization & Deployment
    Tune latency, validate performance, and monitor results in real-time production systems.

Investment & Pricing

Projects priced by technical scope and performance needs:

  • Basic Context Management: $10K–25K
    Window management, summarization, and simple prioritization

  • Advanced Context Platform: $25K–60K
    End-to-end system with memory, scoring, and compression logic

  • Production Context Platform: $60K–120K+
    Multi-LLM orchestration with monitoring, scaling, and latency guarantees

  • Research & Development: $150–250/hr
    For context modeling, novel prioritization logic, or summarization engine development

  • Ongoing Optimization: Monthly support and context tuning available

See Context Engineering in Action

Experience how intelligent context flow can change your AI performance profile. See a live demo featuring real-time memory, prioritization, and LLM adaptability.

Ready to Engineer Smarter Context?

Let’s talk about your context window challenges, token constraints, or memory integration plans.
I help Triangle area companies build AI infrastructure that scales securely, efficiently, and intelligently.