Available for Q2 2026 engagements

The context your AI needs to actually perform.

Senior AI engineering consulting for teams building with LLMs. I design the context architectures that turn unreliable AI into production-grade systems.

Trusted by engineering teams at

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What I do

Context engineering services for production AI systems.

Every engagement is tailored to your stack, your data, and your specific challenges. Here is how I typically help teams.

Core
Context Architecture

Design end-to-end context pipelines that feed your models the right information at the right time. From retrieval strategies to context window optimization.

Retrieval
RAG System Design

Build retrieval-augmented generation systems that go beyond naive vector search. Multi-stage retrieval, re-ranking, and hybrid approaches.

Prompts
Prompt Engineering

Systematic prompt design and evaluation frameworks. Move from ad-hoc prompts to versioned, tested, production-grade prompt systems.

Agents
Agent Systems

Design and implement multi-agent architectures with proper tool use, memory management, and orchestration for complex workflows.

Data
Knowledge Management

Structure and curate your organizational knowledge for optimal AI consumption. Chunking strategies, metadata design, and indexing pipelines.

Ops
Evaluation & Monitoring

Build evaluation harnesses and observability for your AI systems. Measure context quality, track regressions, and iterate with confidence.

Writing

Latest thinking on context engineering.

NewTOON ProtocolAI AgentsContext Engineering

Implementing TOON for AI-Driven REST APIs

The shift from JSON to the TOON protocol is not just a format change — it is a fundamental optimization for how AI agents consume, reason about, and act on structured data. This post walks through the practical implementation, from generic middleware to tangible token savings.

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What this article covers

01

From JSON to TOON

The shift from JSON to the TOON protocol for REST APIs, and why it matters for the next generation of AI-driven systems.

02

Token Efficiency

How TOON directly reduces token usage compared to JSON, especially in flat data structures with nested variants.

03

Context Engineering

Positioning context engineering as the critical discipline for AI systems, and how TOON prevents context rot at scale.

04

Generic Middleware

A practical look at the middleware that converts to TOON when the text/tune header is present, enabling organization-wide adoption.

05

Side-by-Side Comparison

A clear visual comparison of JSON vs TOON formats using a flat product structure with nested variants, demonstrating tangible token reduction.

Feb 25, 202614 min read
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About

Deep expertise, focused execution.

I spent over a decade building AI systems at companies ranging from early-stage startups to Fortune 500 enterprises. I have seen what works and, more importantly, what breaks when AI meets production reality.

Context engineering is the discipline of ensuring your AI systems have the right information, in the right format, at the right time. It is the difference between an AI that demos well and one that ships.

12+Years in AI/ML
40+Enterprise Clients
99.7%System Uptime
3xAvg. Accuracy Lift