Engineering Notes & Domain Intelligence

Shaping domain
intelligence,
one signal at a time.

What we ship, what we learn, and what it signals for aviation, freight, manufacturing, pharma, finance, and government.

( Signals · Agentic AI Engineering )

Blog & Newsroom.

Engineering notes, announcements, and domain intelligence, written by the team that builds and operates the platforms.

Serving those who
need to stay ahead.

We don't pitch slide decks. We show you what we've already built in your domain, then engineer what your mission requires.

ZOROSTIntelligence

Shaping domainintelligence,one signal at a time.

Blog & Newsroom.

Cost and latency engineering for LLM systems

Agent failure taxonomy: ten classes, with recovery for each

Agent evaluation harness: grade the run, not the reply

Structured output: getting data instead of prose from a model

Tool design: typed contracts an agent can actually use

Agent memory: what to persist, what to forget, and where

Ontology Governance, the Semantic Layer AI Actually Needs

Context engineering: treat the window as a budget you spend

Prompt looping: designing a loop that actually closes

The agent harness, not the model, decides if your agent works

Human-in-the-Loop That Doesn’t Kill Velocity

Local and Air-Gapped LLMs, What Actually Works Offline

GraphRAG and Knowledge Graphs, When the Extra Structure Pays

Long-Horizon Agents, Memory, Compaction, Context Discipline

Platform Teams for AI, One Paved Road Instead of Ten Stacks

The Deterministic Shell, Code Computes and Models Narrate

Serving those whoneed to stay ahead.

Applications

Company

Shaping domainintelligence,one signal at a time.

Blog & Newsroom.

Cost and latency engineering for LLM systems

Agent failure taxonomy: ten classes, with recovery for each

Agent evaluation harness: grade the run, not the reply

Structured output: getting data instead of prose from a model

Tool design: typed contracts an agent can actually use

Agent memory: what to persist, what to forget, and where

Ontology Governance, the Semantic Layer AI Actually Needs

Context engineering: treat the window as a budget you spend

Prompt looping: designing a loop that actually closes

The agent harness, not the model, decides if your agent works

Human-in-the-Loop That Doesn’t Kill Velocity

Local and Air-Gapped LLMs, What Actually Works Offline

GraphRAG and Knowledge Graphs, When the Extra Structure Pays

Long-Horizon Agents, Memory, Compaction, Context Discipline

Platform Teams for AI, One Paved Road Instead of Ten Stacks

The Deterministic Shell, Code Computes and Models Narrate

Serving those whoneed to stay ahead.

Applications

Company

Shaping domain
intelligence,
one signal at a time.

Serving those who
need to stay ahead.

Shaping domain
intelligence,
one signal at a time.

Serving those who
need to stay ahead.