InnomiumInsights
Evidence for leaders building AI that has to work.
Practical guidance on AI strategy, agents, computer vision, software, data, cloud, GPU systems, evaluation, and the work between pilot and production.
All insights
12 insights matching filters
AI Engineering
Custom AI Development in the United States: Cost, Timeline, and What Changes Both
A practical planning guide for US companies budgeting a custom AI system—from feasibility evidence through integration, production controls, and ownership.
Innomium EngineeringJuly 23, 20267 min readAI Engineering
Human-in-the-Loop AI Agent Design Without Creating a Review Bottleneck
Place human judgment where consequence and ambiguity justify it, while using risk tiers, structured review, and feedback data to keep the workflow usable.
Innomium Product EngineeringJuly 22, 20265 min readAI Engineering
Prompt Injection in Tool-Using Agents: Design for Untrusted Instructions
Why retrieved documents and tool output must be treated as untrusted data, and how policy, isolation, approval, and evaluation reduce agent risk.
Innomium AI EngineeringJuly 22, 20266 min readAI Engineering
AI Agent Observability: What to Trace Before Production
Trace model calls, tool use, policy decisions, state transitions, cost, and outcome quality without turning sensitive prompts into an uncontrolled log archive.
Innomium Platform EngineeringJuly 22, 20266 min readAI Engineering
An LLM Evaluation Framework for Production Decisions
Move beyond demo prompts and average scores with task populations, failure taxonomies, human review, regressions, and explicit release gates.
Innomium Evaluation TeamJuly 22, 20266 min readAI Engineering
RAG vs. Long Context for Enterprise AI: A Decision Framework
Retrieval and long context solve different parts of the evidence problem. Compare them using document boundaries, access control, latency, attribution, and task structure.
Innomium ResearchJuly 22, 20266 min readAI Engineering
Production RAG Architecture: Retrieval Is a Data Product, Not a Prompting Trick
How to design retrieval-augmented generation around permissioned data, measurable retrieval quality, citations, and operational ownership.
Innomium AI EngineeringJuly 22, 20266 min readAI Engineering
AI Agents vs. Workflow Automation: Choose the Least Autonomous System That Works
A decision framework for separating deterministic automation, AI-assisted workflows, and genuinely agentic systems.
Innomium Product EngineeringJuly 22, 20266 min readAI Engineering
Enterprise AI Agent Architecture: The System Around the Model
A production agent is a controlled software system with tools, state, policy, evaluation, and recovery—not an autonomous prompt wrapped in a chat interface.
Innomium AI EngineeringJuly 22, 20266 min readAI Engineering
When Long Context Is the Wrong Tool
A large context window can add latency, cost, and distraction. Use retrieval, structured state, or deterministic software when those approaches better match the task.
Innomium ResearchJuly 14, 20266 min readAI Engineering
Introducing Continuum1-9B: A Public Long-Context Research Artifact
What Continuum1-9B is, what its linear-attention design is intended to explore, how to inspect the release, and which claims still require workload-specific evaluation.
Innomium ResearchMarch 1, 20266 min readAI Engineering
Why Linear Attention for Two-Million-Token Context
The engineering motivation, state tradeoffs, kernel requirements, and evaluation questions behind very long context with linear-compute architectures.
Innomium ResearchFebruary 20, 20266 min read
Want the methods applied to your system?
Talk with Innomium about evaluation, adaptation, or a production engineering program.