Memory
Durable knowledge that persists, structures meaning, and retrieves what matters. Altu tests continuity in conversation; Eltium builds the memory infrastructure beneath it.
About
Hands-on engineering
Standard benchmarks
Real-world use
Trestle3 is an independent AI product studio. Altu is live in alpha; Eltium, Talent, and Team OS are in active development. The work combines hands-on architecture, delivery discipline, and agentic systems built for real use — not demos.
What we work on
Extending what LLM-based systems can sustain.
LLM-based products hit a ceiling — on what they can remember, what they can hold in focus, and what they can sustain at reasonable cost. Trestle3 builds tools that raise that ceiling across memory, focus and accuracy, and economy.
We design for provider independence — portable systems, open-weight options, and no single-vendor dependency.
Focus & accuracy
Scoped context, progressive decomposition, and governed coordination that sharpen focus to improve quality. Team OS applies this to agentic software delivery.
Economy
Token efficiency, deliberate context construction, and cost-bounded design so systems stay useful over long-running work — not just impressive in a demo.
T3 engineering
Built around practical AI systems work.
MAF / Microsoft Agent FrameworkRAG / Retrieval Augmented GenerationOpen-Weight ModelsStructured MemoryMCP ToolingScalabilityToken ConservationProvider Independence
Leadership
Ben Ridler
Founder / Principal Engineer
Ben brings 20+ years of software engineering and technology leadership across finance, insurance, and complex delivery environments. His work combines hands-on architecture, engineering discipline, product judgment, and current AI-native implementation.
At T3, that background is applied to conversational AI products, governed agentic delivery, model and tooling evaluation, and practical systems that move from research question to deployed alpha.
MAF / Microsoft Agent Framework
Microsoft's Agent Framework introduces a clean, declarative model for orchestrating agents and tools. We use MAF-style orchestration to build reliable, inspectable workflows that scale with complexity.
RAG / Retrieval Augmented Generation
Our RAG process goes beyond vector searches, adding structured retrieval to provide lean and accurate context for agentic workflows.
Open-Weight Models
We work with open-weight models to avoid vendor lock-in, control inference cost, and keep systems portable across providers and deployment environments.
Structured Memory
Our memory systems are informed by benchmark evaluation and research across human-memory literature. This defines what AI should remember, how it should adapt, and how it should behave over time.
MCP Tooling
We follow MCP standards to build modular, composable tools that integrate cleanly into agent workflows. This creates predictable execution paths and clear boundaries between model, memory, and action.
Scalability
We design systems that scale horizontally and operationally - from local development to containerized deployment - without sacrificing clarity, performance, or cost control.
Token Conservation
We build for efficiency. Our workflows minimize token usage through structured retrieval, targeted context construction, and deliberate orchestration, enabling long-running interactions without runaway cost.
Provider Independence
We design for portability across models, providers, and environments — so product direction is not held hostage to a single vendor's roadmap, pricing, or availability.