Resources

Whitepapers & benchmarks

Engineering reports from the team building GoodMem — benchmarks and results on real workloads. Free to read, no sign-up.

Frequently asked questions

Direct answers on what GoodMem stores, how retrieval and access control work, deployment and operations, pricing, and the things it deliberately does not do.

Read the FAQ

Interactive technical brief

The Architecture of Agent Optimization

An interactive map of the agent stack: what each layer contributes to quality and cost, and the measured evidence for engineering it.

Open the full brief

Explore the agent architecture

Layer 03 · Agent componentLLM

PAIR trained a 35B-class MoE that outscored GLM‑5.2 on a controlled BI-agent benchmark. It reached 91% of Opus 4.8’s measured quality in medium-thinking mode at 35× lower serving cost.

View model benchmark
Layer 03 · Agent componentHarness

Zhang et al. report 7.8× more performance variance from harness configuration than model choice. PAIR engineers custom harnesses for GoodMem customers as a service.

Layer 02 · Retrieval modelsRerankers

GoodMem’s Fine-Tuning automatically derives optimized embedding and reranking models from two inputs: memory content and agents’ access patterns.

View Fine-Tuning study
Layer 02 · Retrieval modelsEmbedders

GoodMem’s Pipeline Optimization automatically selects the best combination of embedders and rerankers for each agent’s unique context.

Layer 02GoodMemContext / Memory

Controlled tests cut aggregate token burn 28% and agent steps 23%, with no measurable change in quality. Heavy retrieval tasks saved 30–66%.

View memory study
Layer 01PostgreSQLNeon · AlloyDB

Battle-tested reliability, security, and industry support.

An open data layer with hundreds of deployment paths.

Each colored block links to its evidence in the full brief.

Explore the complete architecture