A practical evaluation framework — task success, tool-call accuracy, hallucination rate, and recoverability — for deciding whether an agent is safe near your email and calendar.
Autonomous coding agents run commands you didn't write. Here's how to sandbox them like the untrusted executors they are — instead of trusting model alignment.
A practical framework for tracing exactly what leaves your environment when AI touches your documents, code, and email — before it becomes someone else's training data.
The Sonnet 4.6 guardrail results suggest output validation, not raw model choice, is the highest-leverage lever for reliable AI code.
A measured look at where 4-bit quantization degrades reasoning and retrieval — so you pick precision by task instead of chasing the smallest model.
A routing layer picks the right model for each request — summarization, extraction, or reasoning — so quality and cost stay flexible instead of locked to one provider.
Fluent isn't the same as correct. Here's how to build task-level evals, groundedness checks, and regression testing into everyday AI features.
Agent-readiness isn't about bolting on chatbots. It's about exposing structured tools, clean state, and observable actions across email, docs, and search.
One model can't optimally draft, extract, schedule, and search. Here's a practical routing framework for choosing and switching models per task.
A practical framework for deciding when an autonomous agent actually beats a prompt or a plain feature — and when it's just expensive theater.
A practical selection matrix across reasoning, coding, latency, and cost — plus why routing one model per task beats crowning a single winner.
LoRA and lightweight fine-tuning are cheaper than ever, but for most inbox and document tasks, prompting plus retrieval still wins. Here's the decision tree.
Get started
Claim your address before someone else does — free to start, with an AI-native inbox built in.