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RESEARCH · IN THE OPEN

Notes from the workbench.

Benchmarks and architecture decisions we publish as we go. Real evaluations against the hardest analytics benchmarks, not cherry-picked demos.
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LatestJune 2026 · Datost98.6% · LongMemEval recall@5

How Good Is Agent Memory, Really? We Benchmarked Datost Against the Field

Agent-memory benchmark numbers are everywhere and almost none are comparable. We ran Datost Brain on LoCoMo and LongMemEval with fixed, disclosed protocols: best-in-class retrieval (98.6 recall@5), an honest 79.2 on end-to-end QA, and the enforcement layer nobody else benchmarks.

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June 2026

Automated Metric Anomaly Detection With Slack Alerts

Detecting that a metric broke is the easy 20%. Pick a detection method that fits the metric, tune out false positives, then route an alert that carries the root cause to Slack, not just the number that moved.

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June 2026

A Single Source of Truth for Metric Definitions: Stop Dashboard Discrepancies

When MRR is up 8% in one dashboard and 3% in another, the problem isn't the data, it's that the definition lives in three places. Here's how to fix it.

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June 2026

How to Get a Slack Bot That Answers Data Questions From Your Warehouse

A practical guide to standing up a Slack bot that answers warehouse questions in plain English. What good looks like (grounded, clarifies, attaches SQL) versus knowledge-base bots that never touch your data.

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June 2026

Why AI Text-to-SQL Returns Wrong Numbers (And How to Catch It)

Text-to-SQL rarely throws an error when it's wrong. It returns a clean number built on the wrong join, the wrong filter, or a guessed metric definition. Here are the failure modes and how to catch them before the number ships.

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June 2026· 75.2%

Best AI Data Analyst Tools for Data Teams (2026)

A fair, benchmark-grounded roundup of AI data analyst tools for 2026, with evaluation criteria (real-DB accuracy, clarification, proactivity, integrations, governance) and where each tool fits.

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June 2026

How to Calculate Cohort Retention in SQL

A practical, runnable SQL walkthrough for cohort retention: bucket users into signup-month cohorts, count who returns in each month, build the retention matrix, and read the curve.

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June 2026

How to Give an LLM the Business Context to Write Correct SQL

The mechanism that makes text-to-SQL accurate is grounding the model in three things before it writes a query: schema, metric definitions, and business context. Here's what to retrieve and how to structure it.

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June 2026· 75.2%

How Accurate Is Text-to-SQL, Really? Spider, BIRD, and the Enterprise Cliff

An honest, benchmark-driven answer to whether AI text-to-SQL can be trusted. We walk Spider to BIRD to BIRD-Interact, explain the enterprise cliff, and show the head-to-head scores.

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April 2026

We stopped guessing how good Datost was

We ran Datost against BIRD-Interact, the hardest interactive analytics benchmark in the field. The best model in the world scores 33%. Datost scores 75%. Same model underneath.

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In the queueDrafting · Q2 2026
COMING SOON
Scaling semantic joins
COMING SOON
RBAC deep-dive
COMING SOON
Agent evals in prod
Going deeperAcross the rest of the site
Glossary
Analytics, AI, and data in plain English

Hand-written definitions for text-to-SQL, semantic layer, BIRD-Interact, and retention curves, with how Datost handles each.

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Comparisons
Datost vs Hex, Sigma, Mode, and the rest

Side-by-side matrices for every AI data and BI tool buyers evaluate against Datost. No FUD, BIRD-Interact data inside.

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