What human-in-the-loop review is
Human-in-the-loop (HITL) review is the quality-control process Databook applies to its highest-value insight datasets. AI and data pipelines generate candidate insights at scale; Databook's Data & Insights analysts then review them in a dedicated internal review application - correcting, approving, or rejecting each item - before anything is published into the product.
The result is a single, reviewed source of truth that is reused consistently across everything Databook produces: account views, AI-generated assets and decks, DatabookAI conversations, and APIs.
How the process works
Databook follows the same governed cycle for each reviewed dataset:
Generate. AI agents and Databook's proprietary data pipelines draft insights at scale, drawing on company filings, financial data, and other trusted sources.
Review. Databook analysts examine each draft in a purpose-built review workspace, editing for accuracy and consistency and approving or rejecting items in managed batches.
Publish. Only approved content is published into the product, where it becomes the consistent source of truth across decks, DatabookAI, account experiences, and APIs.
Refresh. Content is refreshed on a predictable cadence, with older material cleanly retired so what you see stays current.
Datasets covered by HITL review
Dataset | What it provides | Why human review matters |
Industry priorities | Strategic, digital, and sustainability priority themes at industry (~70 industries) and fine-grained sub-industry level (~160 sub-industries). | Ensures themes are accurate and genuinely representative of each industry, not generic or off-target. |
Company strategic priorities | The strategic priorities a specific company has signaled, derived from its filings, investor documents or earnings transcripts. | Confirms priorities are correctly attributed and faithfully reflect the company’s actual strategic priorities. |
Executive compensation insights | Executive compensation metrics and named executives for major US public companies. | Validates named individuals, performance payout percentages and performance KPIs are accurate |
Company peer groups | Relevant competitors selected using Databook's methodology. | Checks that peer sets for high-profile companies are sensible and defensible before they shape benchmarking and positioning. |
Why this matters for you
Trustworthy intelligence. Insights are validated by expert analysts, so your teams can act on them with confidence rather than second-guessing AI output.
Consistency everywhere. The same reviewed insights flow into decks, AI assistants, account views, and APIs - so everyone in your organization works from one aligned source of truth.
Accuracy at scale. Automation lets Databook cover more companies and industries than manual research ever could, while human review keeps quality high.
Fresh and current. A regular, governed refresh cycle replaces one-off manual updates, so the intelligence you rely on stays up to date.
Built for sensitive decisions. Human oversight is especially valuable for high-stakes data such as strategic priorities and executive compensation, where accuracy and accountability are essential.
HITL review reflects Databook's broader commitment to trust, explainability, and enterprise-grade data quality - pairing the scale of AI with human accountability so the insights behind your GTM decisions are ones you can stand behind.