As AI adoption grows, startups are increasingly building systems that enhance human decision-making rather than replace it. These ten companies are leading the way with human-in-the-loop (HITL) workflows—where people guide, verify, or refine what AI produces. The result: better outcomes, higher trust, and more practical automation across industries.

1. Scale AI (USA)
Scale AI is a pioneer in HITL data labeling for AI training. Its platform combines automation with human review to annotate datasets used in self-driving, defense, and enterprise AI systems—ensuring both accuracy and compliance.

2. Writer (USA)
Writer delivers enterprise-grade AI writing tools with human feedback loops built into workflows. Teams can customize tone, approve drafts, and train models on internal style guides—ensuring that content stays consistent and brand-safe.

3. Humanloop (UK)
Humanloop helps developers fine-tune and evaluate LLM apps using real user feedback. It integrates into existing dev stacks and enables teams to observe, review, and retrain models based on user interactions, keeping AI performance aligned with real-world use.

4. Adept AI (USA)
Adept builds agents that can use software like a human—clicking, typing, and navigating tools like spreadsheets or dashboards. Users stay in control, guiding what the agent does and reviewing each step, making it ideal for semi-automated workflows.

5. Kili Technology (France)
Kili offers a data labeling platform with QA workflows and collaborative review. Used in sectors like manufacturing, healthcare, and retail, it enables teams to quickly review and correct AI-labeled data before it’s used in production.

6. Scribe (USA)
Scribe generates how-to guides automatically from user actions—but users can edit and annotate each step before sharing. It's used in customer support, onboarding, and internal documentation, blending automation with expert context.

7. Glean (USA)
Glean helps employees find internal information across company tools using AI. While it suggests answers and documents, it always includes human-curated sources and lets users provide feedback to improve accuracy over time.

8. Hebbia (USA)
Hebbia applies AI to document intelligence—allowing analysts and legal teams to ask complex questions across large datasets. Human reviewers validate AI summaries and query results, making it ideal for compliance-heavy industries.

9. Labelbox (USA)
Labelbox’s HITL system allows users to annotate, audit, and improve ML training data. With built-in collaboration tools and quality scoring, it’s trusted by AI teams needing both scale and oversight in vision and language tasks.

10. Phaidra (USA)
Phaidra builds AI control systems for industrial plants, combining real-time machine learning with operator oversight. Engineers monitor system behavior, validate actions, and intervene when needed—ensuring safety while improving efficiency.

Endnote
These startups show that AI is most effective when paired with human judgment. From legal reviews (Hebbia) and enterprise writing (Writer) to data labeling (Scale AI, Labelbox), human-in-the-loop workflows bring accountability, adaptability, and precision to the AI stack. In 2025, the smartest systems aren't replacing people—they're working alongside them.

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