Perle AI, the human-in-the-loop platform for RLHF, evaluation, and expert data annotation, has raised $9 million in Seed funding led by Framework Ventures to accelerate its mission of delivering high-quality, human-verified training data for LLMs and generative AI systems. The round will also support the launch of Perle Labs, a contributor program geared toward improving AI data quality and reducing bias. The company is led by founder and CEO Ahmed Rashad.

Why this matters: AI quality is a data problem

Most model teams now agree: the biggest lift in model performance comes from better data, clearer rubrics, and trustworthy evaluation, not just bigger architectures. Perle sits squarely in that gap with an expert-in-the-loop approach that blends domain specialists with rigorous workflows for labeling, RLHF, alignment and safety reviews, bias audits, drift detection, and compliance - the blocking and tackling that turns raw datasets into production-ready training signals.

What Perle AI actually provides

Perle’s platform is designed to be modular and future-proof, so teams can bring their own models, policies, and taxonomies while Perle orchestrates the heavy lifting: rubric design, task routing to vetted experts, multi-pass consensus, evaluation harnesses, and continuous quality monitoring. The result is high-fidelity datasets, reproducible feedback, and auditable processes that raise model accuracy without sacrificing safety or governance. For regulated or high-stakes use cases - healthcare, finance, legal, safety - Perle offers specialized domain experts to ensure annotations and judgments meet industry standards.

And here’s the founder-level insight embedded in Perle’s strategy: trust is the real currency of AI infrastructure. Tooling that measurably reduces ambiguity, variance, and review time becomes part of a team’s operating fabric. The defensibility isn’t just throughput - it’s judgment quality encoded as repeatable workflows. When human expertise is systematized into clear rubrics and closed-loop evaluation, labeling stops being a cost center and becomes a performance lever. That’s why an expert-in-the-loop platform can outlast point solutions: it compounds accuracy with every project, making subsequent models cheaper to train and easier to govern.

Perle Labs: aligning incentives with quality

With the new funding, Perle is launching Perle Labs, a program aimed at rewarding contributors of high-quality data and broadening the supply of trustworthy human feedback. By pairing incentives with robust QA and bias-reduction workflows, Perle Labs seeks to scale RLHF and evaluation signals without diluting quality - crucial as enterprises move from pilots to production.

Who benefits right now

• AI platform teams that need reliable pipelines for continuous fine-tuning and post-deployment evaluation
  
• Regulated industries requiring auditable training data and verifiable reviewer expertise
  
• Product teams chasing faster iteration cycles, where cleaner labels + clearer rubrics = shorter model debug loops
  
• Risk & compliance stakeholders who need policy-aware datasets and documented decisions for internal/external audits
  

How Perle reduces total cost of model ownership

Perle’s focus on data management and evaluation helps teams lower the total cost of model ownership (TCMO) across three fronts:

1. Acquisition - Better task design and expert routing cut waste and rework.
   
2. Alignment - RLHF and domain-specific safety reviews improve acceptance rates and reduce escalations.
   
3. Assurance - Versioned datasets, QA metrics, and evaluation harnesses make model updates repeatable and safe for compliance reviews.

Market context

As enterprises standardize on multi-model strategies (proprietary, open-source, and fine-tuned internal models), data quality, provenance, and policy enforcement are becoming board-level priorities. Platforms that operationalize human judgment - not just crowd labor - will set the bar for model performance, explainability, and regulatory readiness. Perle’s positioning around expert oversight and modular workflows speaks directly to that shift.

What’s next

Backed by its Seed round, Perle plans to expand its expert networks, deepen evaluation tooling, and scale Perle Labs to meet surging demand for RLHF and domain-specific alignment. Expect continued investment in bias auditing, drift detection, and compliance features - the capabilities that transform data work from “tasks” into governed, measurable processes enterprises can trust.