Scaling a QA department for an AI document intelligence platform
We partnered with a top AI company to quickly grow a QA team from 5 to 30 in two months, improving their RLHF product's accuracy.
About the company
Our client is a fast-growing AI company building a document ingestion platform that extracts critical information from unstructured data and delivers actionable insights to end users.
Their flagship product leverages Reinforcement Learning from Human Feedback (RLHF) to continuously improve accuracy and relevance — making the quality of human evaluation central to the product's success and competitive positioning.
Client
Industry
Location
Roles Filled
: AI Solutions Company
: Artificial Intelligence
: United States
: 30
The challenge
- The company's RHF strategy required a robust, structured QA function - but no such department existed, creating a critical gap in their ability to improve model accuracy at scale.
- Explosive product growth meant the team needed to scale its QA capacity rapidly, without compromising on the quality or rigor of evaluations.
- There were no established evaluation frameworks, data validation protocols, or structured review workflows in place to support the RLHF pipeline.
- Hiring, onboarding, and operationalizing a large QA team in a highly specialized Al domain required a partner with deep recruiting expertise and speed.
The gap to fill
No dedicated QA department
RLHF accuracy at risk
Explosive growth with no QA infrastructure
No evaluation frameworks or workflows
Urgent need to scale - fast
The solution
Motum rapidly assembled and embedded a high-performing QA team, building the department's infrastructure — frameworks, workflows, and feedback loops - from scratch while scaling headcount at an unprecedented pace.
Scaled the QA department from 5 to 30 professionals in just two months, fully vetted and aligned to the client's Al domain
Established rigorous quality standards, evaluation criteria, and scalable review processes tailored to RHF requirements
Implemented data validation protocols, structured review pipelines, and continuous feedback loops to enhance model performance
Directly supported the client's reinforcement learning strategy by ensuring consistent, high-quality human feedback at scale
Matched QA analysts and specialists to the specific accuracy and domain requirements of the document intelligence platform
Capabilities deployed
QA Analysis
RLHF Evaluation
Data Validation
Evaluation Frameworks
Structured Workflows
Feedback Loop Design
Document Intelligence
AI Model QA
Process Standardization
Highlights and impact
QA department built from the ground up and scaled to 30 professionals in record time
RLHF pipeline strengthened through rigorous, consistent human feedback atscale
Flagship product accuracy and reliability meaningfully improved
Evaluation frameworks and data validation protocols established as lasting infrastructure
Company kept pace with explosive growth without sacrificing QA rigor
Scalable QA foundation positioned the client as a market-leading Al solution
See what we’ve built together
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