aiAble
aiAble provides a model lifecycle platform for enterprise advertising teams, fine-tuning open-source LLMs on private brand policies, marketplace taxonomies, and locale-specific rules. The platform includes a custom inference stack with aggressive caching and batching, achieving 118ms P95 latency and reducing token spend by up to 42% compared to baseline prompting.
- Artificial Intelligence
- Advertising Technology
- Software Only
- Enterprise Software
Funding
Founders
Founder details are not available yet.
Product
Problem
Enterprise advertising teams struggle to apply general-purpose large language models to their specific operational needs, including private brand policies, marketplace taxonomies, and multilingual or locale-specific constraints. Generic prompting often yields inconsistent policy adherence, high token consumption, and latency that is too slow for real-time ad workflows, forcing teams to invest heavily in post-editing and manual moderation.
Solution
aiAble delivers a model lifecycle built around fine-tuned, open-source LLMs that are adapted to each organization's domain-specific rules, taxonomy, and workflow history. The platform provides a custom inference and serving stack optimized for ads traffic patterns, using aggressive caching, batching, and routing to achieve predictable low-latency throughput. It also includes continuous evaluation and drift monitoring, with automated regression checks and alerts that track policy adherence, category accuracy, and brand alignment over time. This approach enables ads teams to enforce internal rulesets, classify content according to marketplace schemas, and generate compliant creative output with fewer tokens and less manual oversight.
Target Audience
Primary customers are ads and commerce teams at enterprises, including moderation, creative generation, and ad intelligence groups that need policy-safe, taxonomy-accurate LLM outputs at scale.
Features
- Domain and organization-specific fine-tuning of open-source LLMs using private data, including brand policies, marketplace taxonomies, and workflow history
- Custom inference serving stack with aggressive caching, batching, and routing for low-latency throughput, demonstrated at 118ms P95 latency
- Token-efficient generation that reduces token spend by up to 42% compared to baseline prompting
- Continuous evaluation and drift monitoring with automated regression checks and alerts for policy adherence, category accuracy, and brand alignment
- Pre-optimized for moderation and policy compliance, as well as category and tagging tasks using internal taxonomy and marketplace schema
- Multilingual and locale-constraint support, including Arabic locale-specific rules and seasonal campaign guidelines