CLICKFORCE is one in all leaders in digital promoting providers in Taiwan, specializing in data-driven promoting and conversion (D4A – Knowledge for Promoting & Motion). With a mission to ship industry-leading, trend-aligned, and revolutionary advertising options, CLICKFORCE helps manufacturers, companies, and media companions make smarter promoting selections.
Nonetheless, because the promoting {industry} quickly evolves, conventional evaluation strategies and generic AI outputs are not ample to offer actionable insights. To stay aggressive, CLICKFORCE turned to AWS to construct Lumos, a next-generation AI-driven advertising evaluation answer powered by Amazon Bedrock, Amazon SageMaker AI, Amazon OpenSearch, and AWS Glue.
On this put up, we show how CLICKFORCE used AWS providers to construct Lumos and rework promoting {industry} evaluation from weeks-long handbook work into an automatic, one-hour course of.
Digital promoting challenges
Earlier than adopting Amazon Bedrock, CLICKFORCE confronted a number of roadblocks in constructing actionable intelligence for digital promoting. Giant language fashions (LLMs) have a tendency to supply generic suggestions reasonably than actionable industry-specific intelligence. With out an understanding of the promoting setting, these fashions didn’t have the {industry} context wanted to align their options with precise {industry} realities.
One other important problem was the absence of built-in inner datasets, which weakened the reliability of outputs and elevated the danger of hallucinated or inaccurate insights. On the identical time, advertising groups relied on disconnected instruments and approach akin to vibe coding, with out standardized architectures or workflows, making the processes troublesome to take care of and scale.
Making ready a complete {industry} evaluation report was additionally a time-consuming course of, sometimes requiring between two and 6 weeks. The timeline stemmed from a number of labor-intensive phases: one to a few days to outline goals and set the analysis plan, one to 4 weeks to assemble and validate knowledge from completely different sources, one to 2 weeks to conduct statistical evaluation and construct charts, one to 2 to extract strategic insights, and eventually three to seven days to draft and finalize the report. Every stage typically required back-and-forth coordination throughout groups, which additional prolonged the timeline. Because of this, advertising methods have been ceaselessly delayed and primarily based extra on instinct than well timed, data-backed insights.
Options overview
To handle these challenges, CLICKFORCE constructed Lumos, an built-in AI-powered {industry} evaluation service, utilizing AWS providers.
The answer is designed round Amazon Bedrock Brokers for contextualized reasoning and Amazon SageMaker AI for fine-tuning Textual content-to-SQL accuracy. CLICKFORCE selected Amazon Bedrock as a result of it offers managed entry to basis fashions with out the necessity to construct or keep infrastructure, whereas additionally providing brokers that may orchestrate multi-step duties and combine with enterprise knowledge sources by means of Information Bases. This allowed the workforce to floor insights in actual, verifiable knowledge, decrease hallucinations, and shortly experiment with completely different fashions, whereas additionally lowering operational overhead and accelerating time-to-market.
Step one was to construct a unified AI agent utilizing Amazon Bedrock. Finish-users work together with a chatbot interface that runs on Amazon ECS, developed with Streamlit and fronted by an Utility Load Balancer. When a person submits a question, it’s routed to an AWS Lambda perform that invokes an Amazon Bedrock Agent. The agent retrieves related info from a Amazon Bedrock Information Bases, which is constructed from supply paperwork—akin to marketing campaign stories, product descriptions, and {industry} evaluation information—hosted in Amazon S3. These paperwork are mechanically transformed into vector embeddings and listed in Amazon OpenSearch Service. By grounding mannequin responses on this curated doc set, CLICKFORCE made positive that outputs have been contextualized, decreased hallucinations, and aligned with real-world promoting knowledge.
Subsequent, CLICKFORCE made the workflows extra action-oriented by utilizing Textual content-to-SQL requests. When queries required knowledge retrieval, the Bedrock Agent generated JSON schemas by way of the Agent Actions API Schema. These have been handed to Lambda Executor capabilities that translated requests into Textual content-to-SQL queries. With AWS Glue crawlers constantly updating SQL databases from CSV information in Amazon S3, analysts have been capable of run exact queries on marketing campaign efficiency, viewers behaviors, and aggressive benchmarks.
Lastly, the corporate improved accuracy by incorporating Amazon SageMaker and MLflow into the event workflow. Initially, CLICKFORCE relied on basis fashions for Textual content-to-SQL translation however discovered them to be rigid and infrequently inaccurate. Through the use of SageMaker, the workforce processed knowledge, evaluated completely different approaches, and tuned the general Textual content-to-SQL pipeline. As soon as validated, the optimized pipeline was deployed by means of AWS Lambda capabilities and built-in again into the agent, ensuring that enhancements flowed straight into the Lumos software. With MLflow offering experiment monitoring and analysis, the cycle of information processing, pipeline tuning, and deployment turned streamlined, permitting Lumos to attain increased precision in question era and ship automated, data-driven advertising stories.
Outcomes
The impression of adopting Amazon Bedrock Brokers and SageMaker AI has been transformative for CLICKFORCE. Trade evaluation that beforehand required two to 6 weeks can now be accomplished in below one hour, dramatically accelerating decision-making. The corporate additionally decreased its reliance on third-party {industry} analysis stories, which resulted in a 47 p.c discount in operational prices.
Along with time and value financial savings, the Lumos system has prolonged scalability throughout roles throughout the advertising setting. Model homeowners, companies, analysts, entrepreneurs, and media companions can now independently generate insights with out ready for centralized analyst groups. This autonomy has led to better agility throughout campaigns. Furthermore, by grounding outputs in each inner datasets and industry-specific context, Lumos considerably decreased the danger of hallucinations and made positive that insights aligned extra carefully with {industry} realities.
Customers can generate {industry} evaluation stories by means of pure language conversations and iteratively refine the content material by persevering with the dialogue.
These visible stories, generated by means of the Lumos system powered by Amazon Bedrock Brokers and SageMaker AI, showcase the platform’s capacity to supply complete market intelligence inside minutes. The charts illustrate model gross sales distribution, retail and e-commerce efficiency, and demonstrating how AI-driven analytics automate knowledge aggregation, visualization, and perception era with excessive precision and effectivity.
Conclusion
CLICKFORCE’s Lumos system represents a breakthrough in how digital advertising selections are made. By combining Amazon Bedrock Brokers, Amazon SageMaker AI, Amazon OpenSearch Service, and AWS Glue, CLICKFORCE reworked its {industry} evaluation workflow from a sluggish, handbook course of into a quick, automated, and dependable system. On this put up, we demonstrated how CLICKFORCE used these AWS providers to construct Lumos and rework promoting {industry} evaluation from weeks-long handbook work into an automatic, one-hour course of.
In regards to the Authors
Ray Wang is a Senior Options Architect at AWS. With 12+ years of expertise within the backend and guide, Ray is devoted to constructing trendy options within the cloud, particularly in particularly in NoSQL, massive knowledge, machine studying, and Generative AI. As a hungry go-getter, he handed all 12 AWS certificates to extend the breadth and depth of his technical information. He likes to learn and watch sci-fi motion pictures in his spare time.
Shanna Chang is a Options Architect at AWS. She focuses on observability in trendy architectures and cloud-native monitoring options. Earlier than becoming a member of AWS, she was a software program engineer.
