Marketers have long struggled with brand intelligence that was either prohibitively expensive—costing up to $1.6 million annually—or hopelessly slow, with campaign cycles stretching to 42 weeks. Early attempts to solve this with open-source AI prototypes collapsed under real-world demand, plagued by latency and throttling. By building an always-on, multi-agent system on a managed generative AI platform, this solution delivers role-specific insights in hours instead of days, slashing analytics costs by up to 96% and improving operational efficiency by 97%. The result is a transformative shift: brand intelligence that was once reserved for enterprises with deep pockets is now accessible, immediate, and reliable at scale.
Case Study Source: Amazon Web Services, Inc.
Problem Statement
Marketers needed rapid, contextual brand intelligence across vast, fragmented data, but traditional research was slow and costly, and early AI prototypes suffered from latency, throttling, and unreliable scale for complex workflows.
Goal
Create an always‑on, multi‑agent brand strategist that delivers immediate, role‑specific insights and recommendations, drastically lowering costs and cycle times while scaling reliably on a managed generative AI platform.
Challenges
Traditional brand research was prohibitively expensive—data sources cost $20,000–$400,000 per year, with processing and consulting adding $250,000–$1.2 million annually.
Long campaign cycles of up to 42 weeks meant insights were often outdated by the time decisions were made.
Open‑source LLM prototypes hit limits under growing demand, causing latency, unpredictable uptime, and data throttling.
Complex tasks—such as building a 90‑day, channel‑specific strategy—exposed scaling and coordination gaps across models and tools.
Unifying disparate public data (reviews, social, market research, industry reports) into consistent, role‑relevant outputs was challenging.
Actions
Built a data acquisition pipeline combining public marketing and competitive intelligence sources to form a unified foundation.
Developed an always‑on, multi‑agent framework on a managed generative AI service to orchestrate specialised agents and tools.
Revamped prompt engineering and introduced role‑specific, pre‑seeded prompts to produce tailored outputs for different marketing roles.
Ran multiple foundation models in parallel to accelerate processing, cutting time from 5–6 days to hours.
Used a hybrid‑reasoning model for on‑demand orchestration and applied other models for large‑scale data annotation.
Scaled agents to handle heavy token volumes and complex queries, enabling rapid benchmarking, perception analysis, and content generation.
Key Results
Impact
Brand intelligence is now accessible to organisations that could not afford analogue methods, making data central to decision‑making.
Marketers can query in natural language and receive role‑aligned guidance, increasing confidence and stakeholder alignment.
A multi‑year product vision was delivered rapidly—achieving a 5‑year roadmap milestone in 6 months—while laying the groundwork for proactive insights and next‑best‑action recommendations.
The Challenge
Marketing teams were drowning in scattered data. They needed quick answers about their brands, but legacy research methods couldn’t keep up. Annual fees for data alone ranged from $20,000 to $400,000, whilst analysis and consultancy pushed total costs to somewhere between $250,000 and $1.2 million each year.
The real problem wasn’t just the price tag. Campaign planning stretched across 42 weeks, meaning insights arrived too late to be useful. By the time decisions were made, the market had already moved on.
Early attempts with open-source language models showed promise but quickly buckled under pressure. Systems became sluggish, went offline unexpectedly, and couldn’t handle the volume. Building something as straightforward as a 90-day campaign plan across multiple channels exposed serious gaps in how these tools worked together. Pulling in reviews, social posts, research papers, and industry reports, then turning that mess into something useful for different team members, proved remarkably difficult.
The Solution
The team started by creating a pipeline that gathered public marketing and competitive data into one place. On top of that foundation, they built a system of specialised AI agents running continuously on a managed platform. Each agent had a specific job, and they worked in concert rather than isolation.
Prompts were rewritten from scratch. Instead of generic queries, the system now used role-specific templates pre-loaded with context. A brand manager received different outputs than a content creator, even when asking about the same topic.
Running several AI models side by side cut processing time dramatically—tasks that took 5 to 6 days now finished in hours. One model handled on-demand coordination whilst others tagged and organised massive datasets. The architecture could cope with heavy workloads and complex questions, enabling rapid comparisons, audience perception analysis, and content creation at scale.
The Results
Efficiency jumped by 97%. Work that previously consumed five days now took roughly two hours. Analytics spending dropped between 66% and 96%, with data subscription fees eliminated entirely since the platform includes them. Marketing investments paid back up to four times faster than before. The provider itself reduced operating costs by 21%.
The system processed approximately 1.2 billion tokens in a single month and answered more than 10,000 marketing questions. What had been planned as a five-year development roadmap was delivered in just six months.
What It Means
Brand intelligence is no longer exclusive to organisations with deep pockets. Companies that couldn’t justify the old costs can now put data at the heart of their decisions. Marketing teams type questions in plain English and receive guidance tailored to their role, which builds confidence and gets everyone aligned faster.
The speed of delivery also changed how the product itself evolved. Reaching a milestone intended for 2028 before the end of 2023 freed the team to focus on proactive recommendations and smarter automation—features that help marketers see what’s coming rather than simply react to what’s already happened.
Case Study Source: Amazon Web Services, Inc.
These industry AI case studies featured on our site are based on publicly available sources and are presented for informational and educational purposes only; we do not claim ownership of these case studies or affiliation with the companies mentioned, and attribution is provided where applicable.
