AI-Powered Marketing Intelligence Platform Accelerates Industry Analysis

clickforce · Media & Entertainment · 2026 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Marketing analytics and advertising intelligence

CLICKFORCE needed to turn slow, manual industry-analysis work into a reliable self-service marketing intelligence system that could use proprietary advertising data without producing generic or hallucinated recommendations.

Constraints:

• reports previously required weeks of manual research, data validation, analysis, and drafting
• generic LLM output lacked industry-specific advertising context
• internal datasets were disconnected, weakening reliability
• Text-to-SQL accuracy mattered because business users needed precise campaign and benchmark queries
• multiple stakeholder groups needed independent access without waiting for central analysts

Architecture

CLICKFORCE built Lumos around Amazon Bedrock Agents. Users query a Streamlit chatbot running on ECS behind an Application Load Balancer. Queries go through Lambda to a Bedrock Agent, which retrieves context from Bedrock Knowledge Bases over S3 documents embedded and indexed in OpenSearch. For structured-data questions, the agent emits JSON schemas through the Agent Actions API Schema, Lambda executors translate requests into Text-to-SQL, and Glue keeps SQL databases current from S3 CSV data. SageMaker and MLflow tune and evaluate the Text-to-SQL pipeline before deploying optimized Lambda functions back into the agent.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  streamlit-chatbot["streamlit-chatbot"] --> application-load-balancer["application-load-balancer"]
  application-load-balancer --> ecs-application["ecs-application"]
  ecs-application --> lambda-invoker["lambda-invoker"]
  lambda-invoker --> bedrock-agent["bedrock-agent"]
  bedrock-agent --> bedrock-knowledge-base["bedrock-knowledge-base"]
  bedrock-knowledge-base --> s3-document-corpus["s3-document-corpus"]
  s3-document-corpus --> opensearch-index["opensearch-index"]
  bedrock-agent --> agent-actions-schema["agent-actions-schema"]
  agent-actions-schema --> text-to-sql-executors["text-to-sql-executors"]
  s3-document-corpus --> glue-crawlers["glue-crawlers"]
  glue-crawlers --> glue-sql-databases["glue-sql-databases"]
  text-to-sql-executors --> glue-sql-databases
  sagemaker-text-to-sql["sagemaker-text-to-sql"] --> text-to-sql-executors

Components

Data flow

A user submits a marketing question through the Streamlit chatbot. The query is routed through Lambda to a Bedrock Agent. The agent grounds answers in Bedrock Knowledge Bases built from S3-hosted campaign reports, product descriptions, and industry analysis files embedded in OpenSearch. If the query needs structured data, the agent generates a JSON schema through the Agent Actions API Schema, Lambda Executor functions translate it into Text-to-SQL, and Glue-maintained SQL databases return campaign, audience, and competitive data. SageMaker-tuned Text-to-SQL improvements are deployed through Lambda into Lumos.

Why this architecture

CLICKFORCE combined an agent, retrieval, and Text-to-SQL because the business problem had both unstructured marketing context and structured campaign data. Bedrock Agents provided managed orchestration without the team building model infrastructure. Knowledge Bases and OpenSearch grounded answers in curated documents to reduce hallucinations. Text-to-SQL actions let the same assistant answer data-backed performance questions. SageMaker and MLflow were added because foundation models alone were too inaccurate and inflexible for query translation.

Models used: Amazon Bedrock Agents · Amazon Bedrock Knowledge Bases · Amazon SageMaker AI · Text-to-SQL model · foundation models

Scale

• Industry analysis moved from two to six weeks to under one hour.
  “Industry analysis that previously required two to six weeks can now be completed in under one hour, dramatically accelerating decision-making.”article, stated
• The new workflow reduced reliance on third-party research enough to cut operational costs by 47 percent.
  “The company also reduced its reliance on third-party industry research reports, which resulted in a 47 percent reduction in operational costs.”article, stated
• The previous reporting process included one to four weeks just to gather and validate data.
  “one to four weeks to gather and validate data from different sources, one to two weeks to conduct statistical analysis and build charts”article, stated
• The system expanded self-service access across brand owners, agencies, analysts, marketers, and media partners.
  “Brand owners, agencies, analysts, marketers, and media partners can now independently generate insights without waiting for centralized analyst teams.”article, stated

Alternatives considered (roads not taken)

Generic foundation-model recommendations

Rejected because: They produced generic output without industry-specific advertising context.

Disconnected tools and vibe-coding-style workflows

Rejected because: They lacked standardized architecture and were difficult to maintain and scale.

Foundation models alone for Text-to-SQL

Rejected because: They were too inflexible and inaccurate, so CLICKFORCE tuned the pipeline with SageMaker and MLflow.

Centralized analyst-only report production

Rejected because: Self-service access was needed so stakeholders could generate insights without waiting for analyst teams.

Patterns

multi-agent-orchestration (orchestration)

The Bedrock Agent orchestrates retrieval and structured data actions in one marketing-analysis assistant.

rag-retrieval (retrieval)

Lumos grounds responses in Bedrock Knowledge Bases built from curated advertising documents stored in S3.

hybrid-search (retrieval)

The system combines vector retrieval over documents with SQL retrieval over structured campaign data.

structured-output-contract (integration)

The agent uses JSON schemas through the Agent Actions API Schema to make data-retrieval requests executable by Lambda functions.

eval-driven-development (evaluation)

SageMaker and MLflow were used to process data, compare approaches, tune the Text-to-SQL pipeline, and deploy validated improvements.

Tradeoffs

Use managed Bedrock Agents as the orchestration layer.

Chose managed agent infrastructure over building and maintaining custom foundation-model infrastructure.

Rationale: The team wanted quick experimentation, enterprise-data integration, and lower operational overhead.

Ground answers in curated documents and data.

Chose Knowledge Bases, vector indexes, and structured SQL access over ungrounded generic model answers.

Rationale: Advertising analysis needed verifiable data and industry context to reduce hallucination.

Tune Text-to-SQL rather than relying on base foundation models.

Chose SageMaker and MLflow tuning over foundation models alone for query translation.

Rationale: Untuned models did not produce reliable enough SQL translation for business analysis.

Gotchas (what bit them)

Generic LLM outputs were not actionable for advertising intelligence.

Root cause: The models lacked the advertising environment and industry context needed for real recommendations.

Resolution: Ground responses in curated internal documents, industry context, and structured business data.

Disconnected datasets increased hallucination risk.

Root cause: Internal data was not integrated, weakening output reliability.

Resolution: Use S3, Knowledge Bases, OpenSearch, Glue, and SQL access to ground outputs in curated and structured datasets.

Foundation models were not sufficient for accurate Text-to-SQL.

Root cause: Untuned models were inflexible and often inaccurate for query translation.

Resolution: Process data and tune the Text-to-SQL pipeline with SageMaker, then deploy validated improvements through Lambda.

Evaluation

CLICKFORCE evaluated and tuned the Text-to-SQL pipeline using SageMaker and MLflow, then deployed validated improvements back into the Lumos agent through Lambda functions.

Metrics: Text-to-SQL precision · query-generation accuracy · industry-analysis time · operational cost reduction

Outcomes

• industry analysis time fell from two to six weeks to under one hour
• operational costs fell by 47 percent through reduced reliance on third-party reports
• brand owners, agencies, analysts, marketers, and media partners gained self-service insight generation
• grounded responses reduced hallucination risk and improved advertising-data alignment

The main quantified outcomes were under-one-hour report generation, 47 percent operational cost reduction, and access across multiple marketing stakeholder groups.