Multi-Agent AI Competitor Intelligence Platform on Databricks
Challenges
They manually gathered information that was difficult to validate, reproduce, or update. Teams struggled to separate genuine AI adoption from marketing claims, while insights remained locked in static reports. This resulted in slow, inconsistent benchmarking and strategic decisions based on outdated or incomplete intelligence.
Outcome
Benchmarking cycle time reduced from weeks to hours and research efficiency increased by over 90%.
Solution
Workflow Automation (MCP-enabled)
Challenges
Solution
Technology Stack
Outcomes
The global apparel & food retail enterprise eagerly wanted to get a clear view of how its competitors were using AI. This was not a simple benchmarking exercise. The market was moving fast, with constant announcements, pilots and claims across the retail value chain. But most of this information lacked clarity. It was difficult to tell what was real, what was scaled and what was still experimental.
The existing approach relied on fragmented inputs like analyst reports, vendor pitches and internal interpretation. These insights were not consistent, not easy to validate and could not keep up with how quickly the market was changing. Cloudaeon addressed this by building a structured, multi-agent AI solution on Databricks. This shifted the process from manual and opinion-led to automated, evidence-based and repeatable. It gave the enterprise a reliable way to track and compare AI maturity across competitors.
Client Problem
The enterprise’s strategic planning team had a major task to identify how their major competitors, like Walmart, Amazon, Tesco, Zara, and Target, were using and scaling AI across their retail operations. They needed an evidence-based review of the following:
Where were competitors genuinely ahead?
Where was progress being overstated?
Which AI investments were driving real operational change and which were simply marketing noise?
The answers available to them were very fragmented. These answers came from analyst decks, vendor pitches and internal opinion. None of it was repeatable or tied back to verifiable evidence. And none of it could be updated quickly as the market evolved.
Competitive intelligence relied on manual research, copy-paste from articles, and subjective scoring. It was impossible to audit or reproduce.
Lacking a structured way to separate genuine, deployed AI capability from press release language.
Slide decks had the insights, which were not in a queryable data format. As a result, they could not be reused by other teams, BI tools or chat assistants.
Each refresh of the benchmark cost weeks of analyst time and produced inconsistent results.
Strategic decisions on AI investment priorities were being made on stale and partially sourced information. As a result, leadership often lacked a defensible answer when questioned on the accuracy of competitor maturity ratings. At the same time, knowledge built during one benchmarking cycle was not retained and was effectively lost before the next cycle began, forcing teams to restart the process from scratch each time.
Root Cause Analysis
When the enterprise sought Cloudaeon’s help, our AI experts started with the base work first. A thorough root cause analysis was conducted to understand the real cause. The main issue was not a lack of information, because public reporting on retail AI is abundant. The actual problem was that no part of the existing process was built to handle that volume of evidence in a structured, defensible way.
Clouadeon’s AI experts first found out why the existing approach was failing:
Evidence was unstructured: Analysts captured opinions, not verified facts, so results varied from person to person.
No clear line between hype and reality: Small pilots and large-scale rollouts were treated the same.
No review process: Once a score was given, it was rarely checked or challenged.
Outputs were not reusable: Insights stayed in slides and PDFs and couldn’t be used elsewhere.
No audit trail: There was no way to trace scores back to sources or verify how they were created.
Solution Architecture
Cloudaeon designed and built the Competitor AI Maturity Research Agent for the retailer. It is an end-to-end, multi-agent AI platform running on Databricks. It automates the entire benchmarking lifecycle. Right from gathering public evidence, to scoring competitors across seven retail AI dimensions, to publishing the results as reports, structured data to a semantic search index.
The Seven Dimensions of Retail AI Maturity
Every competitor is scored 1–5 on each of these dimensions, giving a like-for-like view across the value chain:
Strategic Planning
Supply Chain
Product & Content Operations
Digital Experience
Store & Colleague
Customer Service
Governance & Data
How the Multi-Agent AI Pipeline Works
The platform uses a set of specialised AI agents, each handling a specific task. Together, they generate and then validate results to ensure accuracy.
Article Discovery Agent: Finds relevant articles and keeps only the most credible and recent sources.
Evidence Collection Agent: Extracts clear, verifiable AI use cases from each article and tags them to the right category.
Evidence Normalisation Agent: Removes duplicates, identifies if it’s a pilot or scaled use case, and rates how strong the evidence is.
Scoring Agent: Assigns a score (1–5) based on maturity, scale and impact, while preventing weak evidence from inflating scores.
Review Agent: Re-checks all scores, challenges weak claims and flags anything that doesn’t hold up.
From Insight to Action
Once scoring is complete, the platform automatically generates outputs that teams can use immediately:
An executive heatmap showing all competitors across all dimensions at a glance
A detailed report for each company, with evidence behind every score
A structured dataset that can be used in BI tools, dashboards, and SQL queries
A semantic search layer powering a chat assistant, where users can ask questions and get source-backed answers
How we Delivered
Platform Choices
Databricks: Used as the unified platform, from data collection, AI scoring, reporting, to serving all runs in one place. This creates a governed environment where Unity Catalog manages access control and tracks data lineage across the process.
Multi-agent design: Each agent returns structured and typed data instead of free-form text. This makes every step easy to test and audit, and keeps results consistent over time.
Vector search: Built directly on Databricks. The chat assistant uses the same governed data as the reports. This ensures answers are consistent and reliable.
Automation First
A single scheduled job runs the entire benchmark. Starting with discovery, scoring, review, reporting and publishing, with absolutely no manual handoffs.
Versioned run folders preserve every historical benchmark, so the teams can compare any two points in time and see exactly what changed.
Checkpoints after every step mean a failed run can be resumed without redoing earlier work. This has proved to be a key cost and reliability win for long AI pipelines.
Secrets are pulled from a managed key vault, so that no API keys or credentials live in code.
Quality & Validation
Every score is backed by a list of source URLs and extracted claims. These are fully traceable from the headline number back to the article that informed it.
The review agent acts as an automated quality gate, catching inflated or weak-evidence scores before they reach decision-makers.
An audit log captures every run. For example, which model was used, how many articles were processed, how many scores were generated and where every output landed.
Key Deliverables
A reusable Python package that any future Cloudaeon or organisation’s team can run, extend or adapt to new industries was delivered.
Executive PDFs were created so that strategy leadership can drop straight into board materials.
A structured competitor scores table was formed that feeds BI dashboards and SQL queries.
A semantic search index now exists that powers a competitor-intelligence chat assistant for internal teams.
Technology Stack
Databricks Lakehouse Platform (Unity Catalog, Delta tables, Vector Search, serverless compute)
Multi-agent AI orchestration
Large language models served via Databricks model serving and Azure AI Foundry
Embedding models for semantic search and chat-based Q&A
Automated web evidence collection and source ranking
Python-based, modular pipeline package
Managed key vault for secrets and credentials
Outcomes
Cloudaeon’s Competitor AI Maturity Research Agent transformed how the global brand builds competitive AI intelligence:
Cycle time reduced from weeks to hours: A full benchmark across six competitors and seven dimensions now runs in a single automated job. This replaces weeks of manual work.
Every score is defensible: All scores are traceable to source articles and extracted claims. The audit log makes it easy to explain why a score is a 4 and not a 5.
Reusable knowledge base: Insights no longer stay in PDFs. Scores are stored in governed data tables and evidence is stored in a semantic search index. Both can be reused across BI tools, chat and future analysis.
Repeatable and comparable: Each run is versioned. Teams can compare results across different time periods and clearly see what has changed.
Lower cost of refresh: The pipeline is modular and checkpointed. Only the steps that need updates are re-run. This reduces compute and API costs over time.
POD & Managed Ops Transition
Cloudaeon followed a three-stage model to turn the solution into a long-term capability:
Solution Delivery: Built and delivered a complete, production-ready platform with architecture, AI agents, reporting and audit framework.
POD Engagement: A dedicated POD team enhanced the platform. This included tuning agents, adding new competitors and dimensions and integrating new data sources and features.
Managed Operations: Cloudaeon took over ongoing operations. This included running benchmarks, monitoring quality, managing costs and handling updates and support under SLAs.
Conclusion
This transformation went beyond just automation. It established a new standard for how enterprises approach competitive intelligence in the age of AI. By combining strong architectural design, multi-agent AI engineering and governed data foundations, Cloudaeon enabled the organisation to build a system that is not only accurate but also trusted and scalable. This is where Cloudaeon stands apart, delivering production-ready AI solutions that enterprises can rely on for continuous, evidence-based decision making.