38% Revenue Boost with Agentic AI-Powered Competitive Intelligence for Market Expansion

The client, a public-listed, India-headquartered cloud services provider, was expanding into the MENA region to capture rising enterprise demand for AI-ready, hybrid cloud infrastructure. As part of this expansion, the Middle East emerged as the highest-priority market.

The leadership team wanted faster, deeper competitive intelligence to inform pricing strategies, partnerships, and go-to-market decisions. However, competitor data was scattered across multilingual sources, including Arabic regulatory filings, English analyst reports, and French investor briefings, making manual research slow and error-prone.

The client approached Superteams.ai with its market research challenge. After analyzing their requirements, Superteams.ai recommended a pilot-first approach focused exclusively on competitive intelligence automation for the Middle East - their most immediate and critical need.

Challenge Faced

The client faced several bottlenecks that slowed down their market expansion strategies:

• Multilingual Data Sources – Competitor filings, analyst reports, and compliance disclosures were in Arabic, English, and French, requiring context-aware translations.
• Scattered Intelligence – Data was spread across financial filings, investor decks, earnings transcripts, regulatory portals, and local news.
• Inconsistent Formats – Information came in scanned PDFs, structured APIs, HTML tables, and regional press releases, making aggregation difficult.
• Slow Insights – Manual compilation and translation workflows took 4–6 weeks, delaying GTM launches and pricing decisions.
• Data Sovereignty Requirements – As a cloud provider, the client mandated on-premise deployment to maintain complete control over sensitive competitive data.

Our Approach

Step 1. Forming an Agentic AI Taskforce

We deployed an Enterprise Agentic AI Team combining:

• LLM Engineers → Fine-tuned multilingual models to extract pricing moves, partnerships, and market shifts from documents in Arabic, English, and French.
• Solution Architects → Designed a multi-agent orchestration layer to automate data collection, translation, normalization, and insights generation.
• MLOps Engineers → Ensured on-prem deployment optimized for latency and compute within the client’s private cloud infrastructure.

Step 2. Designing the Competitive Intelligence Blueprint

Working with the client’s strategy and analytics leaders, we:

• Defined primary objectives: track competitor pricing, product launches, expansion signals, and enterprise client acquisitions.
• Built data ingestion pipelines to collect multilingual competitive data from:
  • Financial filings in Arabic and English
  • Analyst reports in English and French
  • Regional compliance disclosures in Arabic
  • Local press coverage
• Developed an LLM-powered translation layer to ensure context-preserving multilingual understanding.
• Normalized extracted data into standardized schemas, enabling apples-to-apples comparisons across regions and languages.

Step 3. Delivering a 30-Day Multilingual Prototype

We delivered a functional prototype within 30 days by:

• Ingesting real Middle East competitor data from multiple sources.
• Training multilingual LLMs to extract pricing patterns, service SKUs, expansion geographies, and partnerships.
• Automating executive-ready summaries in English while retaining document-level traceability.
• Setting up weekly feedback loops with the client’s leadership to refine outputs based on regional strategic priorities.

A Real-World Scenario Solved

Before the prototype, analyzing a 90-page Arabic-language competitor regulatory filing involved:

• Translation into English by in-house analysts
• Manual scanning for pricing updates, compliance changes, and expansion footprints
• Collating findings into leadership-ready decks

This process consumed 3+ days per filing and often lost critical contextual nuance.

With the Agentic AI prototype:

• The filing was ingested, translated, and normalized instantly.
• Pricing insights, SKUs, and partnership signals were extracted into structured dashboards.
• A concise executive summary in English highlighted competitive risks and opportunities within minutes.

Result: A 3-day multilingual workflow became a 3-hour process with 95%+ contextual accuracy.

By automating multilingual intelligence pipelines, the company accelerated regional GTM launches, refined pricing strategies, and captured high-value enterprise deals in the Middle East.

Conclusion & Roadmap

The 30-day multilingual competitive intelligence prototype demonstrated how Agentic AI could deliver faster, richer insights while navigating language diversity and data sovereignty challenges.

Encouraged by the results, the company is now expanding AI adoption to:

• Demographic analysis for regional product launches
• Cross-border growth projections across the wider MENA region
• Regulatory intelligence tracking for multilingual compliance readiness

By starting with a single high-value use case, Superteams.ai helped the client prove impact quickly while laying the foundation for scalable, multilingual market intelligence across multiple geographies.