Idwala | gecco

Overview

Idwala is a consultancy specialising in market research and intelligence. Their work includes tracking job movements across senior executives, identifying market intelligence from LinkedIn and job boards, and producing insight reports for clients in venture capital, executive search and market research.

Their core challenge was manual data collection. Every week, the team scraped job boards, LinkedIn and company websites for eight different companies. The data was logged in spreadsheets, manually cleaned and consolidated into reports. The process consumed 24 hours per week and was prone to gaps and errors.

gecco designed and built an end-to-end automation that scrapes target job boards and LinkedIn daily, classifies and consolidates the data, flags new hires and departures, and generates a weekly report PDF. The system runs unattended and produces consistent, structured output.

Idwala now delivers three times as many reports monthly, with higher quality and consistency. The team moved from data collection to strategy and client insight. The business scaled revenue without scaling headcount.

Manual tracking, repetitive bottleneck

Idwala's intelligence reports are valuable precisely because they're current and precise. The team tracks job movements, executive departures, funding activity and organisational changes across eight companies. This work requires daily attention.

But daily attention was happening manually. Every morning, someone logged into six different websites, scraped data, logged it in a spreadsheet, cross-referenced against previous weeks, flagged changes and cleaned formatting issues. This routine consumed 3-4 hours daily.

The process was error-prone. Manually scraped data meant typos, missed entries and formatting inconsistencies. Some key hires were missed because they weren't captured at the exact moment of announcement. Client reports reflected the team's bandwidth as much as the actual data.

The real cost wasn't time. It was opportunity cost. The team's expertise was in analysis and insight, not data entry. That's where they should have been spending energy. Instead, they were trapped in daily scraping and consolidation.

Unattended automation, daily collection

gecco mapped Idwala's entire workflow: which websites to monitor, which fields to extract, how to classify new data, how to consolidate across sources and how to flag changes for the team.

The build included: automated daily scraping of six job boards and LinkedIn via headless browsing and API calls; intelligent parsing to extract names, titles, companies and dates; fuzzy matching to deduplicate entries and identify departures; and automatic PDF report generation.

The system runs daily without human intervention. It handles formatting consistency, deduplication, and flagging of significant changes. The team now reviews a daily log instead of building it manually.

The critical detail: the system was designed for Idwala's specific clients and intelligence requirements. It's not a generic data scraper. It's built into their actual workflow and output standards.

Three times the output, half the work

The first week of automation, Idwala delivered 30 reports instead of 10. The data was cleaner. The reporting was consistent. The team spent the freed hours on interpretation and client insight, the high-value work.

Quality improved. Because data collection was now automated and consistent, gaps were obvious. The team could focus on validation and enrichment, not on searching for missed entries.

The business impact was immediate. Idwala could serve more clients without adding headcount. Revenue grew 3x within six months while the team stayed the same size. The return came from pure operational leverage: same people, three times the output.

For other organisations managing similar workflows, the path is clear. Map the repetitive process. Identify the data sources. Build unattended automation. Free your team for what they're actually hired to do.

Automation multiplies expertise

Idwala's story isn't about replacing people with robots. It's about removing toil so people can do the work only humans can do. The team's value is in their intelligence analysis. The system's value is in consistency and scale.

For consultancies and research firms, this model applies widely. Any repetitive data collection process that feeds into expert analysis is a candidate for automation. The more manual data work you can eliminate, the more your experts can focus on actual insight.

The best part: once the system was built, it required almost no ongoing maintenance. Idwala owns it. If their target companies or intelligence requirements change, they can modify the system themselves.

What we learned

Manual repetitive processes in knowledge work are not productivity bottlenecks. They're capability limitations. Automating them doesn't just save time. It changes what your team can deliver.

The best candidates for automation are those where consistency and speed matter more than human judgment. Data collection, consolidation and reporting are perfect. But they need to feed into human analysis to deliver value.

Organisations that succeed with automation are those that reinvest freed time into higher-value work, not headcount reduction. Idwala didn't reduce the team. They expanded output. The business grew revenue while the team grew expertise.