CASE STUDIES & AI SUCCESS STORIES

Our client, a mid-size manufacturing company needed to:

• Fix onboarding for a 20-person hybrid sales team.
• Stop finding out only after 9 months that a hire was a bad fit.
• Stop putting underprepared reps in front of clients and losing deals.
• Cut the $100k+ cost of a bad hire.

• New hires were dropped into dozens of disconnected documents instead of a real learning process.
• Materials were outdated, incomplete, contradictory, and partly existed only in senior employees' heads.
• Support was reactive: people answered what was asked, but nobody checked what had not been asked or misunderstood.
• Onboarding ended when someone said they had "gone through everything," not when they had proven they were ready.
• The result: 50% lower closing rates for new employees, late discovery of bad hires, and senior people repeating the same explanations 10 times instead of doing revenue work.

• We replaced the document dump with a structured onboarding program that new hires described as better than anything they had seen before.
• We made readiness criteria explicit and added topic-specific custom GPTs using the client's existing GenAI tools.
• New hires could get answers, explanations, and practice with feedback in one place instead of hunting through scattered materials.
• Senior staff spent far less time on repeated questions and more time on mentoring and client work.
• Results: first-attempt pass rate increased from 70% to 90%, time to identify role mismatch dropped from 9 months to 1 month, first-month mistakes fell by ~30%, senior support time dropped by ~50%, and the cost of a bad hire fell from $100k+ to under $20k.

An SMB SaaS client needed to:

• Keep a 10+ year-old legacy product with 100,000+ downloads on the market, as it was an underlying tech for their best selling solution.
• Stop burning a whole team's effort just to keep the stack current and third-party dependencies up to date.
• Remove the constant problem of having to search for months for rare replacements every time someone left the project.
• Move engineers with rare cross-functional expertise onto new product opportunities where their skills mattered more.

• Maintaining the product required a rare and expensive mix of C++, C#, PDF generation, and text processing.
• There were almost no people on the market with that profile, and few wanted to grow into such a narrow legacy niche.
• The client spent tens of thousands of dollars on hiring just to close gaps, not to get ahead.
• New features release frequency dropped from eight in 2021 to one in 2025, and the product sat on 10–15 year-old technology that was stable, but painful to maintain.

• We translated the C++ part into C# using AI under strict expert control, so support no longer depended on one of the hardest skill sets to hire for.
• That moved the product onto a common stack that one mid-level C# developer can maintain instead of five unique specialists.
• We introduced strict TDD with a verified baseline and cut QA time from days to minutes on new releases.
• Work estimated at 12+ months manually was completed in 2 months, support cost dropped 5 times, and rare engineers were freed up to help launch another product already seeing demand.

• For a representative 50-person training company, webinars were not a side activity. They were a core channel for attracting new clients, educating prospects, and creating follow-up opportunities. That fits the broader market: two-thirds of organizations use webinars to educate customers, organizations run 29.9 webinars a year on average, and a quarter run 50 or more.
• The team needed a reliable way to see where audience interest was high, where confusion remained, and where a webinar topic, structure, or format needed to change. Audience questions were one of the clearest signals they had.
• They also needed webinar activity to connect to business outcomes, not just registrations. Industry tracking is still weak: only 17% of teams track webinar impact on pipeline and only 12% track impact on revenue.
• They needed to protect the broader learning experience as the webinar program scaled. If attendees left without being able to ask what mattered to them, that frustration did not stay inside the webinar.

• The company could not put five or seven people around every webinar to monitor chat, moderate questions, and prepare follow-up. At scale, that is exactly where live Q&A breaks down: ON24 reports an average of 216 attendees, a 57% registration-to-attendee conversion rate, 51 minutes of engagement, and over 300 interactions per webinar in 2024, including 14 attendee questions on average.
• A short Q&A window could not realistically cover everything the audience wanted to ask. Valuable questions were buried in chat, left unanswered, or lost once the session ended.
• That created two business problems at once: attendees left without clarity, and the team lost one of its best signals for improving the next webinar and deciding where follow-up was needed.
• The issue did not stop with the live session. ON24 also reports that 45% of attendees chose on-demand webinars in 2024, and making webinars available on demand by default can increase total views by up to 80%.

• We built an AI Q&A assistant for webinar and training environments to remove that bottleneck.
• It let attendees ask questions during the session and after it ended, including questions they only thought of later, while preserving the discussion so they could return to it.
• It gave organizers a structured view of unanswered questions, audience interests, and the most engaged participants, making it easier to improve future webinars and plan relevant follow-up.
• In pilot launches, introducing the assistant increased audience engagement by 20% and led to 20% more feedback on webinar content.

• As a 100+ person B2B SaaS company, the client needed to remove growing business dependence on a legacy core and database platform that had become too costly, too specialized, and too risky to sustain.
• The business wanted to move away from a database platform built on technology already more than 15 years behind modern standards.
• Keeping that database platform current required at least $1M for major upgrades and another $1M+ for ongoing maintenance and support.
• The client also needed to reduce reliance on a team of 10+ senior specialists with rare and expensive database and legacy system expertise.

• The core product depended on more than 100,000 lines of code written in outdated technology and tightly coupled to the existing database.
• Around 90% of the business logic embedded in the legacy database-driven system had no documentation at all.
• The system had no automated tests, which made any database migration slow, risky, and hard to validate.
• Any modernization had to be seamless for users and could not require additional actions or disruption on their side.

• We analyzed the legacy solution, reviewed the alternatives, and selected a free database platform that covered the client's real business and technical needs.
• We used AI to structure requirements, generate tests, and accelerate code delivery throughout the migration rather than limiting it to analysis alone.
• As a result, the client reached a validated production-ready transition about 3x faster than with a fully manual approach.
• The client now runs a simpler modern core with code reduced by more than half, 50% fewer servers, 40% lower support costs, and at least 30% lower team demand.