Addictive Learning Technology Ltd Q4 FY2026 Concall Summary

Key Takeaways

• Disruption past, recovery underway: H2 FY26 revenue was hit by a spike in ad costs, but management says April–June have progressively improved and monthly revenue is back to prior levels.
• Cash flow is the sole northstar: The internal operating goal is ₹2 crores per month positive cash flow; the company is already cash-flow positive and stopped drawing from reserves two months ago.
• AI is now the operating system, not just a product: AI has replaced large swaths of human coaching, sales, and evaluation staff, driving down costs while maintaining revenue and pushing learner satisfaction to an all-time high.
• Capex cycle is over: The ₹38 crore build phase on course libraries and technology is complete; future capex is minimal, shifting the business into a harvest phase.
• What to watch next quarter: Whether the monthly cash-flow trajectory sustains toward the ₹2 crore target, and if the overhauled AI-first sales engine can scale direct-sales revenue without a proportional increase in headcount.

The H2 Disruption & Recovery

• Ad-cost shock: In February and March, lead-generation costs suddenly spiked to 2–3x previous levels, directly compressing revenue in the second half. Management characterizes this as a one-time disruption that has now passed.
• Recovery trajectory: April, May, and June have shown progressive improvement; ad costs are now at their "best ever" levels in many cases, and monthly revenue has recovered to prior levels.
• Cash-flow trough navigated: Monthly cash outflow peaked at ₹8 crores in January (including capex and opex) but was reduced to ₹6 crores and below. The company has stopped drawing from reserves in the last two months.
• Deferred-revenue cushion: ₹9 crores of deferred revenue will aid the current financial year, and the negativity from last year's adjustments will not recur.
• Founder’s admission on delay: Management attributes the prolonged ad problem to over-reliance on external experts, calling it a failure of inexperience. The fix came only when the founder personally analyzed the data using new AI tools.

Ramanuj: "My biggest failure has been you know accepting experts opinion in a lot of places and thinking that it is gospel. We could have fixed the ad problem much earlier if I did not listen to so-called experts and looked at it myself which I did eventually and then fixed it."

AI-Driven Operating Model Reset

• Workforce reduction via AI: Consultants and employees were let go as AI replaced their functions, triggering upfront cash costs for severance, gratuity, and full-and-final settlements.
• Coaching & evaluation automated: A task like Upwork profile building went from 70–80 people to one person serving ~1,000 learners per week. Assignment evaluation now requires only 4–5 people for 1,000–1,200 monthly enrollments, down from an "army" of evaluators.
• AI sales system deployed: The new sales stack includes AI-driven discovery calls, automated roadmap generation, and AI nudges for callers. Management claims it has "the best sales AI in the market," and it has materially increased the productivity of top-performing callers.
• Proprietary marketing platform: The marketing team built an in-house AI platform (growthx.skillarbitrage) that tripled page load speed and cut server costs from ₹10 lakh to ₹1.5 lakh per month, built by non-engineers using AI tools.
• Learner satisfaction at peak: Despite headcount reduction, student feedback on AI tools is positive and satisfaction is at an all-time high. Daily messages from learners report jobs secured and freelance work won.
• Founder’s visibility transformed: The founder credits AI with giving him a "cloud code" that reads every piece of business data and delivers analysis at a level previously unavailable, calling this the biggest change in the last six months.

Ramanuj: "What really changed in the last 6 months was my visibility over the business really changed with AI because now I have something like a cloud code which can read every data and give me analysis at a level that was never available to me as a business owner before and that's the biggest biggest change."

Sales & Revenue Engine

• Sales leadership overhauled: The sales team has been fully restructured in the last three months and is now AI-first. The process splits into AI-led discovery and roadmap creation, human-led mentorship calls, and a closing call that has not yet been automated.
• Direct sales as growth driver: Management sees direct sales (not communities or boot camps) as the primary source of future growth, with significant runway. The current team size can scale to ₹10 crores monthly revenue with only 10–20% more headcount, whereas the old model would have required 200–300 people.
• Community revenue steady: Communities are deliberately messaged only once or twice a month to avoid unsubscribes, yet still drive ₹50–75 lakhs in revenue per month.
• Boot camp ad spend rationalized: Ad spend on boot camps was cut significantly to ~₹1 crore per month in the last two months, down from prior levels. It may increase slightly as results improve and more boot camps are planned.
• Lawsikho growth: Lawsikho revenue has grown steadily in the last 2–3 months, with law communities performing particularly well. Skill Arbitrage remains larger but fluctuates month-on-month due to batch starts and boot camps.
• No new courses: The company is focused on selling existing courses and upgrading them where needed; management believes the current library is more than sufficient.

Costs, Margins & Cash Flow

• People costs down, revenue maintained: AI efficiency has reduced the need for human coaches and sales staff without cutting services; revenue is at the same level or better.
• Margin recovery in progress: Margins are almost back to old levels as of May, driven by opex reduction and the total cut in capex.
• CAC improvement: Customer acquisition costs are falling because ad costs have improved significantly, with scope for further reduction.
• Capex effectively zero: The ₹38 crore build phase is complete; only ~₹40 lakhs remains to finish existing builds. No further material capex is planned.
• No borrowings, no receivables: The company has no debt and no plans to borrow. Students pay upfront, so there are no trade receivables.
• IPO funds largely deployed: Most IPO proceeds have been used as planned; only the portion earmarked for acquisitions remains undeployed, and no acquisitions are being pursued.

Strategic Pivot: Harvest Mode

• Build phase complete, harvest begins: Management explicitly states the company is entering a scale-up and harvest phase after two years of heavy investment in content and AI capabilities.
• All expansion on hold: International expansion, the Arizona university project, and ancillary services like job platforms are paused until the ₹2 crore monthly cash-flow target is achieved. The US university plan is in "cold storage."
• Hello Paralegal exception: The one new initiative still active is Hello Paralegal (100% owned), which has started securing contracts from US law firms for automating legal workflows. It has gone viral in US legal circles, though revenue has not yet hit the account.
• No fundraise: Management is "extremely against" raising funds now, believing the company can grow profitably. No plans to raise in the next six months.
• Founder share purchases: Promoters Ramanuj and Abhyuday have not sold any shares and have bought from the open market since IPO. Both are planning to buy more shares. One other co-founder sold a small holding about a year ago for a daughter’s wedding; most of those shares were bought by the two founders.

Ramanuj: "I do not I want to do it but I am always afraid that we I will start the university and there's a regulator right and then if I am not able to generate enough enrollments or if I generate enrollment students are not happy it's a new battlefront"

AI & Sector Positioning

• AI as competitive moat: Management asserts that two years of investment have built proprietary AI capabilities that no competitor possesses, spanning content, learner journey, learner outcomes, and learner outreach.
• Licensed-profession focus: The company operates in sectors like law, tax, accounting, and corporate governance where AI cannot legally replace licensed professionals. Management argues AI will help these professionals earn more, not lose their jobs, and demand for AI courses in these fields is massive.
• 50+ career apps deployed: AI applications include a Pitch Machine for finding US SMBs, an SLP drafting tool for lawyers, a CPA finder, and an academic outreach tool for professors and PhD holders.
• Content licensing potential: Large AI labs have expressed interest in licensing the company’s content. No deal has been done yet, but management believes the prospect will improve with time.

Ramanuj: "Software engineering is very different but these categories are very different. So we are sitting in sectors where AI is going to help us not cause a problem and there are very few companies we have really invested in AI we build our own AI, we can teach AI."

Guidance & Commitments

Key Metrics