EGAN

• Market Recognition: Gartner published its first-ever Magic Quadrant for Customer Service Knowledge Management Systems, naming eGain a Leader with the highest position for Ability to Execute and furthest for Completeness of Vision.
• Strategic Shift to 'AI Customers': Management redefined its core metric from product hubs to 'AI Customers' (those using one or more AI offerings). AI Customer ARR now represents 72% of total SaaS ARR, up from 63% mid-year, signaling a successful transition toward an AI-led business model.
• Product Innovation: Launched key capabilities including eGain IVA (intelligent voice agent leveraging the same knowledge platform as digital tools), eGain Agentics Studio (zero-code environment for agentic workflows), and eGain Evaluator (for continuous AI pipeline QA).
• New Business Momentum: New logo wins increased 27% YoY. Pipeline opportunities valued at $500k+ ARR doubled YoY. Core verticals (banking, insurance, healthcare) saw a 40% increase in pipeline opportunities.
• Shift in Sales Motion: Buyers increasingly prefer paid pilots over free trials to validate platforms before full rollout. Examples include major pharmaceutical, testing/certification, and gaming companies deploying knowledge foundations and AI agents.
• Operational Trends: Enterprises are treating knowledge as core AI infrastructure, driving demand for real-time APIs and stringent SLAs. There is growing interest in customer self-service deployments rather than just contact center productivity.

• Q1 Fiscal 2027 Guidance:
  • Total Revenue: $20.9 million to $21.4 million.
  • AI Customer Revenue: $13.7 million to $14.0 million.
  • GAAP Net Income: $0.5 million to $1.0 million ($0.02–$0.04 per share).
  • Non-GAAP Net Income: $1.4 million to $2.0 million ($0.05–$0.08 per share).
  • Adjusted EBITDA: $1.4 million to $1.9 million (7%–9% margin).
• Full Year Fiscal 2027 Guidance:
  • Total Revenue: $84.5 million to $86.0 million (indicating a decline from FY2026 due to legacy runoff).
  • AI Customer Revenue: $59.5 million to $60.5 million (~8%–10% growth).
  • GAAP Net Loss: $(2.0) million to $(3.0) million.
  • Non-GAAP Net Income: $1.0 million to $2.0 million.
  • Adjusted EBITDA: $0.65 million to $1.4 million (1%–2% margin).
• Long-Term Financial Model (FY2030 Targets):
  • AI Customer ARR: $100 million to $120 million (17%–22% CAGR).
  • Total SaaS ARR: $100 million to $120 million (aiming for ~100% AI composition).
  • Total Revenue: $110 million to $120 million (15%–20% annual growth).
  • AI Customer Revenue to represent ~95% of total revenue.
  • Target Gross Margins: ~80%; Positive Adjusted EBITDA margins.

The transcript does not provide a breakdown of financial performance by specific product segment (e.g., SaaS vs. Professional Services) with distinct revenue contribution percentages for each. Instead, eGain has shifted its reporting focus to customer-based AI metrics. Total revenue was $91.1 million for the full year (up 3% YoY). Within this, AI customer revenue grew 20% YoY, while legacy non-AI customer revenue declined significantly. For Q4 specifically, total revenue was $22.2 million (down from $23.2 million in the prior year quarter), driven by declines in legacy conversation and analytics customers, partially offset by growth in AI-related business.

• Legacy Revenue Decline: Significant expected decline in revenue from profitable legacy customers (estimated 20% drop in FY2027 total revenue) as the company transitions resources to AI growth.
• Pricing Pressure: Potential for 1-2 percentage points of pricing pressure on SaaS products over the next two to three years due to broader AI market dynamics.
• Execution Risk in Transition: The need to successfully convert paid pilots into large-scale production rollouts and migrate the remaining non-AI customer base to zero by FY2030.
• Operational Complexity: Managing token costs and ensuring knowledge accuracy across diverse AI models requires precise instruction layers; failure in knowledge quality leads to confident but incorrect AI outputs.

Q: Vijay (Craig Hallam) asked about the timeline for running off non-AI ARR and whether AI is causing pricing pressure.

A: CFO Eric Smit confirmed the goal is to reduce non-AI ARR to zero by FY2030, similar to past messaging business transitions. CEO Ashutosh Roy noted that while there is some pricing pressure from general AI adoption, it is mitigated by new value-added AI offerings. He expects modest 1-2 point pressure over 2-3 years but emphasized that precise knowledge instructions create differentiation that protects pricing power.

Q: Vijay (Craig Hallam) requested a breakdown of the drivers for the long-term growth profile (price vs. new logos vs. expansion).

A: CFO Eric Smit stated that new logo acquisition will be the primary driver of growth, fueled by brand awareness from the Gartner Magic Quadrant recognition. While moving legacy customers to AI is important, it is not the main growth engine compared to acquiring new enterprise accounts.

Q: Ethan Whitehouse (B. Riley) asked if adopting multiple AI models increases demand for knowledge management solutions like iDynamic, and how open-source vs. frontier models compare.

A: CEO Ashutosh Roy explained that precise knowledge management reduces AI token costs by up to 10x by avoiding 'kitchen sink' context dumping. Regarding models, he noted that while frontier models may have 10-15% quality advantages, their cost is disproportionately higher. Therefore, smart routing to appropriate models (including open-source where suitable) is critical for managing costs in continuous operations.