What “production AI” looks like in practice

Challenge: A fast-growing e-commerce brand saw support tickets surge, with long wait times and inconsistent answers across agents.

Solution: We implemented a knowledge-grounded chatbot with escalation rules for edge cases and a feedback loop to improve coverage weekly.

Tools used:

• RAG knowledge base (policies, product docs, FAQs)
• Quality evaluation set for top intents
• Analytics tracking deflection, containment, and failure modes

Results:

• 40% reduction in cost-to-serve (support)
• 24% faster first response time
• Improved consistency on policy-related answers

Build a support bot

Challenge: A SaaS team lacked visibility into which trials would convert. Sales reps followed intuition and missed the highest-intent accounts.

Solution: We built a propensity model and integrated the score into the CRM workflow so reps prioritized the right accounts daily.

Tools used:

• Feature pipelines from product usage data
• Model evaluation with holdout testing
• CRM integration for operational adoption

Results:

• 25% uplift in sales conversion in targeted segments
• Reduced wasted outreach on low-intent accounts
• Clear weekly reporting for leadership

Improve forecasting

Challenge: A fintech operations team manually reviewed and routed inbound requests across multiple systems, causing bottlenecks and delays.

Solution: We implemented an automation layer that classified requests, generated summaries, and routed to the correct queue—with human approval for sensitive cases.

Tools used:

• Workflow mapping and automation candidates
• LLM summarization with guardrails
• Audit-friendly human-in-the-loop controls

Results:

• 60% reduction in time spent per request
• Faster SLA adherence and fewer escalations
• Improved consistency with routing rules

Automate a workflow