Product Introduction
Dolphin AI is an automated customer intelligence platform that analyzes B2B customer calls to identify and track feature requests, pain points, and positive feedback. It integrates with call recording tools like Gong to process conversations using natural language processing (NLP) and machine learning (ML) models. The system eliminates manual tracking by automatically generating structured insights, categorizing them, and updating their status in real time. This enables teams to focus on strategic decision-making rather than data collection.
The core value of Dolphin AI lies in its ability to bridge the gap between customer success (CS) teams and product teams through automated, data-driven insights. By converting unstructured call data into actionable items, it helps organizations prioritize features based on actual customer needs and business impact. The platform ensures continuous feedback loops by tracking request implementation statuses and providing visibility across departments. This drives customer retention, reduces churn, and aligns product roadmaps with market demand.
Main Features
AI-Powered Call Analysis: Dolphin AI automatically detects feature requests, pain points, and customer praise in recorded calls using advanced NLP algorithms. It extracts verbatim quotes, generates short video clips with timestamps, and attaches CRM context like account value and industry. The system operates continuously, scanning new calls as they are added to integrated platforms like Gong.
Smart Request Merging and Categorization: The platform groups similar requests using semantic analysis to prevent duplicate entries in product backlogs. It calculates the frequency of recurring issues and associates them with relevant customers, ARR (annual recurring revenue), and priority scores. Users can merge related insights with one click, maintaining a clean, centralized repository of customer needs.
Closed-Loop Workflow Automation: Dolphin AI creates Jira tickets directly from validated insights and syncs their status updates back to the platform. It automatically notifies CS teams when features are shipped, enabling proactive customer follow-ups via CRM integrations. The system also generates shareable links with video snippets and business impact metrics to support prioritization discussions.
Problems Solved
Inefficient Manual Tracking: CS and product teams often waste hours manually reviewing calls, submitting requests to tools like Productboard, and chasing status updates. Dolphin AI eliminates spreadsheet/Slack-based tracking by automating insight extraction, categorization, and prioritization. This reduces feedback processing time from hours per call to minutes.
Misalignment Between Teams: Product teams lack visibility into which requests matter most to high-value accounts, while CS teams struggle to prove request validity. Dolphin quantifies demand by linking requests to specific customers, revenue impact, and video evidence. This creates a single source of truth for both teams to collaborate on roadmap decisions.
Lost Customer Insights: Critical feedback from calls frequently goes unrecorded or fails to reach decision-makers. Dolphin ensures no request is overlooked by analyzing 100% of calls, flagging urgent issues, and maintaining a searchable archive. Its automated tracking also prevents implemented features from being uncommunicated to requesting customers.
Unique Advantages
Voice-of-Customer Automation: Unlike Productboard or spreadsheets, Dolphin extracts insights directly from raw customer conversations rather than relying on manual input. Its AI detects nuanced context, such as frustration tones or enthusiastic endorsements, that text-based tools miss. This produces richer, more credible insights tied to real interactions.
End-to-End Workflow Integration: The platform uniquely connects call analysis to downstream actions like Jira ticket creation and CRM updates. Automated status tracking ensures every request has a clear lifecycle from identification to implementation, which manual tools cannot replicate. This closes the loop between customer input and product outcomes.
Revenue-Centric Prioritization: Dolphin prioritizes requests based on requester ARR, request frequency, and potential retention/expansion impact. Machine learning models predict which features will deliver the highest ROI, helping product teams align roadmaps with revenue goals. Competitors lack this direct link between customer feedback and financial metrics.
Frequently Asked Questions (FAQ)
How does Dolphin identify relevant insights in calls? Dolphin uses NLP models trained on B2B SaaS terminology to detect keywords, sentiment patterns, and contextual cues indicating feature requests or pain points. The AI ignores casual conversation and focuses on structured analysis of problem-solution statements. Users can adjust sensitivity thresholds to filter false positives.
What call platforms does Dolphin support? Dolphin currently integrates with Gong for automated analysis, with Zoom and Microsoft Teams integrations in development. Users can manually upload recordings from other platforms via Dolphin’s web interface. All processing occurs through encrypted APIs to maintain security.
How is Dolphin different from Productboard? While Productboard requires manual entry of feedback, Dolphin automates insight extraction from actual customer conversations. It provides video evidence and CRM context that static Productboard entries lack. Dolphin also automates status tracking and customer follow-ups, which Productboard does not support.
How secure is customer call data? All data is encrypted in transit (TLS 1.3) and at rest (AES-256), with SOC 2 Type II compliance. Dolphin automatically redacts sensitive information like payment details from transcripts. Access is controlled via role-based permissions and audit logs.
What time savings can teams expect? CS teams reduce feedback processing time by 80–90%, from 3–5 hours per call to under 30 minutes. Product managers save 10+ hours weekly on request validation and prioritization. The system also eliminates 90% of inter-team follow-ups by providing real-time status visibility.
