{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_manja-ai","slug":"manja-ai","name":"Manja.ai","type":"product","url":"https://manja.ai","page_url":"https://unfragile.ai/manja-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_manja-ai__cap_0","uri":"capability://data.processing.analysis.conversation.based.sales.performance.analysis","name":"conversation-based sales performance analysis","description":"Analyzes uploaded call recordings and transcripts to extract performance metrics, objection patterns, and deal progression signals specific to each rep's actual conversations. Uses speech-to-text transcription combined with NLP-based intent detection to identify talking points, objection handling, and close attempts, then correlates these patterns with deal outcomes to surface personalized coaching areas rather than generic sales advice.","intents":["I want to understand what I'm doing wrong in my sales calls without relying on manager feedback","I need to identify which objections I struggle with most and see examples from my own deals","I want to see how my conversation patterns correlate with won vs lost deals"],"best_for":["Individual sales reps seeking self-directed improvement","Sales teams with 5-50 reps where personalized coaching is cost-prohibitive","Reps using non-standard sales methodologies who need methodology-agnostic feedback"],"limitations":["Requires consistent call recording and upload; sporadic data (< 5 calls/month) produces unreliable pattern detection","Transcription quality depends on audio clarity; background noise and multiple speakers degrade accuracy","Analysis is retrospective only — no real-time guidance during active calls","Unclear whether coaching adapts to different sales methodologies or enforces a single generic approach"],"requires":["Call recordings in MP3, WAV, or M4A format (typical sales call recording tools)","Ability to upload files (web UI or API integration)","Minimum 3-5 call recordings to establish baseline patterns","Optional: CRM integration (Salesforce, HubSpot) to correlate call patterns with deal outcomes"],"input_types":["audio files (call recordings)","transcripts (text)","deal metadata (stage, outcome, value)"],"output_types":["structured coaching insights (JSON/dashboard)","objection pattern reports","performance metrics (talk time %, objection handling rate, close rate by objection type)"],"categories":["data-processing-analysis","sales-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_1","uri":"capability://data.processing.analysis.objection.pattern.extraction.and.clustering","name":"objection-pattern extraction and clustering","description":"Automatically identifies and categorizes objections from call transcripts using NLP classification, then clusters similar objections across multiple calls to reveal which objection types appear most frequently and which ones correlate with deal loss. Builds a rep-specific objection taxonomy that evolves as more calls are analyzed, enabling targeted practice on high-impact objection types.","intents":["I want to know which objections I hear most often and which ones I lose deals on","I need to practice handling the specific objections my prospects raise, not generic ones","I want to see how my objection handling has improved over time"],"best_for":["Sales reps in competitive markets facing consistent objection patterns (price, timing, competitor comparison)","Sales managers coaching reps on specific weakness areas","Teams implementing objection-handling training programs"],"limitations":["Objection detection accuracy depends on transcript quality and context clarity; sarcasm, indirect objections, and cultural nuances may be misclassified","Requires minimum 10-15 calls to establish reliable objection patterns; early-stage reps see noisy results","Cannot distinguish between prospect objections and rep's own objection statements without semantic context","No built-in A/B testing framework to validate whether coaching on specific objections actually improves close rates"],"requires":["Transcribed call recordings (audio or text)","Minimum 10 calls for statistically meaningful clustering","Optional: deal outcome data (won/lost) to correlate objections with conversion"],"input_types":["call transcripts (text)","deal outcome labels (won/lost)"],"output_types":["objection taxonomy (structured list of objection types and frequency)","objection-to-outcome correlation matrix","rep-specific objection handling metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_2","uri":"capability://data.processing.analysis.deal.stage.specific.coaching.recommendations","name":"deal-stage-specific coaching recommendations","description":"Segments call analysis by deal stage (discovery, qualification, proposal, negotiation, close) and generates stage-specific coaching insights tied to rep behavior patterns at each stage. Uses temporal analysis of call transcripts to identify which stage each call belongs to, then compares rep's approach (questions asked, value propositions mentioned, objection handling) against successful patterns from their own win history.","intents":["I want to know if I'm asking the right discovery questions early in deals","I need to understand why my proposals aren't converting and what I should change","I want to see what I do differently in deals I win vs deals I lose at the negotiation stage"],"best_for":["Sales reps following structured sales methodologies (MEDDIC, Sandler, Consultative Selling)","Teams with defined deal stages in CRM who want to optimize stage-specific behaviors","Reps managing longer sales cycles (B2B, enterprise) where stage progression is critical"],"limitations":["Requires CRM integration or manual deal stage labeling to map calls to stages; without this, stage detection is heuristic-based and error-prone","Coaching recommendations are descriptive (what you did) not prescriptive (what you should do); reps must interpret insights and self-coach","Cannot account for deal-specific context (prospect industry, company size, competitive situation) that may justify different approaches at same stage","Limited to rep's own win/loss history; new reps or those with few deals see unreliable recommendations"],"requires":["Call transcripts with temporal markers (timestamps or call duration)","Deal stage metadata from CRM (Salesforce, HubSpot) OR manual stage labeling","Minimum 5-10 deals per stage to establish baseline patterns"],"input_types":["call transcripts with timestamps","deal stage labels","deal outcome (won/lost)"],"output_types":["stage-specific behavior reports (questions asked, value props mentioned, objection types by stage)","win/loss comparison by stage","coaching recommendations tied to specific stage"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_3","uri":"capability://data.processing.analysis.talk.time.and.conversation.balance.metrics","name":"talk-time and conversation-balance metrics","description":"Extracts speaker diarization from call recordings to measure rep talk time vs prospect talk time, then calculates conversation balance metrics (prospect-to-rep talk time ratio, rep interruption frequency, prospect question count). Compares these metrics against rep's own win/loss history and industry benchmarks to surface whether rep is over-talking, under-listening, or interrupting too frequently.","intents":["I want to know if I'm talking too much or not listening enough in my calls","I need to see if my conversation balance has improved over time","I want to understand if my interruption habits correlate with deal loss"],"best_for":["Sales reps with coaching-receptive mindsets who want behavioral feedback","Sales managers coaching reps on listening skills and presence","Teams implementing consultative or solution-selling methodologies that emphasize prospect listening"],"limitations":["Speaker diarization accuracy degrades with multiple speakers, background noise, or overlapping speech; may misattribute talk time","Metrics are descriptive (you talked 70% of the time) not causal; doesn't prove talk time caused deal loss","Industry benchmarks are generic and may not apply to rep's specific market, product, or prospect type","Cannot distinguish between productive rep talking (discovery questions, value prop explanation) and unproductive talking (rambling, over-explaining)"],"requires":["Call recordings in audio format (MP3, WAV, M4A)","Minimum 5-10 calls to establish baseline talk-time patterns","Optional: deal outcome data to correlate talk time with win/loss"],"input_types":["audio files (call recordings)"],"output_types":["talk-time metrics (rep % vs prospect %, interruption count, prospect question count)","trend analysis (talk time over time)","correlation with deal outcomes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_4","uri":"capability://planning.reasoning.personalized.coaching.action.plans","name":"personalized coaching action plans","description":"Synthesizes insights from conversation analysis, objection patterns, and deal-stage behavior into prioritized coaching action plans that recommend specific skills to practice (e.g., 'improve discovery questioning in first calls' or 'handle price objections with value-based reframing'). Generates rep-specific practice scenarios and suggested talking points based on actual objections and deal patterns from their call history.","intents":["I want a clear, prioritized list of skills to work on based on my actual call patterns","I need practice scenarios and talking points for the objections I actually face","I want to track progress on coaching recommendations over time"],"best_for":["Self-directed sales reps who prefer data-driven coaching over manager feedback","Sales managers coaching reps on specific, measurable improvement areas","Teams implementing peer coaching or rep-to-rep skill sharing programs"],"limitations":["Action plans are generated from historical data; they don't account for market changes, new competitors, or product updates that may require different approaches","Recommended talking points are extracted from rep's own calls, which may perpetuate bad habits if rep's current approach is fundamentally flawed","No built-in accountability mechanism; reps can ignore recommendations without consequence","Progress tracking requires manual re-upload of new calls; no automated monitoring of whether rep is actually implementing recommendations"],"requires":["Minimum 10-15 analyzed calls to generate meaningful action plans","Deal outcome data (won/lost) to prioritize recommendations by impact","Optional: CRM integration to track action plan progress over time"],"input_types":["conversation analysis results","objection patterns","deal-stage behavior data","deal outcomes"],"output_types":["prioritized coaching action plans (JSON/dashboard)","practice scenarios with suggested talking points","progress tracking metrics"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_5","uri":"capability://data.processing.analysis.call.recording.ingestion.and.transcription.pipeline","name":"call-recording ingestion and transcription pipeline","description":"Accepts call recordings in multiple audio formats (MP3, WAV, M4A) via web upload or API, automatically transcribes them using speech-to-text (likely cloud-based ASR like AWS Transcribe or Google Cloud Speech-to-Text), and stores transcripts with metadata (call date, duration, rep, prospect) for downstream analysis. Handles variable audio quality and call lengths (typically 15-60 minutes for sales calls).","intents":["I want to upload my call recordings and have them automatically transcribed","I need to integrate call recording uploads into my existing sales workflow","I want to ensure my call data is stored securely and linked to my CRM"],"best_for":["Sales reps using standard call recording tools (Gong, Chorus, Dialpad, native phone system recording)","Teams with existing call recording infrastructure who want to add coaching analysis","Organizations with compliance requirements for call recording storage and retention"],"limitations":["Transcription accuracy degrades with background noise, multiple speakers, or heavy accents; typical WER (word error rate) is 5-15% for sales calls","Transcription latency is typically 5-30 minutes depending on call length and ASR provider load; not real-time","Audio format support is limited; proprietary formats (Zoom, Teams, Cisco) may require conversion","No built-in PII redaction; transcripts may contain sensitive customer data (credit card numbers, SSNs) requiring manual review or downstream filtering"],"requires":["Call recordings in MP3, WAV, or M4A format","Web UI or API access to upload files","Optional: CRM integration (Salesforce, HubSpot) to auto-link calls to deals and reps","Optional: call recording tool integration (Gong, Chorus, Dialpad) for automated ingestion"],"input_types":["audio files (MP3, WAV, M4A)","metadata (call date, rep name, prospect name, deal ID)"],"output_types":["transcripts (text with timestamps)","call metadata (duration, participants, quality score)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_6","uri":"capability://automation.workflow.freemium.tier.with.usage.based.upsell","name":"freemium tier with usage-based upsell","description":"Offers free tier with limited monthly call analysis (typically 5-10 calls/month) to enable individual reps to test value before team/enterprise commitment. Upsells to paid tiers based on call volume, team size, or advanced features (CRM integration, custom coaching frameworks, team dashboards). Freemium model reduces adoption friction by allowing reps to experiment without manager approval or budget allocation.","intents":["I want to try AI sales coaching without committing to a paid plan","I want to convince my manager to buy a team license by showing ROI from my own usage","I want to scale from individual usage to team-wide coaching as my team grows"],"best_for":["Individual high-performing sales reps seeking self-improvement","Sales managers evaluating coaching tools before enterprise rollout","Small teams (5-50 reps) with limited training budgets"],"limitations":["Free tier call limits (5-10/month) are insufficient for meaningful pattern detection; reps need 10-15 calls minimum to see reliable insights","Freemium model creates adoption friction for teams; reps must manually upload calls rather than auto-ingesting from CRM or call recording tool","Unclear pricing and feature differentiation between tiers; reps may not understand what they gain by upgrading","No team collaboration features in free tier; coaching insights are individual-only, limiting manager coaching workflows"],"requires":["Email address to create account","Call recordings to upload (free tier limited to 5-10/month)","Optional: payment method for paid tier upgrade"],"input_types":["user account data (email, name, company)","call recordings (limited by tier)"],"output_types":["account tier status","usage metrics (calls analyzed, remaining quota)","upgrade recommendations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_7","uri":"capability://tool.use.integration.crm.integration.for.deal.outcome.correlation","name":"crm integration for deal outcome correlation","description":"Integrates with Salesforce, HubSpot, or other CRMs to automatically link analyzed calls to deals, pull deal stage and outcome data (won/lost), and correlate rep conversation patterns with deal results. Enables analysis like 'your discovery questions correlate with 15% higher close rates' by matching call metadata (rep, prospect, date) with CRM deal records.","intents":["I want to see how my conversation patterns correlate with deals I actually win or lose","I want to automatically link my calls to the right deals without manual data entry","I want to understand which of my behaviors actually impact close rates"],"best_for":["Sales teams with mature CRM implementations (Salesforce, HubSpot) and consistent deal tracking","Organizations with defined deal stages and outcome tracking","Sales managers analyzing rep performance and coaching effectiveness"],"limitations":["Integration requires CRM API access and OAuth setup; not all CRM systems are supported (Salesforce, HubSpot yes; Pipedrive, Copper may have limited support)","Deal outcome correlation is probabilistic, not causal; correlation with close rates doesn't prove conversation patterns caused the outcome","Requires accurate deal metadata in CRM (stage, outcome, close date); garbage data in CRM produces garbage insights","Privacy and compliance concerns; syncing call transcripts with CRM may violate data residency or retention policies"],"requires":["Active CRM account (Salesforce, HubSpot, or other supported system)","CRM API access and OAuth permissions","Consistent deal tracking in CRM (stage, outcome, close date)","Optional: custom field mapping if CRM schema differs from Manja's expectations"],"input_types":["call metadata (rep, prospect, date, duration)","CRM deal data (deal ID, stage, outcome, close date, value)"],"output_types":["deal-linked call analysis","outcome correlation metrics (win rate by conversation pattern)","rep performance dashboards"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_manja-ai__cap_8","uri":"capability://data.processing.analysis.rep.to.rep.benchmarking.and.peer.comparison","name":"rep-to-rep benchmarking and peer comparison","description":"Aggregates anonymized conversation metrics and coaching insights across multiple reps on a team to enable peer benchmarking (e.g., 'top performers ask 40% more discovery questions than average reps'). Surfaces best practices from high-performing reps (talk time ratio, objection handling patterns, discovery question types) that lower-performing reps can learn from, without exposing individual rep names or sensitive deal data.","intents":["I want to see how my conversation patterns compare to top performers on my team","I want to understand what high-performing reps do differently in their calls","I want to identify best practices from my team to share in coaching sessions"],"best_for":["Sales managers coaching teams and identifying best practices","Sales teams with 10+ reps where peer learning is valuable","Organizations implementing peer coaching or rep-to-rep mentoring programs"],"limitations":["Benchmarking requires minimum 10-15 reps with sufficient call volume; small teams see unreliable comparisons","Anonymization may be insufficient; reps can often identify peers by deal patterns or unique objection handling styles","Benchmarking assumes all reps sell the same product to similar prospects; differences in territory, product, or prospect type invalidate comparisons","No causal analysis; top performers may succeed despite their conversation patterns, not because of them (e.g., better territory, stronger product-market fit)"],"requires":["Minimum 10-15 reps with analyzed calls","Minimum 5-10 calls per rep to establish baseline patterns","Team-level account or admin access to view benchmarking data","Optional: deal outcome data to weight benchmarks by win rate"],"input_types":["aggregated conversation metrics across reps","deal outcomes (won/lost) per rep"],"output_types":["benchmarking reports (anonymized rep comparison)","best practice summaries (top performer patterns)","peer learning recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Call recordings in MP3, WAV, or M4A format (typical sales call recording tools)","Ability to upload files (web UI or API integration)","Minimum 3-5 call recordings to establish baseline patterns","Optional: CRM integration (Salesforce, HubSpot) to correlate call patterns with deal outcomes","Transcribed call recordings (audio or text)","Minimum 10 calls for statistically meaningful clustering","Optional: deal outcome data (won/lost) to correlate objections with conversion","Call transcripts with temporal markers (timestamps or call duration)","Deal stage metadata from CRM (Salesforce, HubSpot) OR manual stage labeling","Minimum 5-10 deals per stage to establish baseline patterns"],"failure_modes":["Requires consistent call recording and upload; sporadic data (< 5 calls/month) produces unreliable pattern detection","Transcription quality depends on audio clarity; background noise and multiple speakers degrade accuracy","Analysis is retrospective only — no real-time guidance during active calls","Unclear whether coaching adapts to different sales methodologies or enforces a single generic approach","Objection detection accuracy depends on transcript quality and context clarity; sarcasm, indirect objections, and cultural nuances may be misclassified","Requires minimum 10-15 calls to establish reliable objection patterns; early-stage reps see noisy results","Cannot distinguish between prospect objections and rep's own objection statements without semantic context","No built-in A/B testing framework to validate whether coaching on specific objections actually improves close rates","Requires CRM integration or manual deal stage labeling to map calls to stages; without this, stage detection is heuristic-based and error-prone","Coaching recommendations are descriptive (what you did) not prescriptive (what you should do); reps must interpret insights and self-coach","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.857Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=manja-ai","compare_url":"https://unfragile.ai/compare?artifact=manja-ai"}},"signature":"uE1PdJjz5sBI1+P7IG0uuGjj8V+wVW+AELBPpvrMfxa0Qi9D0h+/e7aMKb7tujpGcmJpBEoi2Nz2zBhTZ2WcBQ==","signedAt":"2026-06-22T00:16:35.911Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/manja-ai","artifact":"https://unfragile.ai/manja-ai","verify":"https://unfragile.ai/api/v1/verify?slug=manja-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}