From prompts to production sales platform.
Monday: 3 core prompts for deal intelligence, email enrichment, and pipeline analysis. Tuesday: automated code with Salesforce sync. Wednesday: team workflows across sales, ops, and leadership. Thursday: complete technical architecture with AI agents, real-time CRM integration, and enterprise-grade security. Scale from 10 to 10,000 deals/month.
Key Assumptions
System Requirements
Functional
- Deal intelligence: score deals, identify risks, suggest next actions
- Email enrichment: extract contacts, companies, sentiment from threads
- Pipeline analysis: forecast close dates, revenue, and churn risk
- CRM sync: bidirectional updates with conflict resolution
- Analytics: dashboards for win rates, deal velocity, rep performance
- Notifications: Slack/email alerts for high-value deals and risks
- Search: semantic search across deals, emails, and notes
Non-Functional (SLOs)
💰 Cost Targets: {"per_deal_usd":0.15,"per_email_enrichment_usd":0.05,"per_user_per_month_usd":50}
Agent Layer
planner
L4Decomposes sales tasks into subtasks, selects appropriate agents and tools
🔧 CRM metadata API, User permission service, Task decomposition LLM
⚡ Recovery: If decomposition fails, fall back to single-agent execution, If tool unavailable, suggest manual workflow, Retry with simplified plan (max 2 retries)
deal_scorer
L3Scores deals based on historical data, engagement signals, and market context
🔧 ML scoring model (gradient boosting), Feature store (deal velocity, engagement score), Salesforce API (historical win rates)
⚡ Recovery: If ML model fails, fall back to rule-based scoring, If feature store unavailable, use cached features (max 1 hour old), If confidence < 0.6, flag for human review
email_enricher
L2Extracts contacts, companies, sentiment, and action items from email threads
🔧 Gmail/Outlook API, NER model (spaCy + custom), Sentiment classifier, Clearbit API (company enrichment)
⚡ Recovery: If NER fails, extract emails via regex, If sentiment model unavailable, default to neutral (0), If Clearbit rate-limited, queue for later enrichment
pipeline_forecaster
L3Predicts quarterly revenue, close dates, and churn risk using time-series models
🔧 Time-series model (Prophet or ARIMA), Feature store (deal velocity, stage duration), Salesforce reports API
⚡ Recovery: If model training fails, use last known good model, If data insufficient (<3 months), return wide confidence interval, If prediction outlier, flag for manual review
evaluator
L3Validates outputs from other agents, checks quality, detects hallucinations
🔧 Validation rules engine, Cross-reference with CRM data, Hallucination detector (consistency checks)
⚡ Recovery: If validation fails, route to human review queue, If ground truth unavailable, use heuristic checks, If quality < 0.7, block output and retry
guardrail
L4Enforces policies, redacts PII, checks for sensitive data leakage
🔧 PII detection (AWS Comprehend or custom NER), Policy engine (OPA or custom rules), Audit logger
⚡ Recovery: If PII detection fails, block output entirely (fail-safe), If policy check times out, apply strictest policy, If audit log unavailable, queue for later write
ML Layer
Feature Store
Update: Real-time for engagement metrics, daily batch for historical aggregates
- • deal_velocity (days in current stage)
- • engagement_score (email replies, meeting attendance)
- • contact_seniority (VP+, C-level, IC)
- • company_size (employees, revenue)
- • historical_win_rate (by rep, by industry)
- • email_sentiment_trend (last 30 days)
- • competitor_mentions (count, sentiment)
- • stage_duration_percentile (vs historical)
Model Registry
Strategy: Semantic versioning with A/B testing (10% traffic to new model)
- • deal_scorer_v3
- • email_sentiment_classifier
- • pipeline_forecaster
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM hallucinations lead to incorrect deal scores
Medium✓ Mitigation: 4-layer validation (confidence, cross-reference, consistency, human review). Shadow mode testing before production. Hallucination rate <1%.
⚠️ Salesforce API rate limits exceeded during peak hours
High✓ Mitigation: Request queue with priority (high-value deals first). Retry with exponential backoff. Cache frequently accessed data in Redis. Negotiate higher rate limits with Salesforce.
⚠️ PII leakage to LLM providers
Low✓ Mitigation: PII detection before every LLM call (fail-safe). Redaction service with 99.9% precision. Audit logs for all LLM requests. BYO keys for enterprise customers.
⚠️ Model drift reduces scoring accuracy over time
High✓ Mitigation: Weekly drift monitoring (KL divergence, rolling RMSE). Automated retraining pipeline. A/B testing before rollout. Human review for low-confidence predictions.
⚠️ Multi-tenant data leakage (Org A sees Org B's deals)
Low✓ Mitigation: Org-level sharding in database. Row-level security (RLS) policies. API gateway enforces org_id filtering. Quarterly penetration testing. Dedicated VPCs for enterprise.
⚠️ Email sync fails silently, missing critical deal updates
Medium✓ Mitigation: Webhook health checks every 5 minutes. Alerting if no emails received in 1 hour. Manual re-sync option in UI. Backup polling mechanism (hourly) if webhooks fail.
⚠️ Cost overruns from excessive LLM API calls
Medium✓ Mitigation: Per-org monthly token budget. Rate limiting (10 scores/min per user). Cache LLM responses (1-hour TTL). Cost dashboard with alerts at 80% of budget.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from presentation to security
Presentation
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
6 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Sequence Diagram - Deal Scoring Flow
Automated data flow every hour
Data Flow - Deal Scoring Workflow
Sales rep request → Salesforce update in 2.1 seconds
Key Integrations
Salesforce CRM
Gmail/Outlook Email APIs
Slack Notifications
Clearbit Enrichment
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Salesforce API down | Queue updates in Redis, retry with exponential backoff (max 5 attempts) | Delayed sync (up to 30 min), no data loss | 99.5% |
| LLM API rate limit exceeded | Switch to backup LLM provider (Anthropic → OpenAI → Azure), queue requests if all exhausted | Increased latency (2-5s), no functionality loss | 99.9% |
| Deal scoring model returns low confidence (<0.6) | Route to human review queue, use rule-based scoring as interim | Manual review required, slower turnaround (hours vs seconds) | 95% |
| Email enrichment fails (NER model error) | Extract emails via regex, skip company enrichment, flag for manual review | Reduced data quality, no blocking errors | 99% |
| Database primary node failure | Promote read replica to primary (30s failover), redirect traffic | 30s downtime, read-only mode during failover | 99.95% |
| PII detection service unavailable | Block all LLM calls (fail-safe), queue requests for later processing | Degraded functionality, no PII leakage risk | 100% (safety first) |
| Feature store cache miss (Redis down) | Query PostgreSQL directly (slower), rebuild cache on recovery | Increased latency (500ms → 2s), no data loss | 99% |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Model Drift Detection
Tech Stack Summary
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