From prompts to production partnership intelligence.
Monday: 3 core prompts for discovery, tracking, and contract analysis. Tuesday: automated deal flow agents. Wednesday: cross-functional team workflows. Thursday: complete system architecture with multi-agent orchestration, ML pipelines, and enterprise scaling patterns. This is how you build partnership systems that handle thousands of deals simultaneously.
Key Assumptions
- •Managing 10-1,000 active partnerships across discovery, negotiation, execution phases
- •Integration with existing CRM (Salesforce, HubSpot, or custom) and document systems (DocuSign, Box)
- •Need for real-time deal intelligence and automated contract review
- •Compliance with SOC2, data residency requirements for enterprise customers
- •Team of 5-50 BD professionals using the system daily
System Requirements
Functional
- Partner Discovery: Search 100K+ companies, score fit, enrich profiles
- Deal Tracking: Automated pipeline updates, stage progression, activity logging
- Contract Intelligence: Extract terms, flag risks, compare to playbook
- Analytics Dashboard: Deal velocity, conversion rates, partnership ROI
- Integration Hub: Bi-directional sync with CRM, Slack notifications, DocuSign webhooks
- Collaboration Tools: Shared notes, @mentions, approval workflows
- Document Management: Version control, redlining, clause library
Non-Functional (SLOs)
💰 Cost Targets: {"per_partnership_month_usd":12,"per_contract_analysis_usd":0.5,"per_discovery_search_usd":0.08}
Agent Layer
planner
L4Decomposes user requests into agent tasks, selects tools, orchestrates workflow
🔧 task_decomposer, tool_selector, dependency_resolver
⚡ Recovery: Retry with simplified plan, Escalate to human if 3 retries fail
discovery_executor
L3Searches company databases, scores fit, enriches profiles
🔧 company_search_api, scoring_model, enrichment_api
⚡ Recovery: Cache partial results, Fallback to rule-based scoring if ML fails, Queue for retry if API down
contract_analyzer
L3Extracts terms from contracts, flags risks, compares to playbook
🔧 pdf_parser, llm_extractor, rule_matcher, risk_classifier
⚡ Recovery: OCR fallback if PDF parsing fails, Human review queue for low-confidence extractions, Partial extraction if timeout
deal_tracker
L2Monitors deal pipeline, updates stages, triggers notifications
🔧 crm_sync, stage_transition_logic, notification_service
⚡ Recovery: Queue updates if CRM unavailable, Eventual consistency model, Manual override option
evaluator
L2Validates agent outputs, checks quality, enforces business rules
🔧 confidence_checker, rule_validator, consistency_checker
⚡ Recovery: Flag for human review if validation fails, Retry upstream agent with feedback, Accept with warning if non-critical
guardrail
L1Policy enforcement, PII redaction, safety filters, compliance checks
🔧 pii_detector, policy_engine, content_filter
⚡ Recovery: Block processing if critical violation, Log and alert security team, Quarantine data for review
ML Layer
Feature Store
Update: Daily batch + real-time for critical features
- • company_size_bucket
- • industry_match_score
- • recent_funding_amount
- • tech_stack_overlap
- • geographic_fit
- • deal_velocity_30d
- • contract_risk_history
- • partner_engagement_score
Model Registry
Strategy: Semantic versioning with blue-green deployment
- • partner_fit_scorer
- • contract_risk_classifier
- • deal_stage_predictor
- • embedding_model
Observability
Metrics
- 📊 api_request_latency_p95_ms
- 📊 agent_execution_time_ms
- 📊 llm_call_latency_ms
- 📊 contract_extraction_accuracy
- 📊 partner_discovery_precision
- 📊 crm_sync_success_rate
- 📊 queue_depth
- 📊 error_rate_per_endpoint
- 📊 cost_per_partnership_usd
Dashboards
- 📈 ops_dashboard
- 📈 ml_performance_dashboard
- 📈 cost_tracking_dashboard
- 📈 business_metrics_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Serverless-first (Vercel + AWS Lambda)
- • Managed services (RDS, ElastiCache, Pinecone)
- • Direct LLM API calls (Anthropic, OpenAI)
- • Simple auth (Auth0 free tier)
- • CloudWatch for monitoring
→ Focus on speed to market
→ Single-tenant architecture
→ Manual scaling adjustments
→ Cost: $200-800/month
→ Suitable for 0-500 partnerships/month
🏢 Enterprise
Infrastructure:
- • Kubernetes on EKS/GKE
- • Multi-region deployment (US, EU)
- • Private networking (VPC, VPN, PrivateLink)
- • BYO KMS/HSM for encryption
- • SSO/SAML integration
- • Dedicated tenants with row-level security
- • Replicated databases with failover
- • Custom SLAs (99.95% uptime)
→ Multi-tenant with strict isolation
→ Data residency compliance (GDPR, etc.)
→ SOC2 Type II certified
→ Audit logs with 7-year retention
→ Cost: $10K+/month
→ Suitable for 5,000+ partnerships/month
📈 Migration: Start with startup stack. At 500 partnerships/month, migrate to Kubernetes with blue-green deployment. Introduce multi-tenancy at 1,000 partnerships. Add regional deployments at 5,000 partnerships. Full enterprise features by month 6-9.
Risks & Mitigations
⚠️ LLM hallucination in partner data
Medium✓ Mitigation: Multi-layer validation: confidence scores, external API cross-check, human review queue for low-confidence. Track hallucination rate <1%.
⚠️ CRM integration failures
Medium✓ Mitigation: Retry logic with exponential backoff, queue for eventual consistency, alerting for prolonged failures. SLA: 99% sync success.
⚠️ PII leakage to LLM providers
Low✓ Mitigation: PII detection and redaction before LLM calls, audit all LLM requests, use private LLM deployments for sensitive data.
⚠️ Cost overruns from LLM usage
High✓ Mitigation: Rate limiting per user, caching of LLM responses, smaller models for simple tasks, cost alerts at 80% budget.
⚠️ Model drift in partner scoring
Medium✓ Mitigation: Weekly performance monitoring, automated retraining pipeline, A/B testing before deployment, rollback if accuracy drops >5%.
⚠️ Vendor lock-in (CRM, LLM providers)
High✓ Mitigation: Abstraction layers for CRM and LLM APIs, multi-provider support, regular migration testing, data export capabilities.
⚠️ Scaling bottlenecks at 1K+ partnerships
Medium✓ Mitigation: Load testing at 2x expected volume, database sharding plan, async processing for heavy tasks, auto-scaling infrastructure.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Months 0-3- → Launch core partner discovery and deal tracking
- → Integrate with primary CRM (Salesforce or HubSpot)
- → Basic contract analysis (top 10 terms)
- → Support 10-50 partnerships/month
Phase 2: Scale & Intelligence (3-6 months)
Months 3-6- → Add ML-powered partner scoring
- → Expand contract analysis to 47 terms
- → Implement multi-agent orchestration
- → Support 50-500 partnerships/month
- → Add Slack and email integrations
Phase 3: Enterprise & Optimization (6-12 months)
Months 6-12- → Multi-tenancy with tenant isolation
- → SOC2 compliance and audit readiness
- → Support 500-5,000+ partnerships/month
- → Add DocuSign integration for e-signatures
- → Implement agentic RAG for semantic search
Complete Systems Architecture
9-layer architecture from user interface to security
Sequence Diagram - Contract Analysis Flow
Partnership Management - Hub Orchestration
6 ComponentsPartnership Management - Feedback & Collaboration Network
6 ComponentsData Flow - End-to-End
From partner search to contract signing
Scaling Patterns
Key Integrations
Salesforce CRM
HubSpot CRM
DocuSign
Company Data APIs (Clearbit, Apollo)
Slack
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (Anthropic/OpenAI) | Failover to secondary LLM provider (Gemini/DeepSeek) | Slight quality degradation, no downtime | 99.9% (multi-provider redundancy) |
| Contract extraction low confidence (<0.7) | Route to human review queue | Delayed processing (SLA: 4 hours) | 99.5% accuracy maintained |
| CRM API timeout (Salesforce) | Retry 3x with exponential backoff → Queue for later | Eventual consistency (sync within 15 min) | 99.0% sync success |
| Database unavailable | Read from replica, queue writes | Read-only mode, writes delayed | 99.5% availability |
| Agent execution timeout (>30 sec) | Cancel task, log error, notify user | User retries manually | 95% completion within 30 sec |
| PII detected in LLM output | Block output, redact, log security event | User sees redacted response | 100% PII blocked |
| Vector DB query timeout | Fallback to keyword search | Lower search quality | 99.0% semantic search uptime |
Multi-Agent Architecture
How 6 specialized agents collaborate autonomously
┌──────────────┐
│ Planner │ ← Orchestrates workflow
│ Agent │
└──────┬───────┘
│
┌───┴────┬─────────┬──────────┬──────────┐
│ │ │ │ │
┌──▼──┐ ┌─▼───┐ ┌──▼────┐ ┌──▼────┐ ┌─▼────┐
│Disc-│ │Deal │ │Contract│ │Eval- │ │Guard-│
│overy│ │Track│ │Analyzer│ │uator │ │rail │
│Exec │ │er │ │ │ │ │ │ │
└──┬──┘ └──┬──┘ └───┬────┘ └───┬───┘ └──┬───┘
│ │ │ │ │
└────────┴─────────┴───────────┴─────────┘
│
┌─────▼─────┐
│ External │
│ Systems │
│(CRM, APIs)│
└───────────┘