From prompts to production grant system.
Monday: 3 core prompts (Opportunity Finder, Application Builder, Deadline Tracker). Tuesday: automation code with LangGraph. Wednesday: team workflows (Grant Writer, Development Director, Executive Director). Thursday: complete technical architecture. Multi-agent orchestration, document management, deadline tracking, and scaling to 10,000 applications/month.
Key Assumptions
- •Process 10-10,000 grant applications per month
- •Average application: 15 pages, 8 attachments, 30-day deadline
- •Integration with 3-5 grant databases (GrantStation, Foundation Directory, Candid)
- •Document storage: 50GB-5TB (startup to enterprise)
- •SOC2 Type II compliance required for enterprise tier
- •Multi-tenant with org-level data isolation
- •99.5% uptime SLA for grant submission deadlines
System Requirements
Functional
- Opportunity discovery: Search 100K+ grants, match to org profile
- Application generation: Auto-fill from templates, org data, past wins
- Deadline tracking: Calendar sync, 7/3/1-day reminders, priority scoring
- Document management: Version control, template library, attachment handling
- Collaboration: Multi-user editing, comment threads, approval workflows
- Reporting: Win rate, ROI per grant, time saved vs manual
- Integration: Grant databases, Google Workspace, Microsoft 365, Salesforce
Non-Functional (SLOs)
💰 Cost Targets: {"per_application_usd":2.5,"per_user_per_month_usd":50,"storage_per_gb_per_month_usd":0.15}
Agent Layer
planner
L4Decomposes high-level tasks (create application) into steps (search → draft → review → submit)
🔧 OpportunityAgent.search, WriterAgent.generate, EvaluatorAgent.validate
⚡ Recovery: If tool fails: retry 3x with backoff, If unrecoverable: route to human queue, Log failure context for debugging
executor
L3Orchestrates workflow execution, manages state, handles loops (search → refine → search)
🔧 All domain agents (Opportunity, Writer, Deadline), Database operations, External API calls
⚡ Recovery: Checkpoint state after each step, Resume from last checkpoint on failure, Timeout protection (max 5 min per step)
evaluator
L2Quality checks: completeness, coherence, compliance with grant requirements
🔧 GPT-4 for coherence check, Rule-based validator for completeness, Similarity scorer vs past wins
⚡ Recovery: If LLM fails: fall back to rule-based checks, If score < 70: flag for human review, Log evaluation criteria and results
guardrail
L2Policy enforcement: PII redaction, compliance checks, safety filters
🔧 AWS Comprehend (PII detection), Custom rule engine (policy checks), Blocklist matcher (prohibited terms)
⚡ Recovery: If PII detection fails: block processing (fail-safe), If rule engine fails: route to manual review, Log all violations for audit
opportunity
L3Search 100K+ grants, rank by fit score, return top 10 matches
🔧 Grant DB APIs (GrantStation, Candid), Vector DB (semantic search), Ranking model (fine-tuned classifier)
⚡ Recovery: If API down: fall back to cached results, If no matches: broaden search criteria, Log search params and results
writer
L3Generate 15-page grant application from template, org data, and past wins
🔧 GPT-4 or Claude (long-context generation), RAG system (retrieve relevant past content), Template engine (fill placeholders)
⚡ Recovery: If LLM fails: retry with shorter context, If output truncated: chunk and merge, Save partial drafts for recovery
deadline
L2Track deadlines, send reminders (7/3/1 days), prioritize by urgency × amount
🔧 Calendar API (Google, Outlook), Email service (SendGrid), SMS service (Twilio)
⚡ Recovery: If email fails: retry 3x, then SMS, If calendar sync fails: log and notify admin, Queue reminders for retry
ML Layer
Feature Store
Update: Daily batch + real-time on new application
- • org_past_win_rate (rolling 12mo)
- • grant_fit_score (semantic similarity)
- • funder_relationship_strength (interaction history)
- • budget_alignment (org budget vs grant amount)
- • deadline_urgency (days remaining)
- • application_quality_score (historical avg)
Model Registry
Strategy: Semantic versioning (major.minor.patch), A/B test new versions
- • grant_ranker_v3
- • gpt4_writer
- • claude_evaluator
Observability
Metrics
- 📊 application_generation_time_sec
- 📊 llm_latency_p95_ms
- 📊 grant_search_success_rate
- 📊 quality_score_distribution
- 📊 deadline_reminder_delivery_rate
- 📊 api_error_rate
- 📊 cost_per_application_usd
- 📊 win_rate_percent
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 business_metrics_dashboard
- 📈 cost_tracking_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Vercel/Netlify (frontend)
- • Serverless functions (backend)
- • Managed PostgreSQL (Supabase/Neon)
- • Redis Cloud (cache)
- • S3 (documents)
- • OpenAI API (LLMs)
→ Single-tenant (1 org per deployment)
→ No VPC, public endpoints
→ Shared infrastructure
→ Quick to deploy (< 1 week)
→ Cost: $100-500/month
→ Good for MVP, up to 500 apps/month
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS/GKE) in VPC
- • Multi-region deployment
- • Private PostgreSQL (RDS/Cloud SQL)
- • Redis Cluster (ElastiCache)
- • S3/GCS with customer-managed keys
- • Multi-LLM failover (GPT-4 + Claude + Gemini)
- • Dedicated vector DB (Pinecone Enterprise)
- • WAF + DDoS protection
→ Multi-tenant with org-level isolation
→ VPC peering, private networking
→ BYO KMS/HSM for encryption
→ SSO/SAML integration
→ Audit logging (7-year retention)
→ Data residency (EU/US/APAC)
→ 99.9% SLA
→ Cost: $8,000+/month
→ Supports 10,000+ apps/month
📈 Migration: Phase 1: Migrate DB to RDS/Cloud SQL. Phase 2: Deploy K8s cluster, run parallel. Phase 3: Cutover DNS, decommission serverless. Phase 4: Add multi-region, SSO, audit logging.
Risks & Mitigations
⚠️ LLM hallucinations (fake grants, incorrect data)
Medium✓ Mitigation: 4-layer detection: confidence scores, cross-reference DB, logical checks, human review. 0.5% rate, 100% caught.
⚠️ Grant DB API rate limits or downtime
Medium✓ Mitigation: 24-hour cache, multi-provider failover, rate limiting (10 req/sec), exponential backoff.
⚠️ PII leakage to LLM providers
Low✓ Mitigation: Redact PII before LLM call (AWS Comprehend), audit logs, fail-safe if detection fails.
⚠️ Deadline missed due to system failure
Low✓ Mitigation: 99.9% SLA, multi-channel reminders (email + SMS), auto-failover, 7/3/1-day alerts.
⚠️ Cost overrun (LLM API costs)
Medium✓ Mitigation: Per-org quotas, cost tracking dashboard, alerts at 80% budget, auto-throttle at 100%.
⚠️ Low-quality applications (poor win rate)
Medium✓ Mitigation: Quality scoring (< 70 = refine), human review queue, A/B testing new prompts, quarterly retraining.
⚠️ Data loss (document storage failure)
Low✓ Mitigation: S3 versioning, cross-region replication, daily backups, 30-day retention, disaster recovery plan.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Weeks 1-12- → Launch core features: Opportunity Finder, Application Builder, Deadline Tracker
- → Support 10-50 applications/month
- → Single-tenant deployment
- → Serverless architecture
Phase 2: Scale (3-6 months)
Weeks 13-26- → Scale to 500 applications/month
- → Add multi-agent orchestration
- → Improve quality with RAG
- → Add collaboration features
Phase 3: Enterprise (6-12 months)
Weeks 27-52- → Scale to 10,000+ applications/month
- → Multi-tenant with org isolation
- → SOC2 compliance
- → 99.9% SLA
Complete Systems Architecture
9-layer architecture from user interface to external integrations
Sequence Diagram - Grant Application Flow
Grant Application System - Hub Orchestration
7 ComponentsGrant Application System - Iterative Refinement Mesh
7 ComponentsData Flow - End-to-End Application Creation
From user request to submitted application in 3 minutes
Scaling Patterns
Key Integrations
Grant Databases (GrantStation, Candid, Foundation Directory)
Document Storage (Google Drive, Dropbox, SharePoint)
Calendar Sync (Google Calendar, Outlook)
CRM (Salesforce, HubSpot)
Email/SMS (SendGrid, Twilio)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| OpenAI API down | → Switch to Claude API (multi-LLM failover) | Degraded (different model), not broken | 99.5% (multi-provider resilience) |
| Grant DB API timeout | → Serve cached results (24h cache) | Stale data (up to 24h old) | 99.0% (cache hit rate 80%) |
| WriterAgent generates low-quality draft (score < 70) | → Loop back with feedback, max 3 iterations | Higher latency (3-5 min vs 2 min) | 99.9% (quality maintained) |
| PII detection service fails | → Block processing (fail-safe) | Application creation blocked | 100% (safety first) |
| PostgreSQL primary down | → Promote read replica to primary | 5-10 sec downtime | 99.9% (auto-failover) |
| Document upload fails (S3 error) | → Retry 3x, then queue for later | Delayed upload (eventual consistency) | 99.5% |
| Deadline reminder not sent (email service down) | → Retry email 3x, then send SMS | Delayed reminder (< 1 hour) | 99.9% (multi-channel) |
Multi-Agent Architecture
7 specialized agents collaborating autonomously
┌─────────────┐
│ Planner │ ← Decomposes tasks
└──────┬──────┘
│
┌───┴────┬─────────┬──────────┬──────────┐
│ │ │ │ │
┌──▼──┐ ┌─▼──┐ ┌───▼────┐ ┌──▼────┐ ┌─▼─────┐
│Oppor│ │Writer│ │Deadline│ │Eval │ │Guard │
│tunity│ │Agent │ │ Agent │ │Agent │ │rail │
└──┬──┘ └─┬───┘ └───┬────┘ └──┬───┘ └─┬─────┘
│ │ │ │ │
└────────┴──────────┴──────────┴────────┘
│
┌────▼─────┐
│ Executor │ ← Orchestrates workflow
└──────────┘Agent Collaboration Flow
Reactive Agent
Reflexive Agent
Deliberative Agent
Orchestrator Agent
Levels of Autonomy
Advanced ML/AI Patterns
Production ML engineering for grant writing systems