Pre-purchase AI-readiness diagnostic that kills "pilot purgatory" before it starts
Pilot purgatory is the pattern where enterprises buy expensive AI software, fail to get it into production, and never identify why. Teams discover hidden schema inconsistencies, pipeline fragility, and missing middleware after signing the contract — when it's too late to back out and too expensive to fix properly.
Preflight solves this by stress-testing your actual enterprise systems before you commit, turning "are we ready?" from an opinion into a measured report.
Enterprise AI deployments fail not because the AI doesn't work, but because enterprise systems aren't ready:
- Hidden Schema Chaos: Customer data is modeled differently across ERP, CRM, and warehouse
- Pipeline Fragility: Existing data flows break under AI agent query volumes
- Middleware Surprises: Required integration layer discovered mid-pilot
- Optimistic Demos: Vendor POCs run on clean sample data, not your messy reality
- No Objective Baseline: Buyers have no vendor-independent readiness assessment
Result: Blown budgets, stalled pilots, and projects that quietly die after the license is paid.
- Secure connectors to your ERP, CRM, database, and warehouse
- Customer-controlled credentials with least-privilege access
- Optional VPC/self-hosted deployment for maximum security
- Describe your intended AI deployment (use case, systems, query volume)
- Diagnostic agents simulate that workload against your real systems
- Stress-test pipelines and discover breaking points
- Map business entities across all connected systems
- Detect schema mismatches that would break cross-system AI agents
- Identify middleware gaps and pipeline bottlenecks
- Single readiness score: Go / Not Yet / Not Ready
- Prioritized remediation backlog with effort estimates
- Executive summary for decision-makers
- Interactive cost modeling for different scenarios
┌─────────────────────┐
│ READINESS SCORE: 67%│
│ VERDICT: NOT YET │
└─────────────────────┘
Critical Issues Found:
├── Customer ID mismatch (ERP vs CRM)
├── Pipeline latency spike at 50+ QPS
└── Missing order history integration
Estimated Remediation: 8-12 weeks
- Schema Inconsistencies: Entity mapping conflicts ranked by impact
- Pipeline Stress Points: Throughput limits and latency spikes
- Middleware Requirements: Integration layer gaps with effort estimates
- Data Quality Issues: Hygiene problems that would break AI agents
- Cost of hidden integration work (vs. original budget)
- Timeline impact of remediation
- Risk assessment and mitigation priorities
- Vendor-independent perspective
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Enterprise │────▶│ Diagnostic │────▶│ Analysis │
│ Connectors │ │ Agents │ │ Engine │
│ (Read-Only) │ │ (Simulation) │ │ (Gap Finding) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Reporting │◀────│ Remediation │◀────│Schema Consistency│
│ Engine │ │ Planner │ │ Analyzer │
└─────────────────┘ └─────────────────┘ └─────────────────┘
- Core Engine: Python (data analysis, schema mapping)
- Connectors: Enterprise SDKs and REST APIs
- Frontend: React dashboard with D3.js visualizations
- Database: PostgreSQL with time-series extensions
- Deployment: Docker/Kubernetes, VPC-ready
- Security: OAuth2, least-privilege access, encryption
- CIO / VP of Data & Platform / Head of Enterprise Architecture
- Program Owner accountable for AI initiative success
- Procurement/Finance teams de-risking large software spends
- Pre-purchase Due Diligence: Before signing AI/ML platform contracts
- Pilot Planning: Scoping integration work for AI deployments
- Vendor Evaluation: Objective comparison of integration requirements
- Budget Planning: Realistic effort estimates for AI readiness work
# Python 3.9+
python --version
# Docker (for containerized deployment)
docker --version
# Enterprise system credentials (read-only)# Clone repository
git clone https://github.com/marcuspat/preflight-integration-tester.git
cd preflight-integration-tester
# Install dependencies
pip install -r requirements.txt
# Configure enterprise connections
cp config.example.yml config.yml
# Edit config.yml with your system credentials
# Run diagnostic
python preflight.py --config config.yml --use-case "customer-service-ai"
# View report
open reports/readiness-assessment.html# VPC deployment
docker build -t preflight .
docker run -p 8080:8080 \
-v /path/to/config:/app/config \
-v /path/to/reports:/app/reports \
preflight
# Or use Kubernetes manifests
kubectl apply -f k8s/- Read-Only Access: Never requests write permissions to any system
- Customer-Controlled: All credentials managed by customer
- Data Isolation: Optional VPC deployment keeps data in your environment
- Encryption: All data encrypted in transit and at rest
- Audit Logging: Full activity log for compliance requirements
- Standards: SOC2 Type II, GDPR compliant
- SAP (S/4HANA, ECC)
- Oracle ERP Cloud
- Microsoft Dynamics 365
- NetSuite
- Workday
- Salesforce
- HubSpot
- Microsoft Dynamics CRM
- Pipedrive
- Zoho
- Snowflake
- Databricks
- Amazon Redshift
- Google BigQuery
- Azure Synapse
- PostgreSQL, MySQL, SQL Server
- Oracle Database
- MongoDB, Cassandra
- Redis, Elasticsearch
- Product Requirements Document
- Enterprise Connectors Guide (Coming Soon)
- Security & Compliance (Coming Soon)
- Deployment Guide (Coming Soon)
- API Reference (Coming Soon)
- Project setup and architecture
- Basic ERP + CRM + warehouse connectors
- Schema consistency analysis
- Static readiness report
- Manual configuration
- Pipeline stress testing
- Middleware gap estimation
- Interactive scenario modeling
- Self-service connection wizard
- Executive reporting
- Expanded connector library (20+ systems)
- Continuous re-assessment
- Peer benchmarking (anonymized)
- Automated remediation recommendations
- API marketplace integration
- Real-time monitoring
- Predictive gap analysis
- Integration marketplace
- White-label deployment
"We spent 8 months and $2M trying to deploy our customer service AI. The vendor demo worked perfectly, but our ERP and CRM had different customer IDs. We discovered this 6 months in." — VP of Customer Operations, Fortune 500 Retailer
"Preflight found 12 critical integration gaps in 3 days. We spent 2 months fixing them before buying anything. The AI deployment took 6 weeks instead of 8 months." — CTO, Manufacturing Company
- $25k - $75k per assessment
- Delivered in 1-2 weeks
- Includes remediation roadmap
- Money-back guarantee if major gaps missed
- $200k - $500k per year
- Unlimited assessments
- Continuous monitoring
- Priority support and custom connectors
ROI: Typical enterprise AI licenses cost $500k - $5M annually. Preflight costs 5-15% of that but prevents 50-80% of failed deployments.
We welcome contributions from enterprise architecture and integration experts!
Priority areas:
- Additional enterprise system connectors
- Schema mapping algorithms
- Load testing methodologies
- Reporting and visualization
- Security and compliance features
See CONTRIBUTING.md for guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
- Sales Inquiries: sales@preflight.ai
- Technical Support: support@preflight.ai
- Issues: GitHub Issues
- Discussions: GitHub Discussions
Built with insights from:
- Enterprise architects who've seen AI deployments succeed and fail
- Integration specialists dealing with legacy system complexity
- Procurement teams burned by optimistic vendor estimates
Don't let pilot purgatory kill your AI initiative. Know before you buy.