Eleven days. That is the time from kickoff to production deployment for a document processing system that handles 50,000 documents per month, integrates with three existing enterprise systems, and replaced a manual workflow that was consuming 120 hours of staff time per week.
This was not a prototype. Not a demo. Not a “minimum viable product” that would need six months of hardening before it could handle real traffic. This was a production system with automated testing, monitoring, error handling, and the operational infrastructure to run reliably at scale.
This article walks through the engagement step by step, showing how HiVE, our High-Velocity Engineering methodology, operates in practice. The specifics have been composited from multiple real engagements to protect client confidentiality, but the numbers, the rhythm, and the methodology are real.
The Starting Point
The client, a mid-market financial services company, had a document processing workflow that was entirely manual. Customer submissions, contracts, verification documents, and compliance paperwork arrived through multiple channels, email, web upload, fax-to-digital, and a team of twelve document processors would read each document, extract relevant data, validate it against business rules, and enter it into the company’s systems of record.
The workflow consumed approximately 120 hours per week of staff time. Average processing time per document was 14 minutes. Error rates ran around 4.2%, which triggered downstream corrections that consumed additional time. The team processed roughly 12,000 documents per month but the volume was growing, and they were already behind.
The client had previously explored two approaches to solving this: a traditional software vendor that quoted an 8-month implementation timeline, and an off-the-shelf AI document processing tool that could handle basic extraction but could not apply the domain-specific business rules that made their compliance process work.
We proposed an 11-day HiVE engagement. Here is how it played out.
Day 1-2: Specification and Architecture
The first two days were entirely dedicated to specification development. No code was written. In a traditional engagement, this might look slow. In HiVE, it is the highest-leverage activity because the quality of the specification determines the quality and speed of everything that follows.
Day 1: Domain Immersion and Outcome Definition
We spent the full first day with the client’s document processing team, compliance officers, and system administrators. The goal was not to gather requirements in the traditional sense. It was to build a comprehensive domain model that would serve as the context layer for agent-driven development.
We documented:
- Every document type the system needed to handle (17 distinct types)
- The extraction rules for each document type (what data points matter and where they appear)
- The business validation rules (83 distinct rules spanning compliance, data quality, and cross-reference checks)
- The integration points with existing systems (a CRM, an underwriting platform, and a compliance database)
- The error handling requirements (what happens when a document is unreadable, incomplete, or fails validation)
- The operational requirements (processing latency targets, throughput minimums, uptime requirements)
We also defined the outcome metrics:
- Primary metric: Reduce average document processing time from 14 minutes to under 2 minutes
- Quality metric: Maintain or improve the 95.8% accuracy rate (reduce the 4.2% error rate)
- Throughput metric: Process current volume (12,000 documents/month) with headroom to 50,000
- Integration metric: Zero manual data entry into systems of record
On Day 2, we translated the domain model into formal specifications and architectural decisions.
The system architecture was defined as three services:
- Ingestion Service: Receives documents from all input channels, normalizes them to a common format, and queues them for processing.
- Processing Service: Applies document classification, data extraction, and business rule validation using a pipeline of specialized AI agents.
- Integration Service: Maps validated output to the schemas expected by downstream systems and handles the write operations.
For each service, we wrote specifications that included:
- Input/output contracts with exact data schemas
- Processing logic with pseudocode for complex business rules
- Error handling for every failure mode we identified
- Test cases covering happy paths, edge cases, and error conditions
The specification document was 47 pages. Forty-seven pages of precise, agent-consumable specifications written in two days. This is the kind of output that specification discipline produces when you have experienced Context Engineers who know how to extract domain knowledge and encode it formally.
We also defined the test infrastructure: 312 test cases across unit, integration, and end-to-end levels, with automated execution built into the delivery pipeline.
Day 1-2 by the numbers:
- 47 pages of formal specifications
- 312 defined test cases
- 83 business rules documented
- 17 document types mapped
- 3 integration points specified
Day 3-5: Core Implementation
With specifications in hand, agent-driven implementation began on Day 3.
Day 3: Ingestion Service and Processing Pipeline Foundation
Three agents worked in parallel, each operating against its section of the specification:
- Agent 1 built the Ingestion Service: file upload handlers, email ingestion adapter, format normalization, and queuing infrastructure.
- Agent 2 built the document classification pipeline: a multi-model approach that routes documents to the correct extraction agent based on document type.
- Agent 3 built the test harness and CI pipeline: automated test execution, deployment scripts, and the quality gate infrastructure.
Human engineers focused on architecture review, ensuring the agents’ implementations aligned with the system design, and on reviewing the first batch of generated code for pattern quality. We caught and corrected three architectural issues in Agent 2’s output before they propagated. This is the value of human oversight at Level 4-5 agentic development: catching structural issues early, when they are cheap to fix.
By end of Day 3, the Ingestion Service was passing all unit and integration tests. The classification pipeline was passing 89% of its test cases.
Day 4: Data Extraction and Business Rules
The processing pipeline came together on Day 4. Agents implemented:
- Data extraction logic for all 17 document types
- Business rule validation for all 83 rules
- Confidence scoring for extracted data (to flag low-confidence extractions for human review)
- Error handling and exception routing
This was the most specification-intensive day. The business rules were complex, with conditional logic, cross-reference checks, and regulatory compliance requirements that required precise implementation. The specifications written on Day 2 paid for themselves here. Agents consumed the formal rule definitions and produced correct implementations for 79 of 83 rules on the first pass. The four that required correction were edge cases involving cross-document validation that the specifications had not fully detailed. We updated the specs and the agents regenerated.
Day 5: Integration Service and End-to-End Testing
Day 5 focused on the Integration Service and end-to-end validation. Agents built:
- Schema mapping between the processing output and the three downstream system APIs
- Transaction handling for write operations (ensuring atomic updates across systems)
- Retry logic and dead-letter queuing for failed integrations
- End-to-end test scenarios that exercised the full pipeline from document ingestion to system-of-record update
By end of Day 5, the system was processing test documents through the full pipeline. End-to-end test pass rate was 94%. The failing 6% were primarily edge cases in document format handling that we scheduled for Day 6.
Day 3-5 by the numbers:
- 23,847 lines of application code generated and validated
- 8,412 lines of test code generated
- 291 of 312 test cases passing (94% pass rate)
- 79 of 83 business rules correct on first pass (95% first-pass accuracy)
- 3 architectural issues caught and corrected by human review
- 0 production-critical defects in reviewed code
Day 6-8: Hardening and Edge Cases
Day 6: Edge Case Resolution
The remaining 21 failing test cases were all edge cases: unusual document formats, malformed inputs, and rare business rule combinations. Agents addressed these with updated specifications, and human engineers reviewed each fix for correctness.
We also added a human-in-the-loop escalation path for documents that fell below the confidence threshold. Rather than rejecting these documents, the system routes them to a human reviewer with the AI’s extracted data pre-filled. The human reviews, corrects if necessary, and approves. This handles the long tail of document variation that no AI system will get right 100% of the time.
We ran load tests simulating 50,000 documents per month (the throughput target) and identified two bottlenecks:
- The document classification model was slower than required under concurrent load. We implemented a caching layer and batch processing optimization that brought latency within target.
- The Integration Service’s database write operations were creating lock contention at scale. We refactored to use asynchronous writes with idempotency guarantees.
After optimization, the system processed the full load test within the latency and throughput requirements with 40% headroom.
Day 8: Security Review and Compliance Verification
Human engineers conducted a security review covering:
- Authentication and authorization for all API endpoints
- Data encryption at rest and in transit
- PII handling compliance with the client’s regulatory requirements
- Input validation and injection prevention
- Dependency vulnerability scanning
The security review identified two issues: an overly permissive CORS configuration and a logging statement that included PII in debug mode. Both were corrected the same day.
Day 6-8 by the numbers:
- 312 of 312 test cases passing (100%)
- 83 of 83 business rules verified correct
- Load tested to 150% of target throughput
- 2 performance bottlenecks identified and resolved
- 2 security issues identified and resolved
- 0 high-severity defects remaining
Day 9-10: Deployment and Monitoring
Day 9: Staging Deployment and Integration Testing
The system was deployed to a staging environment connected to the client’s test instances of their CRM, underwriting platform, and compliance database. We ran a validation suite of 500 real historical documents (anonymized) through the system and compared outputs to the known-correct human-processed results.
Results: 97.3% accuracy on the 500-document validation set. The 2.7% discrepancy was analyzed case by case. 1.1% were cases where the AI system was actually correct and the historical human processing had errors. 1.6% were genuine AI errors, all of which fell below the confidence threshold and would have been routed to human review in production.
Adjusted accuracy: 98.4% for documents processed without human review. 100% for documents including the human-in-the-loop escalation path (because the escalation catches the sub-threshold cases).
Day 10: Production Deployment
Production deployment included:
- Blue-green deployment with instant rollback capability
- Monitoring dashboards tracking processing volume, latency, accuracy, error rates, and escalation rates
- Alerting configured for anomaly detection on all key metrics
- Runbooks for operational team covering common scenarios
The system went live processing real documents at 2:00 PM. By end of day, it had processed 847 documents with zero errors and average processing time of 1.3 minutes (vs. the 14-minute manual baseline).
Day 11: Validation and Handoff
The final day focused on operational validation and client handoff:
- Reviewed the first 24 hours of production metrics
- Conducted knowledge transfer sessions with the client’s operations and engineering teams
- Documented operational procedures, escalation paths, and maintenance requirements
- Delivered the complete specification library, test suite, and deployment infrastructure as client-owned assets
Final metrics:
- Average processing time: 1.3 minutes (from 14 minutes, a 91% reduction)
- Accuracy: 98.4% without human review, effective 100% with escalation path
- Throughput capacity: 50,000+ documents per month (from 12,000 manual capacity)
- Staff time recovered: approximately 110 of the previous 120 hours per week
- Total lines of production code: 32,259
- Total lines of test code: 11,847
- Total defects found in production during first month: 3 (all low severity, none affecting data accuracy)
- Time from kickoff to production: 11 working days
What Made This Possible
This timeline was not achieved by cutting corners. It was achieved by a methodology designed for AI-native delivery:
Specification discipline. Two full days of specification before any code. This investment paid for itself ten times over in agent execution quality and reduced rework.
Agent-driven implementation with human oversight. Agents handled the volume of code generation. Humans handled the judgment: architecture review, security assessment, business rule validation, and deployment decisions.
Automated quality gates. 312 test cases running continuously throughout development. Code that did not pass all gates did not advance. This caught issues in hours, not weeks.
Outcome-oriented measurement. From Day 1, we knew what success looked like in measurable terms. Every decision during the engagement was evaluated against those metrics.
Experienced team. A four-person team of senior engineers with deep domain context skills. Not a large team. A precise one.
This is HiVE in practice. Not theory. Not aspiration. An operational methodology that produces production systems in days. The specifications make it possible. The agents make it fast. The guardrails make it safe. The outcome metrics make it accountable.
Eleven days. Production-grade. Measured results. That is what AI-native delivery looks like when the methodology is right.
