VANCO
From Ad-Hoc AI to Enterprise Operating Model
- Brownfield Analysis Engine for Legacy Codebases
- Role-Specific & On-Demand Playbooks (30 Total)
- Context Packs for AI-Aligned Code Generation
- SDLC Transformation Blueprint (AS-IS → TO-BE)
- Story & Requirements System with Gherkin ACs
- AI-Augmented Testing (Unit, Integration, E2E)
- Cursor Rules, Templates & Reference Implementation
HATCHWORKS TEAM
Automation Architects, AI Engineers, Product Leaders, QA Engineers, and SDLC Transformation Specialists
Overview
Vanco Payment Solutions engaged HatchWorks AI across two phases to transform how their engineering organization builds and ships software.
What began as a GenDD training program for 180 people evolved into a full SDLC scaffolding engagement that produced 41 production-ready deliverables, an enterprise operating model for AI-augmented software development.
The Challenge
Vanco had already proven that generative AI could accelerate individual tasks.
Engineers were using GitHub Copilot. Isolated experiments were showing promise.
But scaling AI across a complex engineering organization with multiple product lines, legacy systems, and distributed teams required something fundamentally different: consistency, governance, and repeatable workflows, not one-off prompt hacks.
What Vanco Was Up Against
- A fragmented technology landscape. Vanco's codebase was roughly 70% legacy and 30% modern, spanning .NET Framework, Web Forms, Python 2.7, GoLang, and significant business logic embedded in SQL stored procedures. Recent M&A activity (the ACS Technologies acquisition) added further fragmentation.
- Tribal knowledge as the bottleneck. Architecture and system behavior lived in the heads of senior engineers, not in documentation. New developers needed months to become productive. Legacy codebases had little to no navigable documentation.
- Upstream quality gaps cascading downstream. Fewer than 15% of user stories used the acceptance criteria field effectively. Requirements entered development as ambiguous bullet points, and QA became a downstream safety net — discovering requirements rather than validating them. An estimated 10–15% of active work was rework or reversions.
- Ungoverned AI usage creating new risks. GitHub Copilot was accelerating code generation, but without governance it shifted bottlenecks downstream — inconsistent pull requests, increased review burden, and no way to ensure generated code matched Vanco's architectural patterns or security requirements.
- Testing as a safety net, not a quality system. Test automation coverage was approximately 20%. QA teams were catching basic functional issues that should have been prevented upstream. Automation deferral was common with no tracking of reasons.
We wanted repeatability and oversight. HatchWorks AI helped us build an approach that scales responsibly across teams. This work gave us a safer, more standardized way to apply GenAI in delivery.
The Journey
- Phase 1: GenDD Training & Workshop
- Building fluency and demand across the organization
- 180 attendees trained across 12 sessions and 16 hours of instruction
- 8 role-specific tracks covering developers, architects, product owners, QA engineers, and more
- 6 tools covered hands-on, including Cursor and N8n
- 4 practical projects completed during the sessions
Before touching a single workflow or template, HatchWorks AI delivered a comprehensive GenDD training program to Vanco’s engineering organization.
The rationale was straightforward: scaffolding only works if teams understand the methodology it encodes.
Training created the shared language, the enthusiasm, and the organizational readiness for the transformation that followed.
- Phase 2: SDLC Scaffolding
- Turning methodology into an enterprise operating model
With 180 trained practitioners ready to go, HatchWorks AI engaged with Vanco to design and implement a complete SDLC scaffolding system, the playbooks, templates, guardrails, and workflows that make GenDD practical at enterprise scale.
The engagement followed a structured four-sprint methodology.
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Sprint 1: Diagnose. HatchWorks conducted targeted interviews with product stakeholders, engineers, architects, and QA representatives. The team performed deep repository reviews of representative codebases, mapped workflows from intake through deployment, and established maturity baselines across process, architecture, testing, and AI readiness dimensions.
Key discovery: Vanco's SDLC operated as a handoff-driven, governance-constrained system. While Scrum ceremonies and two-week sprints were in place, the system behaved closer to a stage-gated, approval-driven model than an adaptive delivery system. Jira data analysis confirmed the primary constraint was input quality, not engineering speed. - Sprint 2: Prescribe. Using discovery findings, HatchWorks defined the target SDLC blueprint — a stage-by-stage transformation plan covering ideation, refinement, development, testing, release governance, and post-release improvement. Every stage received an AS-IS assessment, a TO-BE target state, explicit Human/AI boundary definitions, and mapped playbooks to operationalize the transition. This phase also produced the story and requirements model, architecture standards, testing strategy, PR review guardrails, and the AI augmentation map.
- Sprint 3: Implement. The team built the production-ready assets — the cursor-rules repository, code and test templates, architecture scaffolds, the Brownfield Analysis engine, Context Packs, and a reference implementation repository demonstrating the complete story-to-code-to-test golden path.
- Sprint 4: Socialize. Role-based enablement sessions, golden path demonstrations, governance ownership definitions, and a metrics framework for tracking adoption and impact over the following 3–6 months.
What Was Built
- 41 production-ready deliverables across seven interconnected frameworks
The scaffolding engagement didn’t produce a strategy deck. It produced a working operating system for AI-augmented software development, the VancoSDLC Framework.
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Brownfield Analysis Engine. A four-pass, AI-assisted process for reverse-engineering undocumented legacy codebases into trustworthy architecture documentation. The engine scans repository structure and dependencies, infers architectural decisions with confidence scoring, validates findings with senior engineers through structured human-in-the-loop review, and produces final C4 models generated from actual code.
For Vanco's ~70% legacy codebase, this transforms the single biggest scaling bottleneck — tribal knowledge dependency — into navigable documentation any developer can use on day one. - 30 Playbooks. 18 role-specific playbooks (from Architect to UX Designer) and 12 on-demand task playbooks covering common workflows like generating architecture diagrams, enhancing acceptance criteria with Gherkin format, generating tests, and performing delta analysis. Each is calibrated to Vanco's specific stack, conventions, and organizational structure.
- Context Packs. Repository-specific knowledge bundles that feed AI tools with Vanco's actual patterns and constraints, so generated code compiles and follows Vanco standards out of the box.
- SDLC Transformation Blueprint. A comprehensive AS-IS to TO-BE guide covering all six SDLC stages, with explicit Human/AI boundary definitions at three tiers: Human Decision (requires human judgment), AI Assist (AI drafts, human reviews), and AI Automate (governed automation with human oversight).
- Supporting Assets. Story and bug templates with Definition of Ready/Done standards, Cursor IDE setup guide, cursor-rules repository, and reference implementation repositories demonstrating the complete golden path.
The Outcome
- The Transformation: Before and After
- Why This Matters for Enterprise AI Adoption
The gap between “AI is useful” and “AI is how we work” is enormous, and it can’t be closed with better prompts or more powerful models.
What closes it is what HatchWorks built for Vanco: an explicit operating model that defines where AI enters every stage of the SDLC, what it produces, and where humans remain essential. Gover
nance, golden paths, Context Packs, role-specific playbooks, and a Brownfield Analysis engine that turns the legacy knowledge problem into navigable documentation. The Vanco engagement demonstrates that GenDD isn’t a philosophy, it’s a delivery system. One that trains organizations, then operationalizes the training into production-ready scaffolding that teams actually use.
About the company
Vanco is a leading provider of secure digital payments, online giving, and administrative software for faith communities, schools, and nonprofits. Vanco helps organizations accept and manage payments, streamline everyday tasks, and deepen participation with reliable tools and practical support. The company’s faith business will now operate as ACS Technologies, offering trusted church management platforms such as Realm and MinistryPlatform that help ministries manage membership, contributions, accounting, missions trip management, events, and communication. Vanco also serves K–12 districts with education payments solutions, including RevTrak and SmartCare. Across every offering, Vanco’s purpose is simple: help teams spend less time on systems and more time with people.
About HatchWorks AI
HatchWorks AI turns AI into ROI by automating key business processes, transforming data, deploying intelligent agents, and shipping AI-powered products that deliver measurable results.