Technical audits, stabilization sprints, and implementation for existing software.
Hexglyph works on AI-generated apps, unstable MVPs, and legacy web systems that need technical review, refactoring, and implementation support.
Technical audit snapshot
Current state and next steps
Audit output
Typical engagements include Lovable, Bolt, v0, and Cursor-generated projects, unstable MVPs, legacy internal tools, and React or Next.js applications. View the AI app stabilization page.
What we fix
For systems that already exist and still need engineering work
The work is structured for clients who already have software in place and need technical review, correction, or implementation help.
AI-generated apps that broke after the prototype
Code generated fast, now hard to maintain, deploy, extend, or trust in production.
Unstable MVPs before launch
Bugs, missing validation, weak architecture, poor UX, and unclear deployment steps.
Legacy internal tools
Old systems, manual workflows, fragile integrations, and missing documentation.
React and Next.js performance issues
Slow pages, inconsistent components, accessibility issues, and weak conversion paths.
Missing production basics
No tests, logs, error handling, deployment checklist, or technical handoff notes.
Stabilization packages
Start with review, then move into implementation
The service is split into review and implementation stages so scope and budget are easier to define.
Technical Audit
Best first step
A senior review of the codebase, architecture, UX, risks, deployment path, and highest-value fixes before more development budget is spent.
- Codebase and architecture review
- UX, accessibility, and performance review
- Security and validation checklist
- Prioritized backlog with severity and effort
- Fixed-price stabilization proposal
Stabilization Sprint
For launch-critical fixes
A focused implementation sprint to fix the highest-risk issues and prepare the application for production, handoff, or future scaling.
- Critical bug fixes and refactoring
- Input validation and error handling
- Logging and basic test coverage
- Deployment improvements
- Technical documentation and handoff notes
AI and Automation Implementation
Custom scope
AI features and workflow automation added after the application has the technical foundation to support them reliably.
- OpenAI and Azure OpenAI integration
- RAG and intelligent search
- Workflow automation and internal tools
- AI-assisted dashboards and reports
- Implementation plan tied to business value
Case studies
Public-sector and AI work reframed as problem-solving proof
Existing projects still matter, but they work harder when presented as concrete problems, technical solutions, and operational outcomes.
Orquestra
Problem
Technical teams need a local-first workspace for governed work items, observable LLM agent sessions, modernization pipelines, and portfolio snapshots.
Solution
A desktop-oriented orchestration platform with structured work management, agent session visibility, modernization flows, and project context snapshots.
Outcome
A stronger base for repeatable engineering work, safer AI-assisted delivery, and clearer technical handoffs across complex projects.
Municipal Intelligence Portal
Problem
Public-sector data was fragmented, slow to discover, and difficult to turn into strategic information.
Solution
AI-assisted search, vector databases, unified interfaces, and structured insights for municipal intelligence workflows.
Outcome
Faster discovery of municipal information and a scalable base for public-sector intelligence products.
Urban Management Suite
Problem
City operations require monitoring, alerts, automation, and predictive indicators across disconnected workflows.
Solution
An operational dashboard with automation, alerts, predictive analytics, and centralized visibility for city management.
Outcome
A clearer operating view for decision-making, prioritization, and automation opportunities.
How it works
A working sequence from review to implementation
The sequence is designed for remote work with written decisions, explicit scope, and staged implementation.
01
Technical Intake
You share the repository, product context, current issues, and deployment target.
02
AI-assisted Audit
We analyze architecture, code quality, UX, accessibility, performance, security basics, and delivery risks.
03
Prioritized Plan
You receive a backlog with severity, effort, dependencies, and recommended implementation order.
04
Stabilization Sprint
We fix the critical issues and prepare the application for production, launch, or handoff.
05
Documentation
You receive technical notes, deployment instructions, improvement backlog, and next-step recommendations.
Service index
Primary service pages
These pages cover the main service lines and are linked directly from the homepage.
AI app stabilization
Dedicated page for Lovable, Bolt.new, v0, and Cursor-generated apps that need production engineering.
Technical audit service
Entry page for codebase audits, risk maps, implementation planning, and production-readiness reviews.
Legacy modernization
Service page for internal tools and aging systems that need controlled refactoring, production hardening, and clearer delivery.
MVP rescue
Launch-focused service page for unstable startup products that need bug fixing and production hardening.
Cursor code cleanup
Entry page for refactoring AI-generated code, reducing duplication, and cleaning up fragile architecture.
Next.js performance
Dedicated page for slow pages, weak Core Web Vitals, and React or Next.js optimization work.
Built with production standards
Technical stack and delivery scope
These are the main technologies and delivery concerns covered in typical engagements.
Frontend
Backend
AI
Delivery
Next step
Have an unstable app or MVP?
Send the repository and current issues to start with a technical audit or scoped implementation sprint.