01
Slow development cycles
Features and migrations that should take weeks take quarters, because every step waits on a person.
AI showcase
AI-powered development workflows.
Accelerating delivery without compromising security. Seven tools, two measured case studies and the numbers behind them — all built and used by our own team, all running on local models.
The problem
Where the time goes.
02
Manual and inconsistent code reviews
Standards live in people's heads. Quality depends on who happens to review the pull request.
03
Limited visibility into team performance
KPIs assembled by hand, once a week. Risks show up after the sprint, not during it.
04
Knowledge scattered across tools
Data locked behind SQL, configuration behind XML, answers behind whoever knows the system.
Our approach
AI inside the workflow. Not beside it.
We embed AI directly into real development workflows — securely, practically, and at scale.
01
Secure by design
Local / on-prem AI models. No data leaves the environment — not the code, not the database, not the prompts.
02
Context-aware AI
Integrated with real project data, standards and repositories, so answers match your codebase, not a generic one.
03
Execution-focused
Built to solve real tasks in production — not experimental use cases or demos that never ship.
Developersyour team, your process
AI layerlocal models · RAG · project context
Real outcomesshipped code, answered questions, localized builds
The tools
Seven tools. All in production.
Each demo is the real tool, recorded as it works. Videos are muted screen recordings hosted on this site — nothing is sent to third parties when you press play.
Watch the demo · 0:3601 / 07
Engineering
Code Review AI
Reviews every pull request against your own standards.
What it does
- Analyzes code using AI
- Applies project-specific standards
- Compares code versions (Git-based)
Impact
- Faster code reviews
- Improved code quality
- Consistent standards across teams
Watch the demo · 1:0102 / 07
Sales enablement
Product Visualization AI
From product photo to client-ready deck in minutes.
What it does
- Generates product visuals with logos
- Creates client-ready presentations
- Exports PDF files instantly
Impact
- Faster client deliverables
- Reduced manual design work
- Scalable customization
Watch the demo · 0:2303 / 07
Operations
Submit Time Plugin AI
Structured work data, captured where the work happens.
What it does
- Captures structured work data
- Customizable input workflows
- Integrates with team platforms
Impact
- More accurate tracking
- Better team insights
- Flexible for different use cases
Product screenshot04 / 07
Operations analytics
LeadOps Platform
KPIs computed from operational data, risks flagged before they bite.
What it does
- Tracks project, sprint and weekly team performance
- Calculates KPIs automatically from operational data
- Visualizes trends, efficiency, utilization and delivery health
- Detects risks and performance issues through configurable alerts
- Centralizes operational insights for leads and producers
- Enables historical analysis and AI-assisted decision support
Impact
- Improves visibility across teams and projects
- Helps identify delivery risks earlier
- Supports data-driven production decisions
- Reduces manual reporting and KPI calculations
- Increases accountability and operational consistency
- Creates a scalable foundation for AI-driven analytics and forecasting
Watch the demo · 0:3005 / 07
Editor tooling
AG AI Tool
Edits scene configuration from plain language, inside the editor.
What it does
- Reads and updates XML configuration files
- Extracts and displays scene data
- Applies changes directly to game scenes
Impact
- Faster configuration workflows
- Reduced manual scene editing
- Real-time updates with AI assistance
Watch the demo · 0:2406 / 07
Data access · natural language to SQL
AI Corgee Tools
Ask your database anything. Every model runs on-prem.
What it does
- Queries company data in natural language instead of SQL
- Generates a valid SQL query from intent and context
- Returns results instantly in a structured format
Impact
- Non-technical users access data without writing queries
- Less dependency on data or engineering teams
- Runs entirely on local AI infrastructure, ensuring data privacy
Watch the demo · 0:3607 / 07
Localization
AG Localization
Multi-language scripts with full control over tone and terminology.
What it does
- Translates game scripts into multiple languages preserving context, structure and intent
- Every script is reviewed by a person, who adjusts words, expressions or full sentences
- Full control over tone and terminology before integration
Impact
- Accelerates multi-language localization
- Reduces the effort and cost of the initial translation
- Faster iteration per script and more consistency across dialogue and content
- Localized builds earlier in the cycle; new markets without repeating the effort per language
Case study 01
AI Corgee Tools: ask your database anything.
Natural-language access to company data, with the whole pipeline running on local models.
What you're seeing
- A user queries company data using natural language instead of SQL
- The system automatically generates a valid SQL query based on intent and context
- Results are returned instantly in a structured format
How it works
- Natural language queries are translated into SQL using specialized AI models
- Context is enriched using vector search (RAG) to improve accuracy
- The entire pipeline runs on local models, ensuring secure and controlled execution
Usernatural language
AIVanna + RAG + local LLM
SQL querygenerated & validated
Resultsstructured, instant
Business value
- Enables non-technical users to access data without writing queries
- Reduces dependency on data or engineering teams
- Runs entirely on local AI infrastructure, ensuring data privacy
Watch the demo · 0:24
+180files, ~12,000 lines of code developed
15+modules and 2 AI agents in production
1developer, supported by AI tools
| Scenario | Timeline | Effort |
|---|---|---|
| Without AI | 8–10 months | 1,400–1,760 hrs |
| With AI · delivered | ~4 months | ~600–700 hrs |
Key results: development time reduced by approximately 55–65%; between 800 and 1,000 hours of work saved; a complex AI architecture implemented in a significantly shorter timeframe.
Technology stack
Frontend: React + TypeScriptBackend: PythonAI layer: Vanna.AI + Ollama (local LLM)Data layer: ChromaDB + RAGCustom logic: intent classifier (<50 ms, no LLM)
Case study 02
Corgee Web 2.0: legacy to modern, in five months.
A legacy AngularJS application migrated to a modern React + TypeScript architecture, with AI doing the heavy lifting on analysis and generation.
400+legacy files analyzed and migrated
~25,000lines of code rewritten
30+ / 28modules and services rebuilt
46high-complexity legacy components
| Scenario | Timeline | Effort |
|---|---|---|
| Without AI | 12–14 months | 24–28 dev-months |
| With AI · delivered | ~5 months | 10 dev-months |
Key results: development time reduced by ~60–65%; 7–9 months saved in the delivery timeline; significant reduction in team effort and coordination overhead.
What you're seeing
- A legacy AngularJS application migrated to a modern React + TypeScript architecture
- Core components rebuilt with improved structure and maintainability
- Same functionality, but with a scalable and modern tech stack
Where AI made the difference
- Rapid analysis of undocumented legacy code
- Automated generation of typed components and services
- Faster debugging and integration with legacy APIs
Business value
- Accelerates modernization of legacy systems
- Reduces technical debt and long-term maintenance costs
- Enables teams to adopt modern frameworks without full rewrites from scratch
Technology stack
Frontend: React 19 + TypeScript + ViteState: custom hooks + contextsServices: fully typed API integrationsMigration scope: 400+ files, 30+ modulesLegacy system: AngularJS
Beyond code
The same method, applied to art and 3D.
Our sister studio's art teams use the same approach: AI as a starting point, humans for every final decision. Proof that the method travels across disciplines.
Impact of AI
Measured, not promised.
0Faster developmentup to
0Hours saved across projects
0Projects measured end to endconsistent acceleration across use cases
Real development time with AI against the estimate for the same scope without it, across the AI Corgee Tools and Corgee Web 2.0 projects.
Why it matters
Faster delivery. Secure AI adoption.
Faster delivery
Reviews, migrations, data questions and localization that used to wait on a person now move at the pace of the team — measured at up to 60% faster.
Secure AI adoption
Every model runs on local or on-prem infrastructure. No code, data or prompt leaves your environment — the requirement that keeps enterprise, finance and public-sector work possible.
Consistent quality
Project-specific standards applied to every review and every script, across teams and time zones, instead of depending on who is available.
People stay in control
AI proposes; your team reviews, adjusts and approves. Every script, query and component goes through a person before it ships.
Let's build together.
Bring us a workflow that is too slow, a database nobody can query, or a legacy system that needs to move. We'll show you what the tools do with it.
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