Generative AI
Use generative AI capabilities where they provide useful business value.
AI & Generative AI
Build practical AI into real business workflows
Codithic helps organizations explore, design, integrate, and build AI-powered capabilities around real business needs. AI should solve a business problem, fit into existing applications, and support workflows rather than exist as a disconnected experiment.
What we engineer
AI engineering around real business needs
AI capabilities should fit into applications, workflows, and operational processes rather than operate as isolated technology experiments.
AI-Powered Applications
Build applications that use AI capabilities as part of the user experience or business workflow.
AI Integration
Connect AI capabilities with existing applications and processes where appropriate.
AI-Assisted Workflows
Use AI to support people and business processes with practical assistance.
Machine Learning
Apply machine learning where the problem and available data make it appropriate.
Emerging AI
Explore relevant emerging AI capabilities as technology and business requirements evolve.
How we approach AI
From business problem to practical AI
Codithic uses a practical engineering approach to move from a business problem to an AI capability that can sit inside an application or workflow.
Conceptual AI engineering approach: understand, assess, design, build, integrate, validate, then evolve. Not every engagement follows these seven steps.
01 Understand
Understand the business problem, users, workflow, and desired outcome.
02 Assess
Assess whether AI is appropriate and where it can add value.
03 Design
Design the AI capability around the application and workflow.
04 Build
Implement the required AI-powered capability.
05 Integrate
Connect it with the surrounding application or business process.
06 Validate
Review behavior, usability, and suitability for the intended use.
07 Evolve
Improve the capability as requirements and technology evolve.
This is Codithic's practical engineering approach. It is not a required methodology for every AI engagement.
AI inside the application
AI works best when it fits the workflow
Codithic can engineer AI capabilities into existing or new applications so they support the surrounding workflow rather than sit apart from it.
Conceptual view of AI inside an application: an existing or new business application, an AI capability such as generative AI, AI-assisted processing, or machine learning where appropriate, and a business workflow covering user interaction, operational process, and business decision support.
Application
Existing or new business application.
- Existing application
- New application
AI Capability
Practical AI capabilities used where they fit the problem.
- Generative AI
- AI-assisted processing
- Machine Learning where appropriate
Business Workflow
The work people and processes still need to complete.
- User interaction
- Operational process
- Business decision/support
Current product AI direction
AI capabilities inside Codithic products
Codithic's current product AI direction focuses on practical capabilities inside the DMS + SFA ecosystem. These capabilities are intended to support people and existing operational workflows.
AI Order Capture
Support order capture from relevant business input.
Convert useful information into a structured order workflow.
Connect the capability to the existing order-management process.
Intelligent Sales Assistant
Assist field sales activity with relevant information and workflow support.
Support users while they perform their field activities.
Keep the assistant connected to the operational context.
Evolving AI capabilities
Build what is useful today. Evolve what comes next.
Current product AI direction is distinct from future or planned capabilities. Future items are not currently available.
Current
- AI Order Capture
- Intelligent Sales Assistant
Future / Planned
- Recommendation
- Forecasting
AI + Applications
Add AI around the application
AI can be introduced into existing applications and business workflows without replacing the entire application.
Conceptual relationship: an existing application with its business data and workflows, an AI capability layer such as generative AI, automation, and assisted workflows, then an enhanced workflow with human involvement where appropriate. Not every application requires every capability, and integration is not automatic.
1 Existing Application
Business data and workflows.
2 AI Capability Layer
Generative AI, automation, and assisted workflows.
3 Enhanced Workflow
The resulting workflow, with human involvement where appropriate.
A conceptual relationship. Not every application requires every capability, and AI is not integrated automatically.
Practical AI
Useful AI. Clear boundaries. Human-centered workflows.
AI implementations should consider how the capability will be used, who remains involved, and how it should behave inside the workflow over time.
01 Appropriate use of AI
Use AI where it can support a defined business need.
02 Human involvement
Keep people in the workflow where judgment and review are required.
03 Workflow context
Place AI inside the application and process it is meant to support.
04 Validation
Review behavior, usability, and suitability for the intended use.
05 Expected behavior
Be clear about what the capability should and should not do.
06 Evolution over time
Improve the capability as requirements and technology change.
Connected capabilities
AI engineering can work alongside other technology services
Depending on the requirement, AI engineering can be combined with software engineering, integration, cloud and DevOps, or technology consulting.
Software Engineering
AI capabilities can be built into web, mobile, backend, and business applications.
Integration & Modernization
AI can be introduced into existing application landscapes where appropriate.
Cloud & DevOps
AI-enabled applications can be deployed and operated within the required environment.
Technology Consulting
Assess AI opportunities, technology choices, and implementation direction.
Business context
AI should start with the problem, not the technology
Codithic approaches AI from the business requirement, the workflow, the users, and the applications already in place. For distribution and FMCG, that stays with the current product direction: AI Order Capture and Intelligent Sales Assistant where implemented. Recommendation and forecasting remain future or planned.
- FMCG / Distribution
- Dairy
- Beverage
- Pharma
- General Distribution
Business value
Practical AI. Better Workflows. Continuous Evolution.
Business-focused
Start with the business need.
Workflow-integrated
Fit AI into the surrounding process.
Practical Engineering
Build capabilities that can be implemented and evolved.
Continuous Evolution
Improve AI capabilities as business and technology requirements change.
Ready to explore AI?
Let's build practical AI for your business
Tell us what you are trying to improve, automate, understand, or build.