Process automation, AI agents and company knowledge
We connect systems, rules and AI models to reduce repetitive work and make information easier to access.
Technology choices start with the process, available data and the consequences of an error.
When to consider an implementation
- Data is copied manually between systems.
- Employees search for answers across scattered documentation.
- Your team classifies documents, handles repeated requests or prepares reports.
Scope of work
- API integrations and process automation with exception handling.
- Language models for extraction, classification and content generation.
- RAG: company knowledge retrieval with access control and source references.
- AI agents with defined tools, permissions and approval checkpoints.
How we evaluate feasibility
We agree on sample data, quality criteria, operating costs and acceptable latency.
When assumptions need validation, a prototype tests them before full implementation.
When rules or a standard integration are sufficient, a language model is unnecessary.
Control and limitations
We define providers, data access, retention and operations that need human approval.
AI responses can be wrong. The design includes validation, monitoring and exception handling.
Local, cloud or hybrid deployment depends on your requirements and available infrastructure.
What the analysis delivers
A process map, feasibility assessment, key risks and a proposed scope.
Estimates separate implementation, infrastructure, API usage and optional ongoing support.
Discuss your project
Send your goal, scope, timeline or RFP documentation.
At CODEWART, you work directly with the person responsible for your solution - from defining the scope to acceptance of the agreed work.
contact@codewart.com