AI is a component, not a strategy. We use it where it genuinely improves the product, and keep deterministic business rules, validation and conventional software where those are more reliable.
Possible projects
15- Text classification
- Document analysis
- Summarization
- Structured data extraction
- Question answering
- Semantic search
- RAG systems
- AI agents
- News analysis
- Sentiment analysis
- Content intelligence
- Document comparison
- Automated research workflows
- AI decision support
- Natural-language interfaces for company databases
Making an AI feature production-safe
The difference between a demo and a service is mostly in the controls:
- Structured output schemas and validation
- Deterministic rules around model output
- Grounding and source attribution
- Fallback behaviour when confidence is low
- Prompt and version management
- Cost controls and token budgeting
- Caching of repeated work
- Rate limiting and abuse protection
- Evaluation sets and regression testing
- Full request logging
The output is an ordinary API endpoint: documented, authenticated, versioned, and predictable enough to build a product on.
Related services
API Integration Services
Connect your platform to payment, CRM, ERP, market-data, logistics and AI providers behind one reliable internal interface.
API Aggregation and Unified API Development
One stable endpoint instead of many providers: schema mapping, deduplication, failover, caching and normalized errors.
Discuss an AI API?
Tell us what the API should receive, what it should return and who will be calling it. We will come back with an approach, a scope and a price.