Useful AI flows
We design AI interactions around real user jobs, not novelty demos, so the feature earns its place in the product.
- Workflow mapping
- Prompt architecture
- UX states
SVC_02 / Solution Detail
AI & LLM Integration.
Custom AI agent deployment, vector databases for RAG pipelines, and LLM-powered features seamlessly integrated into your product workflows.
SVC_02 / Roadblocks
Turn model capability into product behavior
We help teams move from impressive prompts to dependable AI workflows with retrieval, orchestration, evaluation, and UX that users can trust.
Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?Where should AI sit in the workflow?Do we need RAG?How do we evaluate responses?Which model should we use?
How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?How do we reduce hallucinations?What data should be indexed?How do agents take actions?What should stream to the UI?
How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?How do we monitor quality?How do we protect user data?What belongs in system prompts?How do we manage token costs?
Retrieval foundations
Knowledge, documents, and business context are structured for accurate retrieval and explainable responses.
- Data ingestion
- Vector search setup
- Source attribution
Safer orchestration
Tool calls, agent logic, and fallback paths are mapped carefully so AI actions remain observable and controlled.
- Tool-call logic
- Guardrail design
- Fallback handling