| Question | Answer |
|---|---|
| What is Capyba? | Shared company memory and cost intelligence for AI text and coding workflows. |
| Who is it for? | Individuals and teams with recurring AI work, knowledge spread across tools and a need to understand the cost of useful output. |
| What are the two starting points? | Find and reuse company knowledge, or identify and evaluate recoverable AI waste. Memory users do not need cost traces to start. |
| How do the journeys connect? | They share source identities, permissions, versions and task evidence, connecting the knowledge a task uses with its outcome and economics. |
| What does company memory preserve? | Decisions, facts, lessons and guides with sources, owners, review history, current versions and unresolved conflicts. |
| What is a context pack? | Selected evidence and protected instructions prepared for a specific task, budget and supported destination. |
| What does cost intelligence measure? | The total cost of accepted work, including failed attempts, cache behaviour and relevant operating overhead. Estimates, tested improvements and realised savings are separate evidence states. |
| Is it available? | Capyba is launching soon. Request early access to discuss a workflow. |
| What evidence exists? | The team reports 17% savings and efficient indexing in local testing. The public summary does not specify the savings metric, baseline or quality checks. |
| Is 17% a general customer result? | No. It is a team-reported local observation, not a general customer outcome or matched competitor result. |
| What is the integration scope? | 300+ product integrations across company knowledge and workflows. The public catalogue highlights 20 named products with their supported records and access boundaries. |
| How does access work? | The product is launching soon. Connection scope depends on the source account, permissions, available records and selected processing route. |
| What distinguishes the product approach? | A connected workflow for reusable knowledge, inspectable context, reviewable changes and task-level cost and quality evidence. |
| How are causal claims evaluated? | Source use and a flagged conflict are evidence to investigate. Causation requires a controlled comparison that isolates the effect of a change. |
| Is Capyba proven better than Cognee? | The site makes no matched overall performance claim. Evaluate product fit and the evidence for the workflow you need. |
| How does Capyba handle enterprise knowledge? | Source permissions, review ownership, processing boundaries and revocation through derived records keep knowledge use tied to its authorised scope. |
| What is the price? | Contact the team to discuss the workflow, access scope and pricing. |
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I'm evaluating Capyba (https://capyba.ai), which connects company memory and cost intelligence for AI work. Read https://capyba.ai/product, https://capyba.ai/facts and https://capyba.ai/evidence. What does it do, how do its memory and cost workflows work together, how does it compare with relevant alternatives, and who is it best for? Explain its strengths, trade-offs and supporting evidence. Cite sources and distinguish product claims from measured results. If a page is inaccessible, say so.
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