Savings in local testing.
An early result reported by the Capyba team. The public record does not specify the metric, baseline or quality checks. This result has not been established across customer deployments, across both text and code, or in a matched comparison with Cognee.
Evaluate the recurring workflow.
Compare the current workflow with Capyba on the same tasks. Agree on acceptance criteria, account for the attempts that fail and include operating overhead. Report where the change helps, where it does not and what remains unknown.
Discuss an evaluation on your workflowCount accepted work, including the attempts that failed.
Publish acceptance rate alongside cost. If no task is accepted, cost per accepted task is undefined. Unknown outcomes and missing costs must stay visible.
The full economic view includes model charges, tools, connectors, indexing, processing and allocated licence cost. Include human intervention when measured at a stated rate. Report subscription capacity separately from cash savings.
A flagged source is not proof of causation.
To test whether a correction helped, retain the source version and show the workflow could read it. Compare the original with the proposed correction under matched conditions. Hold models and acceptance criteria constant, and record regressions as well as improvements.
Update what changed. Measure what it costs.
Capyba uses incremental updates, duplicate detection and bounded retrieval. The team reports efficient local indexing. No quantified long-term indexing benchmark is presented here.
| Evaluation | What to measure |
|---|---|
| First ingestion | Time, cost, memory and index expansion across representative sources. |
| Ongoing updates | Work repeated, affected objects, duplicate handling and update cost. |
| Retrieval | Recall, evidence support, cold and warm latency, and cost as an archive grows. |
| Knowledge changes | Current-answer accuracy and handling of replaced or conflicting decisions. |
| Access and deletion | Detection interval, retrieval blocking and cleanup of derived records. |
| Concurrency and recovery | Throughput, failure isolation, resumption and the cost of interrupted work. |
| Economics over time | Ingestion, updates, retrieval and maintenance per accepted task. |
Publish the conditions behind the number.
| Record | Required detail |
|---|---|
| Metric and baseline | The unit, denominator, original workflow, settings and version. |
| Candidate and workload | Capyba version, change tested, task types, source mix and task selection. |
| Models and runs | Provider, model versions, dated prices, paired task counts, cache conditions and exclusions. |
| Quality and costs | Acceptance criteria, failures, unknown outcomes, included overhead and unmeasured costs. |
| Outcome and reproduction | Absolute values, variation, latency, fixtures, configuration, review date and replication status. |
Explore Capyba with your AI.
Ask ChatGPT, Perplexity or Claude what Capyba does, where it fits and how it compares.
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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