Capture
The important parts surface while people speak.
Capture an intentional conversation or voice note. Actionairy preserves the source and surfaces the moments that may change a decision.
An AI teammate for product and engineering.
Investigate worthwhile opportunities, compare value and tradeoffs, and test public product journeys. Carry approved work forward with evidence attached.
[01] Product
From conversation to product decision.
Actionairy keeps the source, reasoning, approved work, and result connected.
Decide
One event becomes a decision only when the evidence holds.
Actionairy tests the event against company memory, product behavior, and current context. You see why the work matters and what supports the recommendation.
Execute
Approved work runs in the cloud and returns ready to review.
Actionairy turns the approved decision into scoped work, verifies the result, and returns a reviewable artifact to the team.
[02] Decision memory
Every result makes the next decision sharper.
Evidence, rationale, approval, shipped work, and measured outcomes stay connected in one company-owned record.
- Every recommendation links to its sources.
- New evidence can challenge an old assumption.
- Measured outcomes update the next ranking.
Build self-serve workspace import
Repeated setup friction is delaying first value.
Company memory you can inspect, control, and erase.
Every durable claim keeps its source, processing policy, and deletion path.
Inspect what created every durable memory and how it changed.
Choose Never send, Redacted only, or Allowed for each record.
Source and account deletion cascade with a verifiable status record.
Evidence, in the openThe intelligence.
The intelligence.
The evidence behind it.
Useful AI needs more than a convincing answer. We evaluate task relevance, engineering readiness, and code implementation separately—and publish the limits alongside the results.
Archived development results
Latest Nebius results and live workflow checks. The dated results below remain available for comparison.
01 / Relevance
Task relevance
Keep real tasks visible. Let evidence decide what belongs in the engineering queue.
14/14
investigated cases passing all checks
Synthetic development testgpt-5.4-2026-03-05
Small synthetic set, not measured real-world accuracy.
02 / Readiness
Engineering enrichment
Recognize when a request has enough context to become engineering work—and when it does not.
24/24
correct readiness decisions
Synthetic development testgpt-5.4-2026-03-05 · low reasoning
Readiness is not plan correctness. Independent plan-quality review is still pending.
03 / Implementation
Datacurve DeepSWE
Test code changes against a separate, externally defined coding benchmark.
56.6%
64/113 · full-cohort tasks passed
Separate Compress Cloud runArchived Compress Cloud coding run
A separate Compress Cloud run, not a meeting-to-code success rate.
Explore full results and methodologyBaselines, limitations, and what we still need to measure
Separate measurements of task relevance, plan preparation and coding. Development fixtures are not independently labeled real-world accuracy. These scores must not be combined into an end-to-end success rate.
Task relevance
- investigated cases passing all checks
- 14/14100.0%
- Real tasks retained
- 14/14100.0%
- Incorrect queue admissions
- 0/140.0%
- Source-only baseline passing all checks
- 8/1457.1%
- 14 agent-authored cases; live reviewer with frozen history, code and web retrieval. Planning is an invocation spy, not an executed plan.
- A real task may remain visible without belonging in our engineering queue. Human acceptance and execution approval remain separate.
Engineering enrichment
- correct readiness decisions
- 24/24100.0%
- Completed cases
- 24/24100.0%
- False-ready decisions
- 0/120.0%
- Automatic wording checks
- 23/2495.8%
- Plain-model readiness baseline
- 24/24100.0%
- Plain-model wording baseline
- 20/2483.3%
- Raw reviewer contrast checks
- 8/1080.0%
- 24 agent-authored ambiguous tasks (16 original + 8 transfer). Readiness is not plan correctness. The separate raw reviewer scored 8/10, incorrectly rejecting two valid briefs.
- Same source evidence and per-call output cap; unequal total budget: up to 4 Actionairy calls versus 2 baseline calls. No claim of competitor superiority.
- Independently reviewed usable-plan accuracy and human correction time are not yet measured.
- Failed calls remain in the full denominator. Automatic wording checks are lexical, not semantic grading. No independent human usable-plan score is available.
Datacurve DeepSWE
- full-cohort tasks passed
- 64/11356.6%
- 2 tasks remained ungradeable after bounded regrading. They receive no pass credit and stay in the full-cohort denominator.
- Sealed candidate patches and pinned verifier contexts. This is a separate coding evaluation, not an end-to-end Actionairy meeting-to-code test or a current-model leaderboard claim.
What these results do not measure
Meeting extraction: independently labeled capture precision and recall are not yet available. Transcript-upload acceptance is a user-journey check, not an accuracy estimate.
This archived report does not measure independently reviewed plan quality, human correction time, or a representative meeting-to-code success rate. Historical results do not establish current-model performance or competitor superiority.
The DeepSWE mark identifies Datacurve’s benchmark. Our result is a Light Reach evaluation, not a Datacurve endorsement or an official leaderboard entry.
[04] FAQ
What teams need to know.
Investigate opportunities using company and market evidence, review expected value and tradeoffs, and test public browser journeys. Private voice conversations, labeled buyer practice, and People and Market suggestions support that work. You approve implementation and release.
Scheduled meetings, customer and sales calls, support conversations, team discussions, in-person conversations, and voice notes that you intentionally start.
Scheduled sweeps and material activity revisit source-linked company context. Research and revisioned value assessments expose supporting evidence, contradictions, alternatives, costs, and uncertainty. Unsupported financial amounts remain unknown.
Only after you authorize GitHub and approve the work. The cloud agent uses the repositories you allow, works on a review branch, runs verification, and prepares a pull request with the diff, logs, and cost attached.
You do. Sources, evidence, decisions, approved work, and results remain connected inside your client-owned decision memory. The record becomes more useful as completed loops show which evidence led to outcomes.
Every durable record keeps its source, access scope, retention rule, and external-AI policy. Restricted or denied records are excluded before external processing, vector hits are re-authorized against the source record, and deletion follows sources through derived memory.
The decision record. Each loop adds source lineage, competing evidence, approval history, delivered work, and the measured result. New decisions begin with what your company has already learned instead of a blank prompt.
Make one real product decision.
Bring one customer conversation. Actionairy prepares the evidence and the next reviewable move.