Talent and Capability Intelligence shows what people can do, and the evidence behind it.
Capability comes from what people demonstrated in sessions, not from completions. See it for one person or a whole team, with the evidence for every claim and the change over time.
Shown with sample workspace data.
One person, or the whole team.
Illustrations with sample data, not screens from the product. Role requirements and talent profiles beyond learning are in development.
A talent profile built from demonstrated capability.
What a person showed, concept by concept, with the sessions behind each result.
- Product knowledge
- Objection handling
- Escalation rules
- Discovery questions
Cohorts and programs side by side.
Where a team is strong, where it is stuck, and which part of the material is the cause.
| Policy basics | |||||
|---|---|---|---|---|---|
| Refund exceptions | |||||
| Escalation rules | |||||
| Explaining policy |
A cold row points at a gap in the material.
Every result traces back to what was taught.
Which version of the material, to whom, and whether the criteria were met.
- Refund policy v3Luca M.Criteria met
- Refund policy v3Dana K.Not yet
- Escalation guide v2Priya S.Criteria met
Progress as it happens.
Status moves from not yet, to getting there, to demonstrated, as people learn.
- PSPriya S.Escalation rulesGetting there
- LMLuca M.Refund exceptionsGetting there
- DKDana K.Explaining policyGetting there
- OROmar R.Escalation rulesGetting there
A cold row points at a gap in the material.
See each concept against each learner. Where a topic fails across a cohort, that is the session to rework.
Shown with sample data.
One view across program, cohort, learner and concept.
Start with the program and drill down to the concept one person keeps missing. Every level is backed by the same record of what was taught and what was demonstrated.
- Service advisor onboarding3 cohorts in progress
- Front-desk service standards2 cohorts in progress
- Warranty and policy1 cohort in progress
- March intake · 6 learners5 of 6 on track
- April intake · 4 learners4 of 4 on track
- May intake · 5 learners2 need attention
- Opening the conversationDone
- Confirming the concernDone
- Setting expectationsTo do
- Handling an unhappy customerLocked
- Stays calm, does not argueMet
- Names what actually cost the customer timeNot yet
- Offers a concrete next stepNot yet
Examples use sample data.
Measure comprehension by what learners demonstrated.
Completion is tied to learning criteria and the learner's own answers. Time watched and quiz clicks don't count.
- Minutes of video watched
- Slides marked as read
- Quiz answers selected
- Criteria demonstrated, concept by concept
- The learner's own words as the evidence
- What is still open, and why
The audit record proves what was taught, from which version, to whom.
Every course generation leaves a permanent record: the knowledge it drew on, whether it was current, any gap flagged and how it was closed, and the mode each session was assigned, with the reasoning.
When a compliance owner asks how you know someone was trained on the current policy, this is the answer.
Goodwill limits after expiry were not covered in any current source. The gap was flagged at generation, and closed by an author adding the approved figure, recorded against their name.
- Warranty policy v4.2 · DriveCurrent at time of use
- Service escalation SOP · NotionCurrent at time of use
- Goodwill guidance memo (2023)Stale, flagged, superseded
| Session | Assigned mode | Reasoning |
|---|---|---|
| What the warranty actually covers | Teach | New facts for this learner, taught before being checked. |
| Coverage checkpoint | Diagnose | Evaluation only, passed before the simulation opens. |
| Telling a customer their cover has ended | Practice | Judgement against a written standard, gated behind the Diagnose pass. |
Cognitive Modeling Research follows the whole path to the answer.
250+ live learner signals, read in context.
In learning sessions, Athira can interpret more than 250 live conversational and behavioral parameters.
Not every signal is available in every session, and none are weighted equally. No signal is read alone.
- Content and correctness
- Voice
- Facial action units
- Gaze
- Response latency
- Response trajectory
Three layers of evidence, read together.
Athira reads evidence in three layers at once. A Global Cognitive Prior describes how people generally learn and respond. A Domain Model describes what this subject or task actually requires. The Individual Learner State holds what this person has shown so far. No layer decides anything alone.
Why one score isn't enough
A quiz result of 72% cannot say which idea failed, whether the learner was close, or what would help. A path through one learning point can. Athira reads evidence across time instead of reducing the learner to a single number.
A path through one learning point.
Choosing the next action
The path decides what happens next. Athira moves on only when the evidence says the idea held.
- Explain it differentlyA second route to the same idea, in other words or another frame.
- Revisit a prerequisiteStep back to the idea this one depends on.
- Ask a diagnostic questionA targeted probe to find where understanding breaks.
- Increase application difficultyRaise the demand once the basics hold.
- Address a misconceptionName the wrong model directly and replace it.
- AdvanceOnly once the criteria are demonstrated.
Signals support teaching. Nothing more.
Signals are read in context to choose the next teaching step. They are never used as a medical diagnosis, an identity signal, or a measure of emotion, personality or worth.
Research into an evolving cognitive model, built in real sessions.
Athira is building an evolving cognitive model of knowledge, skills, behavior and context. The aim is to improve how AI teaches, reasons, acts and collaborates.
This is an active research direction, developed through real learning sessions inside the product.