THE DISHA INTELLIGENCE ENGINE → TALENT INTELLIGENCE
Find the Human Capability Behind the Resume.
Discover people through skills, evidence, experience, context and readiness — not keyword matches.
Who are the right people — who has the capability, evidence, context and readiness relevant to this work?
Signature: WORK → CAPABILITY → EVIDENCE → MATCH → READINESS → DECISION
One system, six questions — you are here:
The Talent Intelligence Canvas
Five zones between work and the right person
Selecting a work requirement reveals the talent population; selecting a person reveals their evidence trail. Both directions stay open here.
Zone 1
Work Need
Start from the work — outcomes, tasks, constraints. Not a job title.
Zone 2
Capability
Must-have and learnable capabilities the work requires.
Zone 3
Evidence
Proof each capability is real and current — with provenance.
Zone 4
Context
Where and how the person works: location, zones, constraints.
Zone 5
Readiness
How soon they could contribute — human-judged, never a score.
Signature explorer — try it
The Match Explorer: a transparent talent landscape
Pick a work requirement. What opens is not a ranked list — it is a landscape with relevance reasons, missing evidence and readiness considerations.
Must-have capabilities
Distributed systems · Data modelling · Team leadership
Learnable capabilities
Streaming architectures · Cost governance
Context & evidence requirements
Hybrid · 2 days on-site · overlap with three regional hubs · Shipped platform migration; Incident retrospectives; References from direct reports
Compare, don't rank
Side-by-side, with the missing column visible
Two to five people compared across requirements, evidence, experience, readiness and context. Decision-makers define the priorities — there is no universal winner.
| Dimension | Composite A | Composite B |
|---|---|---|
| Must-have capabilities | All three, evidenced | Mostly, with gaps noted |
| Evidence highlights | 2 verified · 2 declared/inferred | 2 verified · 1 declared/inferred |
| Experience relevance & recency | 84 — see factor breakdown above | 66 — see factor breakdown above |
| Context fit | 72 — Hybrid | 88 — Hybrid |
| Readiness | Could ramp in 6–8 weeks | Could ramp in 8–10 weeks |
| Information missing | References from direct reports · Cost-governance evidence | Migration-scale project record · Retrospective writing samples |
The Information Missing column is a first-class citizen — an honest gap in the record outranks a confident guess.
Calculators
Run the numbers — as scenarios, never verdicts
Calculator 1
Talent Coverage
Required capabilities vs the evidence-backed talent available — where coverage is thin and why.
Calculator 2
Build / Buy / Develop
Hiring vs internal development vs redeployment vs external sourcing — compared as scenarios, not verdicts.
Calculator 3
Talent Pool Scenario
How skill or location changes move the discoverable pool. Scenarios, never hiring guarantees.
Every result is explainable, source-linked, permission-aware and freshness-aware — and ends in a meaningful next action.
Ask the AI Talent Analyst
Grounded in authorized records only — it explains, never ranks in secret.
- Why are these people relevant?
- What evidence is missing?
- What skills are common across this pool?
- What could we develop internally instead?
Grounding rules
- Answers cite the authorized records behind them — or say the evidence does not exist.
- Uncertainty is shown, never smoothed away.
- Recommendations are options for a human to judge — never silent decisions.
- Every answer stays within your purpose and access level.
In the live product these conversations stream from your authorized data. Here, the questions show exactly what the analyst is built to answer.
Interconnections
Where talent intelligence hands off next
Conceptual navigation across one intelligence system — links carry your context; none of this implies a causal or predictive pipeline.
Technical architecture — how this page is built
Connected systems
ATS / TAMS, HRIS, LMS/LXP, skills systems and approved talent sources — connected read-only until authorized.
Core objects
Talent, requirement, evidence, skill relationship, provenance, freshness, readiness, audit trail.
Governance — identical on every Intelligence page
How this system stays honest
These five statement types never blend: everywhere in the engine, you can tell what is measured, what is derived, what is projected and what is advised.
Statement types
Observed, inference, forecast, scenario and recommendation are visually distinguished — never blended.
Illustrative by default
All data, people and outcomes on these pages are labeled illustrative composites.
No fabrication
Never fabricate people, employers, credentials, outcomes or statistics.
Explainable AI
AI explanations expose the evidence and the uncertainty behind them.
Authorized use
Data is used only within its authorized purpose and access level.
Human authority
AI supports judgement; it never replaces human decision authority.
Talent Intelligence adds: No inference of protected or sensitive traits to rank people. No deterministic 'best candidate' labels. Every affected person has correction, appeal and human-review paths.
The engine behind the pages
Six pages, one interaction contract
Rule 1
Context handoff
Moving between the six pages carries your question and context with you — you never restart from zero.
Rule 2
Shared vocabulary
Skill, evidence, proficiency, readiness and scenario mean the same thing on every page.
Rule 3
Common evidence layer
Every claim carries source, timestamp, provenance, confidence and access level — everywhere.
Rule 4
AI layer boundaries
The AI summarizes, explains, compares and simulates — without inventing facts or silently converting recommendations into decisions.
Rule 5
Progressive disclosure
Overview first; evidence, assumptions and audit trails open on demand.
Journey · Recruit
Journey · Develop
Journey · Move
Journey · Transform
Journey · Institution
Illustrative journeys — conceptual paths across the engine, not claims about how any decision is made.