Success Frameworks
A Success Framework is the heart of Yareta. It is the model of what success looks like for a given role at a given moment of use: for example a pre-seed CEO you might back, a sales hire you might make, or a leadership team you’re supporting after investment.
People are scored against that model from evidence you already hold (decks, models, CVs, transcripts), plus an optional psychometric. The framework is not a vague “culture fit” slogan. It’s a structured configuration the scoring engine runs against. Every number on a report is produced by that engine using the framework you attached. A language model never invents a different score.
Yareta does not decide whether to hire or invest. The framework and the report are decision support. A named human reviewer always makes the final call.
Why frameworks exist
Without a shared model of success, every reviewer improvises. A Success Framework makes the bar explicit and repeatable:
- You can screen many people on hard requirements (for example years of experience) and layer behavioural inference onto the same record.
- You can go deep on one founder using the same six components, not a different product.
- Findings stay sourced: flags and scores point to framework components and to evidence.
- When you share outcomes (who raised, who succeeded in role, how a team held together), the framework is recalibrated toward what actually predicted success. You don’t run that as a data-science project yourself. It’s a programme with Yareta.
Constructs in the library are academically anchored, developed with partners including the University of Cambridge. The platform learns by calibrating against outcomes and benchmarks. It doesn’t invent new things to measure for each customer.
How the base frameworks are seeded
Base frameworks start from the Yareta Assessment, which we offer free to the public at app.yareta.ai/entrepreneur-dna. Alongside the ~12-minute psychometric, respondents can share their role and career outcomes, such as whether they’ve raised funding. With their consent, this links trait profiles to real outcomes by role.
Each new completion adds to Yareta’s reference data. Base frameworks are periodically recalibrated on that growing dataset and released as new framework versions, so you start from an outcome-linked benchmark rather than a static template. Your own bespoke calibration then builds on top.
Two axes: role × moment of use
Every framework sits on two axes. The same scoring engine is used; only the configuration changes.
Job level
Examples: CEO, CFO, CTO, analyst, graduate, function lead, operator in a specific division.
A CEO framework and a CFO framework are different configurations of the same engine. You can run both in one organisation at the same time.
Stage of use
- Investment: backing a founder; real-time management due diligence
- Hiring: high-volume screening or deep assessment of a shortlist
- Performance / turnaround: supporting a team already in seat, including post-investment portfolio teams
The “pre-seed CEO who went on to raise” example is the classic investor case. The same idea extends to functions inside a company (what a high-performance sales or marketing team looks like) and across company size, from early-stage to large enterprise.
The six components (always present)
Every Success Framework carries all six. That’s how one engine can both filter at volume and diligence in depth.
| Component | What it captures | How you typically use it |
|---|---|---|
| Skills | Demonstrable capabilities | What the person can actually do, evidenced in their work |
| Requirements | Hard filters: experience, credentials, constraints | Minimum years, must-have qualifications; used to screen large funnels |
| Competencies | How work gets done in the role | Behaviours in execution, not just a job title |
| Culture | Fit with how the organisation actually operates | Inferred from how people write, speak and work in context, not a values poster |
| Motivation | What drives the person in this context | Why they’d persist in this role, not generic enthusiasm |
| Traits | Anchored psychological constructs | Inferred from existing evidence and/or the Yareta Assessment (six domains → 25 sub-traits) |
Requirements are the explicit bar (for example “at least N years”). Traits, competencies, culture and motivation are inferred from how people write and speak, then optionally strengthened by the ~12-minute Yareta Assessment.
Because both sit in one framework, you don’t need one tool for CV screening and another for psychology. The report can raise diligence flags against a specific component (for example a requirements miss and a trait gap), with probing questions aimed at raising confidence.
Pre-built, bespoke or Yareta-provided
| Type | What it is | Who it’s for |
|---|---|---|
| Pre-built / role benchmark | Validated starting point from reference cohorts with known outcomes | Fast start for investors, hiring teams and consultancies |
| Bespoke | Built and calibrated on your outcomes (who you backed, who succeeded) | Customers who want the bar to match their own history |
| Yareta-provided (no builder) | You don’t create frameworks; you score against frameworks Yareta supplies | Default for charities |
If you can’t see “create framework” in the app, you’re likely on a programme tenant (common for charities). That’s expected. You still score people; you just don’t design the science layer yourself.
Bespoke calibration often uses historical pipeline you provide (for investors: founders you backed vs founders you passed on who later raised). Yareta identifies the behavioural profile behind each outcome: the traits and patterns that separated who succeeded from who didn’t. Our psychologists and your team refine the result. It’s a managed programme, not a hidden admin screen you operate alone.
Outcomes, calibration and the Predictive Number
A framework is only as good as the outcomes attached to the people and roles scored against it: raised / did not raise; hired and succeeded / did not; team held / team broke down after a raise.
When you share those outcomes with Yareta as part of an ongoing programme, component weightings are recalibrated toward what actually mattered. Each recalibration ships as a new framework version. The scoring engine stays deterministic, and past scores are never silently changed.
Once a framework’s predictive validity is proven against real outcomes, its score is promoted from a Success Framework Score to a Predictive Number. Until then, the score measures fit, not proven prediction.
You don’t need third-party data accounts. Any enrichment Yareta uses on your behalf is covered in your agreement.
Multiple frameworks in one organisation
You can run several frameworks in parallel (CEO, CFO, CTO, a sales-team framework), each scored by the same engine under a different configuration.
Scoring someone against a CEO framework doesn’t stop you scoring them later against a function or team framework. Attach the framework that matches the decision you’re making now.
What scoring produces against a framework
When a person and their evidence are attached to a framework, a real-time report typically includes:
- the Success Framework Score, or Predictive Number where the framework has earned one
- diligence flags on specific components
- value-creation drivers
- probing questions to raise confidence
- a Low / Medium / High confidence band based on how complete the evidence is
Optionally, release the Yareta Assessment (and Insights, where enabled). Each new piece of evidence or answered probe re-scores the person. Regeneration carries, enriches, adds or retires findings. It never silently deletes them. Scorecards show the engine version and framework version.
Full walk-through: How scoring works.
Teams and functions
The same six components apply beyond one person. When several people from one team are scored, you see:
- which domains are covered
- which are carried by a single person (concentration risk)
- which are uncovered (gaps / breakdown risk)
Use that view to assess a founding team today and, over time, to compose complementary teams. As team outcomes accumulate, they build into Team Success Frameworks by function and company stage. Post-investment, investors also use this on portfolio teams that are underperforming despite having raised.
See Teams.
How to use frameworks in the app
Workspace: app.yareta.ai
- Choose or attach a framework: investor templates, organisation roles or Yareta-provided programmes. Admins manage templates in workspace settings; members apply them to companies, roles and teams.
- Attach people and evidence: CRM (recommended), manual add, uploads, transcripts, optional assessment invite.
- Read the score: Success Framework Score, flags by component, drivers, probes, confidence.
- Raise confidence: add evidence, answer probes or release the Yareta Assessment. The history of sourced findings is kept.
Recommended operating model: use your CRM as the entry point so the framework runs on people already in your pipeline, with scores and a Yareta link written back. Alternatively, work only in the app, or send scores to your own systems via API. Integrations are managed, so you don’t need to write code for CRM.
Related reading
- What Yareta does: product purpose and guarantees
- Product components: how frameworks sit alongside the assessment, engine, reports and delivery
- How scoring works: the end-to-end flow
- Use cases: investors, hiring organisations, people consultancies and charities
- Getting started: sign-in and first loop in the portal