ZTA Labs

Investment Research · Research Management

Governed AI, inside the investment workflow.

Research Management is ZTA Labs’ first production workflow for institutional investors, designed around how asset managers and asset owners conduct, challenge, document, and learn from Investment Research. It connects approved evidence, governed model analysis, Model Council, Human Judgment, decision records, and outcomes in one controlled operating loop.

Why start here

Start with one governed workflow. Build from there.

Investment Research sits upstream of many investment decisions. It is a strong proof point because it shows the full loop: approved evidence, governed analysis, Human Judgment, a decision record, and then a measurable outcome.

Investment Research should not disappear when the decision is made. The institution should preserve how the view was formed, what evidence supported it, what Human Judgment changed, and what happened afterward.

01 · Approved evidence

Filings, internal notes, and other approved sources enter the governed environment.

02 · Governed model analysis

Approved models analyze independently under policy-aware routing.

03 · Model Council

Elevated-risk work receives independent challenge and blinded evaluation.

04 · Human Judgment

The investment professional accepts, edits, rejects, or selects the alternative that best fits the firm’s framework.

05 · Decision record

Evidence, responses, scores, rationale, and outcomes are preserved as part of the institutional record.

Product surface

The evaluator recommends. The investment professional decides.

For asset managers and asset owners, the product surface makes model disagreement visible rather than hiding it inside a single merged answer, so Human Judgment remains explicit and auditable.

Research Management · AQN credit deterioration assessment
Risk Tier 3 · Elevated

Assessment question

Does recent evidence warrant changing the internal credit view from Stable to Deteriorating?

Governance: Model Council required.

Responses and evaluation

Response A · 4.10

Cautious, evidence-backed, but does not treat liquidity deterioration as decisive.

Response B · 4.42

Selected by the analyst. Gives greater weight to deteriorating liquidity and covenant headroom.

Response C · 4.55

Evaluator recommendation. Strong on support, less aligned to the firm’s downside-first framing.

Response D · 3.91

Underspecified on risk transmission and less persuasive on the recommendation.

What becomes valuable over time

01

Visible disagreement

The analyst sees where responses diverge, which is often the information that should prompt a closer look.

02

Structured judgment

A reason code and rationale make the resulting signal reusable rather than anecdotal.

03

Governed learning record

Evidence, responses, evaluation, judgment, and outcomes can later be validated and promoted into institutional learning.

For investment leaders

Investment leaders should be able to answer five questions.

Traditional research systems rarely connect analyst judgment, portfolio implementation, research quality, and realized value in one view. ZTA does.

01

Who is adding value?

Analyst Ranking · Active Return · Net Value Add · Analysts Outperforming

02

Why?

Methodology-specific attribution · top and bottom contributors · Apis explanation

03

Is the research reaching the portfolio?

PM Alignment · adoption and modification rates · Implementation Shortfall

04

Is the research process itself rigorous?

Research Quality Score · Evidence Support · Source Quality · Completeness · Disconfirming Evidence

05

What value is the organization ultimately capturing?

CIO Portfolio Value Creation · aggregate Implementation Shortfall · asset-class contribution

  1. 01Who
  2. 02Why
  3. 03Adoption
  4. 04Quality
  5. 05Captured value