How it Works

Validated Actions

Ship validated actions, not just agents.

AI agents rarely fail at what they can do. They fail at the point where they act on a customer. TAZI checks every proposed action with two independent agents, a Focus Group of your own customers and an Expert Panel of your own decision-makers, before anything executes. Solutions stay current with continuous learning and monitoring.

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From 94% to 42%

A churn model is easy to build. Getting its output to a client is the hard part.

In one wealth retention deployment, an accurate model still produced alerts for 94% of advisors' clients, so the pilot sat there. We did not change the model. We added a check on each action, and needless manager alerts fell to 42%.

Why agents stall before production

The Action Carries the Risk and the Value

Model governance reviews lineage, security, drift and fairness. It does not answer the question that decides deployment: was this action right, and can you prove it?

Ungrounded agents hallucinate

An agent that is not grounded in your data and an explainable model has nothing to cite when it acts.

Automation skips the judgment

Workflow tools copy the steps of the work but not the judgment behind it: who may act, when, and how far.

No audit trail, no deployment

Without a record of why each action was allowed, compliance cannot say yes. A second, similar LLM reviewing the first does not help: similar agents make correlated mistakes.

How the platform works

Five Levels of Agency

Shown on client attrition. Each level adds depth to what the people in the loop can do, and humans review and refine at every step.

  1. 01

    Predict

    Flags that a client is likely to leave.

    The problem is known

  2. 02

    Explain

    Names the behaviours, balances, life-stage changes and interactions behind the risk, in business language.

    Possible actions appear

  3. 03

    Recommend

    Proposes the actions most likely to retain the client.

    Humans review and refine

  4. 04 Validation layer

    Validate

    Tests each action against outcomes, constraints, customer response and policy before deployment.

    Only validated actions execute

  5. 05

    Simulate & Optimize

    Asks what happens to the client, portfolio, employees and competitors two or three steps later.

    Humans shape the future

The validation layer

Two Independent Checks on Every Action

The two agents ask different questions, so they do not share the proposing agent's blind spots. Only actions that pass both become validated actions and reach a customer.

  1. Input

    Proposed action

    Drafted by the action agent from the model score and its named pattern.

  2. Check 1

    Focus Group Agent

    Will this customer respond? In fraud: will a legitimate customer be harmed?

    Fails: the action is dropped.

  3. Check 2

    Expert Panel Agent

    Is the action compliant and proportionate?

    Fails: returned with a reason, and the reason is logged.

  4. Output

    Validated action

    Reaches the client or the queue, with its full record.

Focus Group Agent

A Synthetic Panel That Argues Back

A distilled focus population built from your own customer segments, not generic archetypes.

  • Panels that argue back Each persona is its own agent with its own memory, told not to people-please, so critical personas stay critical.

  • Emergent segments Segments come from what personas say. The synthesis never sees the hidden driver notes used to build them.

  • Motivation × behaviour A cross-tab of why a group acts against what it does, instead of a transcript to read.

  • 3–5 actions, any language Concrete next steps, answered in the language of the question. Built and proven in Turkish.

  1. Convene
  2. Ask
  3. Discuss
  4. Synthesize
  5. Decide
Explore the Focus Group Agent

Expert Panel Agent

A Boardroom That Signs Off, or Says No

Modeled on your own advisors, SIU investigators, AML officers and compliance leads, each with the veto they hold in a real committee.

  • Members with a mandate Each member has a title, expertise and mandate (pricing, risk, compliance, brand, operations). Weighted votes reflect who owns the decision.

  • Deliberation, not a poll Members respond to each other over moderated rounds. A devil's-advocate setting brings dissent and trade-offs out before you commit.

  • Minutes and a verdict A secretary condenses positions into a weighted recommendation with reasons: ship, kill or change.

  • Every verdict on the record Logged with its rationale and memory across meetings, inside your own environment.

  1. Convene
  2. Table a decision
  3. Deliberate
  4. Minute & decide
Explore the Expert Panel Agent

Same predictions, validation layer switched on

What Validation Adds

The model and the detector did not change. Only the action layer did.

Wealth retention

from 94% to 42%

Needless manager alerts

27%

Of the book cleared as "no action"

37%

Corrupted-data cases caught, with 0 false alerts

AML

from 98 to 4

Top-tier SAR escalations (−96%), every one corroborated

from 99% to 0.4%

"Source of funds" tipping-off phrasing in draft narratives

Account takeover

from 20 to 0

False SAR exposures on legitimate customers in the top tier, same detector

100%

Auditable rule trail

Results from TAZI deployments and proofs of value, with expert-panel and focus-group validation.

After go-live

Continuously Updated Solutions

A validated action is only as good as the model, the data and the agents behind it. TAZI keeps every part of the solution watched and current, and nothing reaches production without a business and a technical "go".

Monitor

Second-line solution monitoring

Alerts when a predictive or agentic component, or the data feeding it, has an issue: drift, data-quality exceptions, falling performance or fairness, or agents departing from their rules.

Update

Continuous learning

Predictive models, explanations and agents are updated as the need and the use case require, without a full retrain.

Approve

Business and technical "go"

Every solution, and every update to it, gets a "go" from both the business line and the technical line before it is taken to production.

Underneath every validated action

Four Non-Negotiables

Explainable ML

A reason code on every score, from a companion explanation model that names patterns in business language.

Humans in the Loop

Risk officers own the label definitions and can approve, edit or gate any step.

Without validation and risk monitoring, a human in the loop is no better than handing the work to agents. The people doing the work today design what the humans review, through which interfaces and with whom.

Auditable by Default

Every validation is logged and can be reopened. Model cards and compliance documents are generated automatically.

Your Environment

Cloud, on-premise or hybrid, with your own keys and LLMs, on an LLM-agnostic platform.

Where this leads

From AI Users to AI-Enabled Market Makers

Validated actions let companies give more people more agency. Authority can be spread across employees and AI systems while KPIs, controls, constraints and learning loops stay shared.

Represent each decision at full depth

Shallow
A client with $2M AUM → "High-value client → Retention campaign"
Deep
  • Liquidity trend
  • Life stage
  • Risk profile
  • Relationship history
  • Unmet needs
  • Advisor capacity
  • Competitor activity
  • Market conditions
  • Likely response to alternatives

Segments then reflect the situation around each decision, not only net AUM.

Simulate before you act

The simulation layer runs the same loop on simulated data for possible futures instead of past actuals.

"What happens to our whole system, and the competitor's, if we do it?"

The question is no longer how much AI you have, but how much agency, depth and decision-making capacity you can give your people.

Path to production

One Lever, About 72 Proof-of-Value Hours

Start with one use case, improve data as you go, and let each win fund the next.

  1. 01

    Prepare

    Data, label and business KPI.

  2. 02

    Ground & Explain

    Build the grounded model and its explanation layer.

  3. 03

    Validate

    Check actions with the Focus Group and Expert Panel agents.

  4. 04

    A/B Test

    Run against the incumbent process.

  5. 05

    Go Live

    With compliance documentation already written.

Wealth

−15% churn

USD 7.5M saved per year at a European bank with USD 18B in wealth assets.

Fraud

+56% true positives

Within two months at a US credit union.

Insurance

+12% retention

At one auto insurer; USD 2.3M retention savings in one quarter at another, with ROI in 14 days.

The underlying agents already pay back on their own. Validation is what moves them from a pilot to the daily queue.

Connects to Your Existing Systems

TAZI deploys on-premises or in your private cloud and connects agents through MCP. Your data stays in your environment.

  • MCP
  • Claude
  • Slack
  • Salesforce
  • HubSpot
  • Core systems
  • Snowflake
  • Databricks

Which AI-Suggested Action Would You Put in Front of Customers Next Quarter?

And who is stopping it today? See the Focus Group and Expert Panel agents validate actions on your own data.

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No commitment. No credit card. Just insight.

From the Blog

Zehra Cataltepe · LinkedIn ·

Ship Validated Actions, not just Agents

A retention AI sent needless manager alerts on 94% of the book. Validation cut it to 42%.

(opens in a new tab)

TAZI Team ·

Ship Validated Actions, Not Models

Two independent checks on every action, four non-negotiables underneath, and a 72-hour path to production.

TAZI Team ·

From AI Users to AI-Enabled Market Makers

Five levels of agency, from predict to simulate, and why AI should help more people work where they create the most value.
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