Control your AI portfolio. Prove impact before scale.

AI Conveyor helps AI Office teams see every AI initiative, gate, risk, role, and expected impact in one managed operating loop.

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  • Initiative portfolio
  • Stage gates
  • AI assistant
  • ROI and risks
AI Conveyor platform

Dashboard

April 2026

Funnel Ask AI
3 initiatives at risk
Total 0 initiatives
Delivery 0 in progress
Impact 0 pending
Lead time 0 days average
New7 In review11 Completed12
Economic impact RUB mln
Total 0
Pending validation 0
Validated 0
Rejected 0
Top initiatives impact

AI resume scoringRUB 24 mln

Support RAGRUB 18 mln

CodeAgent reviewRUB 12 mln

Problem

AI exists.
Results do not.

  • Fragmented AI initiatives
  • No shared strategy or priorities
  • Data is chaotic
  • Hard to scale
  • Impact is hard to measure

Solution

AI operating model:
a systematic approach to AI adoption.

  • Unified strategy and initiative portfolio
  • Only relevant AI products
  • Data and technology as one platform
  • Repeatability and scalability
  • Transparent analytics and ROI

Outcome

The business gets a managed growth loop.

  • Revenue growth
  • Cost reduction
  • Better decision quality
  • Sustainable competitive position

Our Model

4 connected layers that create a continuous value cycle

1. Strategy

Define goals, value areas, and prioritize AI initiatives.

Value OKR Priorities

2. Platform

Build a reliable AI platform: data, models, tools, and infrastructure.

Data MLOps Tools

3. Governance

Set up processes, roles, and initiative governance.

Roles Processes Governance

4. Operations

Deploy, measure, train models, and scale the best solutions.

Adoption Metrics Growth
↻

5. Growth Cycle

Measure, train, improve, and scale AI as a permanent capability.

AI inside the platform

AI Agent Ensemble

Hover over an agent to see its role in the process.

Analyst

Builds the business case and shapes requirements.

📊 Analyst
We need a business case for ML scoring of loan applications
📊
Business case is ready 📄 download .pdf

Questions for business clarification:
1. Current approval rate for applications?
2. Expected monthly application volume?
3. Historical data for 2+ years?
Finance

Builds the financial model and estimates economic impact.

💰 Finance
Estimate the economic impact of ML scoring
💰
Financial model is ready 📊 download .xlsx

Key metrics:
NPV: +RUB 47 mln over 3 years
Payback period: 14 months
ROI: 3.2×
Security

Gets security approvals moving without delays.

🔐 Security
What is needed for security approval?
🔐
Requirements checked. Needed:
• Data classification: personal data to ISPDn
• Security committee: ~2 weeks
Request template is ready → fill in
Architect

Prepares technical and architecture documentation.

🏗️ Architect
Prepare an architecture description for the solution
🏗️
📄 Architecture Decision Record

Components:
Feature Store (Databricks)
Model Registry (MLflow)
Inference API: p99 < 200ms
DWH

Finds the right data marts and shapes data requirements.

🗄️ DWH
Where is the data for ML scoring?
🗄️
Found the right data marts:
• dma_scoring_features
Owner: Ivan Ivanov
Coverage: 98%, updated daily
→ open in catalog
DevOps

Prepares infrastructure, CI/CD, and monitoring for the ML product.

⚙️ DevOps
What is needed to deploy the model to production?
⚙️
Checklist is ready:
☑ Namespace: ml-scoring-prod
☑ 4 vCPU / 8 GB RAM
CI/CD: pipeline template
Grafana dashboard: connected
Developer

Builds a prototype and shows the idea in the interface.

💻 Developer
Build an MVP to demonstrate scoring
💻
MVP is ready in 2 hours → open demo

FastAPI + XGBoost + Streamlit
12 features from the data mart
Accuracy: AUC 0.81
AI assistant Orchestrator
Analyst
Finance
Security
Architect
DWH
DevOps
Developer

What You Get

-80% less time to formalize an idea

The idea brief is assembled from chat or a form without long processes or extra documents.

-90% fewer duplicates and lookalike solutions

Ideas are checked against existing initiatives before discussion and development.

-50% less time from idea to validated impact

Stage transitions are configured for your process and knowledge base.

100% management transparency

One portfolio, dashboard, and metrics. Risks are visible before they materialize.

RBAC role and access control

Role model, information isolation, and operation under your internal policies.

ROI controlled in the platform, not hidden in slides The platform connects initiatives, gates, resources, and expected impact so AI portfolio decisions can happen in business language.

Cases and Results

Real projects. Measurable impact.

FINTECH +32% revenue

AI models for recommendations and personalization.

RETAIL -18% costs

Inventory and supply chain optimization.

INDUSTRY +25% efficiency

AI assistants and predictive analytics.

What Exists Now and What Comes Next

Q1

Already in the product

  • Value funnel, transitions, and no-code gates
  • Product delivery, tasks, and priorities
  • AI assistant, brief, duplicates, product matching
  • Dashboard and at-risk initiatives
  • Roles, tenants, multi-tenancy
Q2

Resource and Capacity Planning

Who is overloaded, where capacity is available, and what happens if more initiatives move into delivery.

Connectors

We will connect communication tools, data sources, and internal finance systems to the conveyor.

Infrastructure Load

We will connect ideas to the infrastructure cost required to deliver them.

Q3

What-if Scenarios

Compare portfolio options by timelines, risks, and team capacity.

Report Builders

In the required format.

Meeting Transcriber Integration

All initiative meetings update its information and next steps.

Status Meetings

At operational and strategic levels: monthly AI adoption results, impact, insights, and KPI influence.

Q4

Financial Loop

Budgets, forecasts, plan/fact, and ROI across the AI initiative portfolio.

Portfolio Financial Model

Budget by initiative, product, and portfolio, including team and infrastructure cost.

Impact Validation

Compare expected and validated impact, control ROI and missed value.

Constraint-aware Prioritization

Budget allocation scenarios and initiative selection for maximum impact under resource constraints.

2027

Enterprise AI Office Loop

Deep integrations, management reporting, and AI recommendations for scaling the AI portfolio.

Integrations and Unified Context

SSO, webhooks, APIs, and synchronization with task trackers, knowledge bases, HR, and finance systems.

Management Reporting Builder

Custom reports, exports, and regular status packs for leadership, AI Office, and product teams.

AI Co-pilot for the Portfolio

Recommendations for prioritization, resource reallocation, duplicate detection, and scaling the best cases.

Want to know which AI initiatives should move first?

We will review your portfolio, find duplicates and risks, and show where AI Conveyor can create fast management value.

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