One operating environment for the entire AI initiative lifecycle.
Portfolio, owners, gates, delivery, adoption, and validated impact — supported by an ensemble of specialist AI agents.
First step — a 20–30 minute conversation. We review your context and adoption process, then decide whether AI Conveyor is the right next format.
AIOM can be run manually. But it works better in a system built around AIOM.
The methodology can start in a corporate wiki, spreadsheets, task trackers, whiteboards, or other tools. But once initiatives grow, a single working loop becomes necessary.
After diagnostic / when scaling
A platform that turns AIOM into daily operational work
AI Conveyor is the platform layer on top of AIOM: one loop where initiatives, owners, gates, tasks, and impact live in the workflow instead of scattered files.
- single flow of AI initiatives;
- initiative portfolio, statuses, and owners;
- gates, risks, and approvals;
- delivery tracks and tasks;
- expected and validated impact;
- reporting for AI function and leadership.
Illustrative product view and sample data
3 initiatives at risk
New 7
In assessment 11
Completed 12
Economic impact
RUB mln
Main initiatives
impact
AI resume scoringRUB 24 mln
Support knowledge baseRUB 18 mln
Code review agentRUB 12 mln
AI inside the platform
AI Agent Ensemble
Illustrative product view and sample data
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 scoring loan applications
A
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?
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 scoring
F
Financial model is ready download .xlsx
Key metrics:
NPV: +RUB 47 mln over 3 years
Payback: 14 months
Return on investment: 3.2×
Key metrics:
NPV: +RUB 47 mln over 3 years
Payback: 14 months
Return on investment: 3.2×
Security
Gets security approvals moving without delays.
Security
What is needed for security approval?
S
Requirements checked. Needed:
• Data classification: personal data
• Security committee: ~2 weeks
Request template is ready fill in
• Data classification: personal data
• Security committee: ~2 weeks
Request template is ready fill in
Architect
Prepares technical and architecture documentation.
Architect
Prepare an architecture description for the solution
AR
Architecture decision
Components:
feature data mart
model registry
Inference API: p99 < 200ms
Components:
feature data mart
model registry
Inference API: p99 < 200ms
Data
Finds the right data marts and shapes data requirements.
Data
Where is the data for scoring?
D
Found the right data marts:
• scoring_features
Owner: data team
Coverage: 98%, updated daily
open in catalog
• scoring_features
Owner: data team
Coverage: 98%, updated daily
open in catalog
Infrastructure
Prepares environments, deployment, and monitoring for the AI product.
Infrastructure
What is needed to move the model into production?
IN
Checklist is ready:
• production environment
• 4 CPU / 8 GB memory
Deployment: process template
Monitoring connected
• production environment
• 4 CPU / 8 GB memory
Deployment: process template
Monitoring connected
Developer
Builds a prototype and shows the idea in the interface.
Developer
Build an MVP to demonstrate scoring
DEV
Prototype is ready in 2 hours open demo
interface + model + data mart
12 features from the mart
Accuracy: AUC 0.81
interface + model + data mart
12 features from the mart
Accuracy: AUC 0.81
AI assistant
Orchestrator
Analyst
Finance
Security
Architect
Data
Infrastructure
Developer