Measured business impact
Outcomes reported against approved AI implementations, presented for executive review.
- Hours Automated
- 9 hours
- Cost Savings
- —
- Revenue Impact
- —
- AI Deployments
- 3
- Published Case Studies
- 1
Operational hours recovered through verified AI deployments.
Verified operating cost reductions delivered for client organisations.
Revenue created or protected through deployed AI solutions.
Implementations reviewed and approved against the Evidence Standard.
Documented implementations with published business outcomes.
- VerificationVerified
- SpecialisationAI Agents
- AI Deployments3
- AI Case Studies1
- Industries Served5
- LocationVietnam
How this is verifiedWhat the platform has established, and what remains builder-declared.
- Identity and organisation
- Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
- AI Deployments
- An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
- Business outcomes
- Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
- Evidence
- Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
- Currency
- Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
- Traceability
- Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.
The platform does not independently audit client systems or financial records. How this is verified
Top business outcomes
Reported by the builder on the record that produced them, in the original reporting period. Nothing is annualised or converted.
- Hours Automated9 hours
period not specified
Automated 9 hours (period not specified)
Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.
Business Outcome Highlights
Measured outcomes taken from published AI Case Studies, shown with the reporting period the builder stated. Each figure links to its case study.
Capabilities: declared and demonstrated
12 of 24 listed capabilities are supported by an approved deployment or published case study on this platform. The rest are Builder-declared and are not presented as verified.
Implementation capability
- AI Agents
- AI Agent2
- Computer Vision1
- Knowledge Assistant2
- Process Automation1
- RAG System2
Technologies
- Chroma
- Docker3
- FAISS1
- FastAPI3
- LangChain
- LangGraph
- Ollama1
- OpenAI Platform
- Pandas
- PostgreSQL
- PyTorch1
- Python3
- TensorFlow
Industries
- Telecommunications1
- Data & Analytics
- Digital Health
- FinTech
- Cross-Industry / Horizontal
AI Deployments (3)
Technical implementation records: what was built, the technologies used and the scope delivered. Each was reviewed before publication.
Production MLOps solution for customer churn prediction
End-to-end churn prediction service with 23 REST API endpoints, auto-retraining, manual labeling, and role-based access contro
By Ivan SazontovTraffic Sign Detection (CV)
Real-time traffic sign recognition system for 55 classes, deployed via WebSocket with browser camera
By Ivan SazontovRAG-powered portfolio assistant for technical documentation
A fully local RAG system that answers questions about technical portfolios, codebases, and documentation.
By Ivan SazontovAI Case Studies (1)
Business stories: the problem, the approach and the measured result for the organisation. Each is published with its supporting evidence.
About
Machine Learning Engineer · AI Engineer
3+ years of production experience building end-to-end ML systems. Built and deployed RAG systems (8,361 chunks, FAISS, local LLM, FastAPI, Docker), real-time computer vision (YOLO + ResNet, 94% mAP), and MLOps APIs (23 endpoints, auto-retraining, auth).
Master's in Applied Mathematics. Several publications in computer vision. Strong in Python, PyTorch, FastAPI, Docker, SQL.
Currently based in Vietnam. Open to remote or local roles. Looking to build AI products that actually work in production — agents, RAG, automation, and scalable ML systems.
Portfolio: greencat1.tech
GitHub: github.com/greencat1
Ready to discuss your AI implementation?
Tell Ivan Sazontov what you are trying to automate and the outcome you need. Your message goes directly to the builder, with a copy kept in your Organisation Workspace.