LectureHSE · May 23, 2026
AI Platform Basics
A basic introduction: what an AI product platform is made of and why demos do not become production by themselves
/ AI Platform Basics · HSE 2026
Slide contents
1. AI Platform Basics
A basic introduction: what an AI product platform is made of and why demos do not become production by themselves
2. Alexander Polomodov
Technical Director & Fellow, T-Technologies
Architecture and engineering R&D
AI adoption in SDLC
Focus: platform, not chat
3. 01. Why an AI platform
Moving from impressive demos to a repeatable way of shipping AI products
4. AI features become production loops
Then quality, access, cost and owners matter
Repeatability — Teams use shared templates, SDKs and release rules.
Control — Access, audit and fallback become part of the platform.
Economics — Cost per solved task.
5. AI demos do not become products alone
Models do not solve operations
Teams reselect the stack
Quality failures are not reproducible
Rights and knowledge drift
Cost is detached from value
6. 02. AI platform components
Model layer, context, tools, security, evals, observability and economics
7. What layers make up an AI platform
Lower layers provide infrastructure and control; upper layers provide product speed
8. AI platform component map
A minimal vocabulary before diving into AIOps
9. Gateway controls the platform
Routing, limits, fallback, audit, cost
What the gateway gives
One point for APIs and self-host.
Routing, quotas, audit and key protection.
What stays with product
Choose scenario, quality level and latency.
Define fallback and task cost.
10. Quality starts with context
Context quality decides answer quality
Knowledge: retrieval and freshness
Tools: rights and approvals
Memory: user and process
Guardrails: see/say/do
11. 03. AI product lifecycle
From idea and spec to eval, release, monitoring and improvement
12. AI product lifecycle
The platform turns a one-off experiment into a repeatable release process
13. Spec and eval anchor behavior
They preserve the link between intent and behavior
Specification
Intent, data, rights
Scenario beyond implementation
Business-platform contract
Evaluation
Test set, baseline, review
Model/prompt/retrieval effect
Release basis beyond demo
14. AI features ship as changes
A model can change without a code diff
Version prompt, retrieval, model
Evals, shadow, canary, review
Fallback and owner upfront
Observability: quality, cost, signals
15. 04. Platform operating planes
Build-time, run-time, control-plane and cost-plane as four separate management tasks
16. The platform works across four planes
Mixing these tasks creates an expensive and poorly controlled runtime
17. Platform rails, product accountability
Platform gives rails; product owns task
What the platform provides
Gateway, RAG, evals, audit
Golden path and CI
Security, access, data, cost
What product owns
Scenario, user, success, risk
Domain knowledge, UX, feedback
Readiness decision
18. Guardrails live in the platform
Otherwise each team reinvents safety
Access before retrieval/tool/answer
PII and secrets through policy
Risky actions require approval
Exceptions are explicit
19. 05. Operating model and maturity
Who owns the platform, scenarios, data, risk and economics
20. AI platform accountability model
AI product quality emerges at the intersection of platform, product, data and risk
21. How to measure AI platform maturity
Maturity is not the number of models; it is speed, quality, reliability and economics
22. What we take into AIOps / LLMOps
Next: operations, agents, evals, economics
Platform = repeatable AI launch
Minimum: gateway, context, evals
Platform provides rails
Product owns quality and risk
Next step: manage the AI system as a working product
23. Materials
For further reading
OpenAI, Claude, Gemini, Yandex docs.
LangChain platform concepts.
Cloud/Data Platform/DataOps lectures.