What is the modeled unit cost per query at 10x and 100x user scale, and can distillation, routing, or semantic caching compress it?
Architectural Pillar
AI Product Engineering
Engineering AI-native products, feasibility validation, latency/cost trade-offs, and staged production rollout.
Problem Territory
Engineering Problem Territory
Problem class: Prototype to production, unit economics, and staged rollout
Problem Territory
The chasm between an impressive prototype running on an external API and a commercially durable AI product is where most early initiatives fail. Real viability challenges only emerge under scale: when token invoices outpace subscription revenue, response latency frustrates end users, and edge-case hallucinations erode product credibility.
The AI Product Engineering pillar focuses on bridging this gap: validating feasibility, modeling unit economics, optimizing model routing to balance speed and cost, designing resilient UX for non-deterministic responses, and establishing staged technical milestones that preserve architectural integrity.
Context
When This Expertise Is Relevant
Typical engineering scenarios and system lifecycle stages where architectural engagement creates maximum leverage.
A proof-of-concept validates capability, but token costs, response latency, or edge-case variance prevent commercial viability.
Founders and engineering leaders must navigate build-vs-buy decisions across proprietary frontier APIs, open-source weights, and hosted inference.
Product teams need to design user experiences that communicate probabilistic uncertainty and handle multi-second inference latency gracefully.
Architects are structuring a staged release roadmap where early customer feedback hardens models without accumulating technical debt.
Architecture Decisions
Key Architectural & Decision Questions
Structural decisions that govern system reliability, component boundaries, and production unit economics.
Which functional capabilities require frontier commercial models, and which can be offloaded to fine-tuned or local open-weight models?
How should streaming UX, optimistic UI updates, and degraded fallback states be architected for variable inference latency?
What telemetry and evaluation loops should be integrated into product workflows to continuously measure and improve model output quality?
Deliverables
Typical Engineering Deliverables & Decisions
Concrete technical artifacts, evaluation benchmarks, and architectural decision records.
Prototype-to-Production Migration Blueprint with modular service decomposition and data contracts.
Unit Economics and Latency/Cost Trade-off Model with sensitivity analyses under varying user load assumptions.
Build vs. Buy Evaluation Report for model providers, hosting infrastructure, and orchestration frameworks.
Staged Rollout Plan with objective gating metrics for advancing between testing stages and full production release.
Scope Boundaries
What This Work Is Not
Clear scope boundaries preventing commoditized service interpretations and ensuring focused technical leadership.
- Not full-stack mobile/web agency development, landing page creation, or UI visual design.
- Not marketing launch campaigns, customer acquisition, or conversion rate optimization.
- Not bolting speculative AI buzzwords onto software that is better served by deterministic algorithms.
- Not recommending prematurely complex distributed infrastructure where managed, cost-effective services suffice.
Evidence Base
Related Research & AI Lab Studies
Empirical validation and laboratory fault-injection studies verifying architectural patterns.
When an AI Product Does Not Need an LLM: Deterministic Inference, Provenance and Controlled Uncertainty
An engineering decision framework for choosing between generative LLMs and deterministic inference in production runtime. Why replacing an explicit knowledge base with stochastic prompts erodes reproducibility and trust, how to structure claim-level provenance, and which objective criteria determine where a foundation model creates genuine value versus unnecessary cost and liability.
Large PDF Translation Is a Workflow System, Not a Translation API
An architectural investigation into translating voluminous, structurally complex PDF documents. Why the naive abstraction of "PDF in → translation API → translated PDF out" breaks down across multi-hundred-page enterprise documents, how to structure an intermediate representation (DocumentIR), and why pipeline resumability, checkpoints, and layout reconstruction matter far more than model choice.
Simple Before Distributed: When an AI System Actually Needs Workflow Orchestration
An architectural complexity framework for AI workloads: identifying when synchronous execution suffices, and when queues, distributed workers, and durable workflows are genuinely required based on a 12-factor evaluation matrix.
When Green Tests Lie: Coding Agents and Real Runtime Verification
An investigation of a reproduced production failure in vitkovskyi.com: despite extensive automated test suites and green agent implementation reports, the enhanced JavaScript form submitted FormData (multipart/form-data), which the PHP endpoint deliberately rejected with HTTP 415. Mocked Playwright tests failed to detect the defect. The targeted engineering repair converted the payload to URLSearchParams, aligned CSP for Cloudflare Turnstile, reconciled cross-runtime field limits, and introduced real browser-to-PHP integration tests with zero endpoint mocks.
Perfume Engine: An Evidence-Backed Recommendation Prototype Without a Runtime LLM
Perfume Engine began as a broad fragrance-intelligence concept, but was deliberately narrowed during an early iteration into a controlled validation prototype: 12 perfume identities, 6–8 editorial mappings, exact search, manual evidence collection, and deterministic recommendation logic. The experiment was not designed to inject an LLM at every layer, but to verify whether a recommendation system could remain explainable, reproducible, and provenance-first. Because Perfume Engine subsequently evolved into a broader system, this study documents a specific historical architecture experiment rather than the full current product architecture.
Engagement
Discuss System Architecture
If you are designing a complex AI system or encountering production stability bottlenecks, prepare your requirements for asynchronous review.
Initiate a Structured Discussion
Review qualification criteria and advisory formats on the Work With Me page to evaluate architectural fit.