AI engineering, without the hype
Poplavsky Solutions is a founder-led AI engineering studio. Every project is a production system, not a proof of concept.
Strategy and advisory for companies starting their AI journey. On-prem deployment for companies whose data can’t go to the cloud. Inference performance and cost optimisation for companies whose AI bill is out of control.
Hands-on engineering, end to end. Fixed-fee where it makes sense. No demos that never ship.
Trusted by
Certified
AI Strategy & Technical Advisory
Senior AI thinking — before, during, and after the build.
AI Strategy Sprint
A written roadmap before you commit to a build. Capability and opportunity assessment, use-case prioritisation with ROI estimates, technical architecture recommendations, build-vs-buy decisions, and a 6-month implementation roadmap.
Technical Architecture Review
A senior second opinion for teams already mid-build. Review of current architecture and tech choices, identification of risks and dead-ends, and recommendations for stack changes with effort/impact estimates.
Fractional Head of AI
Senior AI leadership one to two days a week, without a full hire. Strategic direction, hiring support, vendor decisions, stakeholder communication, and hands-on architectural input on critical builds.
Hourly Technical Advisory
Ad-hoc deep-dive questions: model selection, infrastructure tradeoffs, evaluation strategy, specific technical problems. Booked in 10-hour blocks.
Where we help
- “We want to use AI but don’t know where to start.”→AI Strategy Sprint
- “We’re mid-build and want a senior second opinion.”→Technical Architecture Review
- “We need AI leadership but can’t justify a full hire yet.”→Fractional Head of AI
- “We have a specific technical question and need an expert for a few sessions.”→Hourly Technical Advisory
End-to-end AI Systems
Complete AI systems, architecture to production — deployed on-prem when your data can’t go to the cloud.
On-prem Readiness Assessment
For prospects unsure if they need on-prem or how to scope it. Data sensitivity and compliance review, use-case prioritisation, hardware and infrastructure sizing, model recommendation, and an implementation roadmap with cost estimates.
Full On-prem Deployment
Hardware specification and procurement support, model selection with fine-tuning where it adds value, inference stack deployment, RAG over internal documents, integration with existing systems, monitoring and security, and team training with handover documentation.
Operations Retainer
For clients post-deployment who want ongoing monitoring, model updates, and architecture evolution. Sold only after a completed deployment.
What we deploy
- LLMs, VLMs, and computer vision models on your infrastructure
- RAG systems over internal documents and knowledge bases
- Inference stacks (vLLM, SGLang, NVIDIA Triton) tuned and profiled for your hardware
- Integration with existing backend systems
- Monitoring, security, and compliance documentation
Performance & Cost Optimisation
Faster inference, lower bills — throughput, latency, and cost-per-token are one engineering problem.
Performance & Cost Audit
A pure diagnostic. Full analysis of prompt patterns, model selection, and token usage by feature. Caching opportunity analysis, model-routing recommendations, prompt-compression review, RAG retrieval efficiency, and a serving-stack review: engine configuration (vLLM, SGLang, NVIDIA Triton), quantisation, KV-cache and batching behaviour, and GPU utilisation profiling. Delivered as a prioritised written roadmap with savings estimates and a working session with engineering and finance.
Audit + Implementation
Audit included, plus implementation of the top three to five highest-ROI items. Before-and-after measurement with a monitoring dashboard, code handover and documentation, and a 2-week stabilisation period.
Performance Retainer
Continuous cost and architecture work for teams who want someone watching the bill. Monthly cost analysis and recommendations, ongoing prompt and model optimisation, pre-launch cost review for new features, and vendor-negotiation support. Sold only after a completed implementation engagement.
How we work
1. Understand the real constraints. Every engagement starts with the facts: your infrastructure, data, compliance, team capacity, deadlines, current AI spend. The constraints shape the work.
2. Design the right approach. Whether it’s a strategy roadmap, a deployment architecture, or an optimisation plan — the design fits your actual environment, not an idealised setup.
3. Deliver against measurable outcomes. Fixed-fee scope where it makes sense, real deliverables, and production standards from day one. No slide decks pretending to be systems.
4. Hand over for long-term ownership. Documentation, team enablement, and the context you need to operate, evolve, and maintain the work after we step away.
About
Alexander Poplavsky
Founder & Technical Lead
Poplavsky Solutions is a founder-led AI engineering studio focused on systems that work in production. Deep experience in LLM platforms and inference optimisation, on-prem AI infrastructure, computer vision, and production software engineering — across consumer AI, hard-tech, and aerospace contexts.
Every engagement is hands-on, technical, and focused on delivering systems that teams can deploy, operate, and maintain long-term.
What clients say
“Alexander built us a self-hosted AI system for scientific analysis of our manufacturing data — nothing leaves our infrastructure, and our engineers run it without any Python. What used to take days of manual Excel work is now a single command, with sharper insight than we could produce by hand.”
“Alexander designed and delivered the Data & AI course for Ironhack’s partnership with Azerbaijan’s education initiative for students. The material was aligned with latest industry standards, students stayed motivated throughout. A reliable, senior partner for AI education.”
Get in touch
If you’d like to discuss a project, schedule a call or contact@poplavsky.solutions