Rinavera is a boutique AI engineering practice for teams that need AI features to work in production.
The work spans architecture through implementation, with production constraints (latency, reliability, cost, state, privacy and integration) in view from day one.
Run by the person who scopes it.
MSc-qualified AI engineer and a vetted member of the Toptal network (top 3% of applicants). Four years of production ML across Matter (via the Toptal network), Crimson Education, Evidentful and Siglum: simulation environments and multi-agent pipelines for AI-powered mobile features, admissions prediction at scale, court-grade interview automation, and NLP pipelines for document-heavy products. Research background in medical image segmentation and datacenter energy optimization.
What experience means for clients
- Engagements are scoped by the engineer who delivers them. No account-management relay.
- Advice is the same advice an operator would give: cost, latency and failure modes included.
- Vendor recommendations carry no incentive beyond being correct.
How the work is run.
Know what quality, latency and cost look like before changing the system.
Production systems need explicit handling for edge cases, regressions and degraded model behavior.
Teams should be able to understand what the system is doing and where it is failing.
Use the simplest architecture that gives the reliability and control production requires.
Start small. Scale only what proves itself.
A short, paid engagement producing a diagnosis and a plan; the default first step when the problem is not yet fully shaped.
A bounded build with measurable exit criteria, used to validate an approach before larger commitment.
Complete delivery of a defined capability: architecture, implementation, evaluation and handover.
Ongoing specialist capacity or part-time technical leadership, for teams that need continuity rather than a project.
We run our own AI infrastructure.
DXAP is the agency's own two-node GPU cluster, built and operated in-house. Durable state lives on NAS-backed storage, inference runs locally on vLLM behind an OpenAI-compatible gateway, and coding agents reach the platform only through a policy-gated access layer where every command is audited.
New evaluation, routing and deployment patterns are proven here before they reach client systems, and running inference in-house keeps day-to-day engineering costs close to zero.
What we cover.
Core stack today: Python, TypeScript, PyTorch, TensorFlow/Keras, FastAPI, Next.js/React, Docker + GitHub Actions, PostgreSQL/Supabase, AWS, Azure and GCP, and Slurm for HPC runs.
Why boutique.
Large agencies sell teams; small engagements get juniors after the pitch. A boutique practice inverts that: engagements are senior-led, delivered by the engineers who scope them, with capacity added only around a senior core.
That structure has consequences: fewer parallel engagements, direct communication, and opinions that arrive with tradeoffs attached.