LLM apps that earn their keep
Copilots, search, summarization and document workflows tied to measurable KPIs. We ship features that move metrics, not demos that stall in pilot.
Services
AI, ML and Data Engineering Services That Ship to Production
YuSMP Group builds production-grade GenAI applications, RAG systems, AI agents and the data pipelines that feed them. 80+ senior engineers across Yerevan deliver in the CET / East-Coast US overlap, with a model-vendor-neutral stack across OpenAI, Anthropic and Bedrock. Every engagement is GDPR-aligned and structured for EU AI Act readiness from day one — not retrofitted later.
We deliver a connected scope: GenAI applications, retrieval-augmented generation, multi-step AI agents, classical machine learning, the data engineering that makes any of it trustworthy, and the MLOps that keeps it running. Our teams stay model-vendor neutral — Anthropic, OpenAI, open-weight via Bedrock or self-hosted — and pick the stack against your data residency, latency and cost envelope. Governance is built in: GDPR, ISO 27001 controls, SOC 2 Type II in progress, HIPAA-capable delivery, and an EU AI Act risk-classification step on every engagement.
AI & Data Services We Deliver End-to-End
RAG that actually retrieves
Hybrid retrieval, reranking, evaluation and observability. We tune on your data, your queries and your acceptance criteria, not on toy benchmarks.
MLOps that scales
Reproducible training, model registries, shadow deploys and monitoring for drift and bias. Every model has a clear path from notebook to production.
Data platforms that hold up
Modern data stack on Snowflake, BigQuery or Databricks. ELT with dbt, contracts between teams, and lineage your auditors and analysts can both trust.
EU AI Act + NIST AI RMF conscious
Risk classification, transparency notices, technical documentation and human oversight built into the product under both the EU AI Act and the NIST AI Risk Management Framework — not bolted on before an audit. State-law aware (Colorado AI Act 2026, NYC AEDT, CCPA ADM rules).
Evals as a habit
Golden datasets, automated regressions and offline evals on every prompt and model change. Quality is a number, not a feeling.
Model-Neutral AI and Data Stack
Python
PyTorch
LangChain
LlamaIndex
OpenAI
Anthropic
Mistral
Bedrock
Vertex AI
Azure OpenAI
pgvector
Qdrant
Snowflake
Databricks
dbt
MLflow
From Discovery to Production in 8-14 Weeks
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01
Discovery
We map use cases against business value, data readiness and dual risk classification — EU AI Act risk class plus NIST AI RMF Govern/Map/Measure/Manage profile — then pick the two or three with the strongest payoff.
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02
Design
Reference architecture, evaluation harness, data pipelines and human-in-the-loop boundaries are designed before any model is wired into the product.
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03
Build
Two-week sprints with offline evals, A/B tests on real users, prompt and model version control, and observability for cost, latency and quality.
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04
Run
Drift, bias and cost dashboards, scheduled re-evaluations, and a backlog tied to model and regulatory changes across the EU (AI Act), the US (NIST AI RMF, state laws) and the major providers.
Engagement models
Fixed Price
For bounded AI proofs of value, RAG pilots and data platform builds with crisp acceptance criteria and a fixed deadline.
Time & Materials
For evolving AI products where prompts, models and metrics change weekly. Senior squad, weekly demos, monthly capacity reviews.
Dedicated Team
A long-running AI and data squad embedded in your product organization, owning data quality, model lifecycle and compliance documentation.
Selected work
AdTech · Lead generation
ARIA
Single-page Tilda landing with Telegram-bot lead capture for an ad agency — shipped in two weeks, US & EU ready.
Blockchain · Crypto
Cyber Token
End-to-end ERC-20 token launch — Solidity contract, security audit, exchange listings, MetaMask checkout on the project site.
FinTech · Lending
Loan Conveyor
A high-throughput loan decision engine on Laravel — automated scoring, credit-bureau integration, and 10x faster decisions for US & EU lenders.
Why US & EU teams pick YuSMP
GDPR-aligned · ISO 27001 ready · SOC 2 Type II in progress · HIPAA-capable · CCPA-acknowledged
Aligned across CET & ET time zones
Data engineers and ML leads on a CET workday with East-Coast US overlap (9 AM–1 PM ET), on your standups, with same-day decisions on prompts, models and rollouts.
Senior-only engineering
ML engineers and data platform leads with shipped US & EU production systems. We do not learn vector databases on your roadmap.
GDPR + CCPA & ISO 27001 ready
Region-locked hosted endpoints (EU data residency · US options on request), zero-retention configurations, signed DPAs and BAAs, ISO 27001-aligned controls with SOC 2 Type II in progress. PCI DSS scoping where ML touches payments; HIPAA-capable where ML touches PHI.
Dual AI governance is part of every architecture decision: in the EU we apply the AI Act (classify each use case, document the system, set human-oversight points, prepare evidence for high-risk scenarios such as hiring, credit scoring and biometric processing); in the US we apply the federal AI executive orders, NIST AI RMF (Govern / Map / Measure / Manage), OMB M-24-10 expectations, and state-law screens — Colorado AI Act (effective 2026), NYC AEDT (Local Law 144), the NY AI Bill of Rights and CCPA / CPRA automated-decision-making rules.
Frequently asked questions
Which LLM should we use — OpenAI, Anthropic, Mistral or open-source?
We benchmark candidate models on your real tasks before recommending. Region-locked Mistral / Claude / OpenAI on Bedrock, Vertex or Azure OpenAI (EU-hosted for EU clients, US-hosted for US clients), and open-source models on EU or US clusters, often beat the obvious choice on cost and data residency once you measure end-to-end latency and accuracy.
Is RAG enough or do we need fine-tuning?
For most knowledge-bound use cases, retrieval-augmented generation with strong evals beats fine-tuning. We move to fine-tuning or LoRA only when style, latency or cost targets cannot be met by RAG, and we measure the gain.
How do you keep AI features safe and on-brand?
Prompt versioning, deterministic evals, red-team prompts, output filters and human-in-the-loop on high-stakes paths. Every release ships with a measurable quality and safety dashboard, not just a vibe check.
Where does the EU AI Act apply to our product?
Most SaaS products use AI in limited-risk or minimal-risk roles, requiring transparency notices and basic logging. We help classify your use cases, document the system, and prepare for high-risk obligations if hiring, credit or biometrics are in scope.
How do you handle US AI compliance (NIST AI RMF, state laws like CO AI Act 2026)?
For US deployments we map controls against the NIST AI Risk Management Framework (Govern / Map / Measure / Manage), align with the federal AI executive orders and OMB M-24-10 expectations, and pre-screen use cases against state-level laws — the Colorado AI Act (effective 2026), NYC AEDT (Local Law 144), the NY AI Bill of Rights and CCPA / CPRA automated-decision-making rules. We document risk class, transparency notices, human oversight and impact assessments in a single AI system card per deployment.
Can AI features run on US-only or EU-only data?
Yes. We use region-locked endpoints from Bedrock, Vertex, Azure OpenAI and Mistral — EU-hosted for EU clients (EU data residency), US-hosted for US clients (US options on request, BAAs available for HIPAA-capable workloads). Self-hosted open models on EU or US clusters when residency is critical. DPAs, BAAs and zero-retention configurations are part of every architecture review.