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Services

The full AI & data stack, one senior team

Five disciplines, one accountable team - from the first model to autonomous agents and the platforms that run them. Pick a starting point; we take it from discovery to a measured result.

AI development services are the design, build, and operation of production machine-learning and generative-AI systems on top of a company's own data. Welzin delivers them end to end across five disciplines: data science that turns raw data into decisions, predictive analytics that forecasts what happens next, AI engineering (retrieval-augmented generation, copilots, and autonomous agents grounded by evaluation and guardrails), MLOps and AIOps that keep models reliable in production through CI/CD, drift monitoring, and a model registry, and the product engineering that ships the resulting interface to users. One senior pod owns the path from the first model to a monitored system running in production, with no hand-offs to juniors, and every engagement is scoped to a single business metric it is accountable for moving. Welzin is based in Chandigarh, India, and works remotely with startups, SMEs, and enterprises across the United States, United Kingdom, United Arab Emirates, and India, handing over systems the client's own engineering team can run and extend without Welzin in the loop.

One senior pod, no hand-offs, and a single metric the work is accountable for moving.
The engineers who scope your system are the ones who build and operate it. Team size and depth follow the problem instead of a fixed package.
Data Science at Welzin
01 / 05

Data Science

We turn messy, real-world data into models and insight you can act on - rigorous, validated, and tied to a business question that matters.

  • Exploratory data analysisProfile and visualize raw data to surface patterns, gaps, and the questions worth modeling.
  • Statistical & ML modelingRigorous, validated models that turn the signal in your data into reliable predictions.
  • Experiment design & A/B testingDesigned experiments that measure real causal impact, not noise.
  • Customer & revenue analyticsSegmentation, LTV, and cohort analysis that explain what actually moves the business.
  • Feature engineeringEngineered, reusable features that lift model accuracy and stability.
  • Data storytellingClear narratives and dashboards that turn analysis into decisions.
Predictive Analytics at Welzin
02 / 05

Predictive Analytics

Forecast what happens next - demand, churn, risk, revenue - and wire those predictions straight into the decisions that depend on them.

  • Demand & revenue forecastingForecasts that plug straight into planning, inventory, and budgets.
  • Churn & propensity modelsSpot who will leave or convert in time to act on it.
  • Risk & fraud scoringReal-time scoring that flags risk before it costs you.
  • Recommendation enginesPersonalized recommendations that lift engagement and basket size.
  • Time-series & anomaly detectionCatch trends and outliers across your metrics as they happen.
  • Decision intelligenceWire predictions straight into the decisions that depend on them.
AI Engineering at Welzin
03 / 05

AI Engineering

Production Generative AI and autonomous agents - RAG, copilots, and tool-using workflows, grounded with evaluation and guardrails.

  • RAG & knowledge assistantsAssistants grounded in your documents, with retrieval and citations you control.
  • Copilots & autonomous agentsTool-using agents that plan and complete real workflows.
  • LLM fine-tuningTune and adapt models to your domain, voice, and tasks.
  • Evaluation & guardrailsMeasured quality and safety checks before anything ships.
  • Document & workflow automationAutomate document-heavy and repetitive workflows end to end.
  • Vector & semantic searchFast, meaning-aware search across your knowledge and data.
DevOps / MLOps / AIOps at Welzin
04 / 05

DevOps / MLOps / AIOps

Keep models and AI systems healthy in production - automated pipelines, monitoring, and the observability that catches drift before your users do.

  • CI/CD for modelsAutomated pipelines to train, test, and ship models safely.
  • Feature storesA single, governed source of features for training and serving.
  • Drift & quality monitoringCatch model decay and data drift before your users feel it.
  • Model registry & governanceVersioned, audited models with clear ownership and approvals.
  • Scalable inferenceServe models reliably at the latency and scale you need.
  • Cost & incident observabilityFull visibility into spend, health, and incidents in production.
Engineering Development at Welzin
05 / 05

Engineering Development

Full-stack product engineering around your AI - APIs, web and mobile apps, cloud infrastructure, and the data platforms that ship and scale it.

  • Web & mobile applicationsPolished, production web and mobile apps built around your AI.
  • API & backend engineeringRobust APIs and services that scale with your product.
  • Cloud & DevOpsCloud infrastructure and automation built for reliability.
  • Data platforms & lakehousePipelines, warehouses, and lakehouse foundations that feed it all.
  • System architectureArchitecture that holds up as data and traffic grow.
  • Platform modernizationModernize legacy systems without stalling the roadmap.
Models

Backed by every frontier model, owned by none

Frontier APIs and open weights alike - the model behind your system is chosen per problem and benchmarked on your data, never by default. When a better one ships, your system can move.

  • GPT-5OpenAI
  • ClaudeAnthropic
  • GeminiGoogle
  • LlamaMeta
  • MistralMistral AI
  • DeepSeekDeepSeek
  • GrokxAI
  • SonarPerplexity
  • WhisperOpenAI
  • Stable DiffusionStability AI
  • QwenAlibaba
  • KimiMoonshot AI
  • MidjourneyMidjourney
  • ElevenLabsElevenLabs
  • NeMoNVIDIA
  • OllamaLocal models
Technology stack

The platforms behind every engagement

76 technologies / 8 disciplines

Cloud & Infrastructure

  • AWS
  • Google Cloud
  • Azure
  • Kubernetes
  • Docker
  • Terraform
  • GitHub Actions
  • Vercel

Data Engineering

  • Databricks
  • Snowflake
  • BigQuery
  • Apache Spark
  • Apache Hadoop
  • Apache Kafka
  • Apache Flink
  • Apache Airflow
  • Airbyte
  • dbt
  • Trino
  • pandas
  • Polars
  • Ray
  • Prefect

Databases & Storage

  • PostgreSQL
  • MongoDB
  • Redis
  • ClickHouse
  • DuckDB
  • Neo4j
  • Supabase

ML & Deep Learning

  • PyTorch
  • TensorFlow
  • scikit-learn
  • Jupyter
  • Hugging Face
  • NVIDIA NeMo
  • ONNX
  • DeepSpeed

GenAI & Agents

  • OpenAI
  • Anthropic
  • Gemini
  • Llama
  • Mistral
  • Vertex AI
  • Google ADK
  • vLLM
  • Ollama
  • LangChain
  • MCP
  • CrewAI
  • kagent
  • n8n
  • ElevenLabs
  • Firecrawl
  • Crawl4AI

Retrieval & Search

  • Pinecone
  • Qdrant
  • ChromaDB
  • Milvus
  • OpenSearch
  • Elasticsearch

MLOps & Observability

  • MLflow
  • Kubeflow
  • DVC
  • Langfuse
  • Grafana
  • Prometheus

Analytics & Applications

  • Tableau
  • Power BI
  • Apache Superset
  • Streamlit
  • Gradio
  • FastAPI
  • Next.js
  • Python
  • Wix

Answers to questions about

What AI development services does Welzin offer?

Welzin works across five disciplines: data science, predictive analytics, AI engineering (RAG, copilots, and agents), MLOps/AIOps, and the product engineering that ships them. One senior pod owns the path from the first model to a system running in production.

What is the difference between AI engineering and MLOps at Welzin?

AI engineering builds the system: production RAG, copilots, and autonomous agents grounded with evaluation and guardrails. MLOps/AIOps keeps it running: CI/CD for models, feature stores, drift monitoring, a model registry, and scalable inference.

Not sure where to start?

Tell us the metric you want to move. We'll map the highest-leverage path on a free call.

Talk to a senior engineer

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