Practice 01
Generative AI & LLM apps
Copilots, RAG knowledge assistants, document intelligence and agentic workflows — grounded in your data, evaluated and guarded for production.
RAGAgentsFine-tuningEvals
Clevanta Technologies
Intelligence,engineered forwhat comes next.
We design and ship generative AI products, LLM applications, data platforms and custom machine learning — built to run in production, not just in a demo.
Model · in productionlive
Generative AILLM ApplicationsData EngineeringCustom MLAnalyticsAgentic AutomationAI ConsultingChatbots
01 — Services
Four practices.One intelligent stack.
From the first strategy workshop to models serving real users, our teams cover the full path — strategy, data, models and the software around them.
Data engineering & analytics
Pipelines, lakehouses and dashboards that make your data AI-ready — and decisions faster.
ETL/ELTLakehouseBI
AI consulting & custom ML
Roadmaps, feasibility, and bespoke models for forecasting, vision, NLP and recommendation.
StrategyMLOpsForecasting
Chatbots & intelligent automation
Conversational assistants for customers and teams, plus automations that remove repetitive work across your tools.
ChatbotsVoiceIntegrationsWorkflow
02 — Process
From ideato impact,in four moves.
03 — Use cases
What we typicallyput into production.
GenAIUse case 01
Knowledge assistant over internal documents
Staff ask questions in plain language and get cited answers from policies, manuals and tickets.
Data + AIUse case 02
Forecasting on a modern data platform
Unified pipelines feed demand and revenue forecasts straight into the dashboards teams already use.
AutomationUse case 03
Document intake and triage, automated
Emails, forms and PDFs are read, classified and routed to the right system without manual re-keying.
04 — Stack
Tools wetrust in production.
OpenAIAnthropic ClaudeGoogle GeminiLlamaLangChainLlamaIndexHugging FacePyTorchscikit-learnPythonSQLSparkdbtAirflowSnowflakeDatabricksPostgresPineconePower BIAWSAzureGoogle CloudDockerKubernetesMLflowFastAPIReact
05 — FAQ
Questions,answered.
It depends on scope. We usually start with a short discovery phase, then deliver a working prototype before scaling to production. We'll share a concrete timeline after our first conversation.
06 — Let's build