AI That Acts, Not Just Answers
We design and deploy autonomous AI agents that execute complex workflows — not chatbots that suggest next steps.
Agenticengineeringisourcorediscipline.WebuildAIsystemsthatgobeyondquestion-answering:agentsthatreadcodebases,orchestratemulti-steptasksacrossservers,generatedocuments,andoperate24/7withoutsupervision.Ourownoperationsrunonthismodel—everytoolweship,everyreportweproduce,everydeploymentwemanageisorchestratedbyautonomousagentsviaMCPprotocolacrossdistributedinfrastructure.
LLM + RAG Systems
Retrieval-augmented generation for enterprise. We implement production RAG pipelines with vector databases (pgvector, Qdrant), semantic chunking, re-ranking, and hybrid search. Typical RAG projects move from POC to production in weeks, not months.
Autonomous Agent Architectures
Multi-agent systems orchestrated via MCP (Model Context Protocol). Agents that code, deploy, test, and monitor — running on distributed VPS infrastructure. We design agent hierarchies with tool access, memory, and self-correction capabilities.
MLOps & Production Pipelines
From Jupyter notebooks to production. Model lifecycle automation, continuous training, A/B testing, monitoring, and rollback. Docker + Kubernetes deployments with GPU scheduling, automated evaluation, and drift detection.
Vector Databases & Semantic Search
High-precision, low-latency semantic search infrastructure. We benchmark and deploy pgvector, Qdrant, or Weaviate based on your scale. Index optimization, embedding model selection, and query pipeline design for millions of documents.
Frequently Asked Questions
Traditional chatbots respond to queries within a single turn. Agentic AI systems execute multi-step workflows autonomously — they can read files, call APIs, deploy code, generate reports, and self-correct errors across extended task sequences without human intervention.
A typical production RAG deployment takes 60-90 days from POC to production. This includes data ingestion pipeline setup, embedding model selection, vector database deployment, retrieval tuning, and integration testing. POC results are usually visible within 2-3 weeks.
It depends on scale and requirements. pgvector is ideal for teams already using PostgreSQL (simplicity, no new infra). Qdrant offers strong performance for large-scale deployments. Weaviate excels at hybrid search with built-in ML model integration. We benchmark all three against your data before recommending.
Yes. Our agents connect via MCP (Model Context Protocol) to any system with an API — ERPs, CRMs, databases, CI/CD pipelines, cloud infrastructure. We design the tool access layer, authentication, and error handling. Agents can also interact with legacy systems through screen scraping or file-based integration.