Production-Grade Generative AI & Autonomous Retrieval Systems.
We build private, domain-specific AI architectures for businesses that cannot afford hallucinations or data leakage. From pgvector semantic pipelines to autonomous multi-step agents.
Core AI Capabilities
Engineered Beyond Generic API Wrappers.
Private RAG Architectures
Retrieval-Augmented Generation systems grounded in your proprietary documents. We implement chunking strategies, hybrid keyword-vector search, and re-ranking models for deterministic accuracy.
- pgvector, Pinecone, and Qdrant vector databases
- Custom embedding models and semantic chunking
- Source attribution and citation verification
Autonomous Workflow Agents
Multi-step agentic systems that use function-calling and tool-use to execute complex workflows: CRM updates, document extraction, automated reporting, and dynamic API orchestration.
- Structured JSON output with Zod schema validation
- Human-in-the-loop escalation checkpoints
- LangGraph and stateful agent loop architectures
Local & Private Cloud Deployment
Self-hosted open-weights LLMs (Llama 3, Mistral, Qwen) on private VPCs or on-premise GPU clusters using Ollama, vLLM, or TGI. Zero data leaves your controlled security perimeter.
- Complete data sovereignty & zero third-party logging
- Quantization (GGUF, AWQ, FP8) for inference efficiency
- Cost-predictable compute infrastructure
Security & Guardrails Engineering
Prompt injection defense, PII masking, output guardrails (NeMo Guardrails, Llama Guard), and latency optimization pipelines designed for mission-critical enterprise environments.
- Role-based data access filters on vector queries
- Automated red-teaming and prompt vulnerability audits
- Sub-200ms token streaming latency targets
Engineering Philosophy
Why Generic "AI Wrappers" Fail in Production.
Anyone can call the OpenAI API with a system prompt. But naive implementations collapse the moment they encounter real-world complexity: token rate limits, unpredictable hallucinations, contextual drift, and catastrophic data leakage.
At PurelyWeb, we treat LLMs as non-deterministic compute kernels that require deterministic scaffolding: strictly typed schemas, vector similarity thresholds, fallback state machines, and real-time observability.