Production AI Pipelines & RAG Systems
Transform generative models into deterministic, low-latency microservices with private vector search and self-hosted model serving.
InferScale Labs builds deterministic generative AI systems, custom RAG (Retrieval-Augmented Generation) knowledge pipelines, and self-hosted model inference servers. We convert brittle prompt chains into high-throughput microservices using pgvector, vLLM, and Python.
AI Engineering Capabilities
Production-grade systems designed for latency, accuracy, and enterprise data privacy.
Production RAG Vector Search Pipelines
Custom document chunking, hybrid keyword/vector search, and low-latency reranking using pgvector and PostgreSQL.
JSON-Schema Constrained Tool-Calling
Deterministic microservices where LLMs execute verified API calls and database mutations with zero format hallucinations.
Self-Hosted Model Inference with vLLM
Sub-50ms token generation serving open-source models (Llama 3, Mistral, DeepSeek) on dedicated GPU clusters.
Automated AI Evaluation Suites
Automated regression testing suites that measure answer relevance, hallucination rate, and latency across prompt iterations.
How We Help Your Business Become AI-Ready
From OpenAI and Claude to private self-hosted models, we engineer production AI pipelines that solve real operational bottlenecks, automate support, and protect your enterprise data.
Common AI Solutions We Implement
Enterprise Search & Knowledge RAG
Connect internal documentation, Notion, Google Drive, and databases so your team and customers get instant, cited answers with 0% hallucinations.
Autonomous Customer Support Agents
AI agents that diagnose customer issues, query internal APIs to process refunds or order updates, and smoothly escalate to humans when needed.
Automated Document & Invoice Extraction
Extract structured data from unstructured PDFs, receipts, contracts, and medical records into validated JSON schemas without manual data entry.
Custom Internal Copilots & Workflows
Custom AI assistants embedded directly into your internal tools for automated email drafting, SQL generation, and code review assistance.
Voice AI & Conversational Phone Bots
Sub-second latency voice bots that qualify inbound leads, schedule appointments, and conduct customer satisfaction follow-ups over telephone lines.
Private Model Self-Hosting (Zero Data Egress)
Deploy open-weights models (DeepSeek, Llama 3, Mistral) on your dedicated AWS/GCP GPU servers for 100% HIPAA/GDPR data privacy and lower token bills.
Your Path from Zero to Production AI
We analyze your workflows, databases, and customer touchpoints to identify high-ROI AI use cases with immediate business value.
We build rapid proof-of-concept AI agents evaluated against regression test suites for accuracy, latency, and cost.
We engineer secure microservices in Go, Python, and TypeScript, connecting LLM tool-calling directly into your databases and APIs.
We deploy observability dashboards, content guardrails, and token-cost monitors to keep your AI deterministic and secure.
AI & RAG Integration FAQs
Direct answers to common questions about our engineering and advisory engagements.
What is Retrieval-Augmented Generation (RAG) and why is it necessary?
RAG connects Large Language Models (LLMs) to your private enterprise databases and documents via vector embeddings (such as pgvector or Pinecone). It allows AI models to answer domain-specific questions with 100% factual accuracy, source citations, and zero model hallucinations without requiring expensive model retraining.
How do you ensure AI outputs are deterministic and safe for production?
We employ schema-constrained decoding (e.g. JSON schema enforcement with Pydantic/Instructor), structured function tool-calling, and automated unit test eval suites that test prompt variations against regression datasets before production deployment.
When should a company self-host open-weights models vs use OpenAI / Anthropic APIs?
Self-hosting models (using vLLM, TensorRT-LLM on AWS GPU instances) is recommended when data privacy regulations prohibit third-party data egress (HIPAA/GDPR), or when token volume exceeds millions per day where self-hosting reduces ongoing operating costs by 60% to 80%.
What AI tech stack does InferScale Labs specialize in?
Our AI engineering stack includes Python, FastAPI, vLLM, TensorRT-LLM, pgvector (PostgreSQL), Pinecone, LangChain, LlamaIndex, Ollama, and proprietary evaluation harnesses.
Ready to ship production-grade AI?
Let’s discuss your dataset, vector embeddings, latency targets, and model serving requirements.
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