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Tailor Made Intelligence

Custom LLM integration, infrastructure advisory, and domain-specific AI engineering.

Bring modern language models into your existing hardware and workflows without exposing sensitive business data to external APIs. From hardware selection and local inference engine setup to custom RAG pipelines and API development.

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Independent Consulting

Full-Spectrum LLM Integration

Every organization has distinct data privacy constraints, hardware budgets, and workflow requirements. Tailor Made Intelligence delivers pragmatic engineering and advisory to get models running reliably inside your stack.

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What I Deliver

  • check_circle Architecture design for hosted vs. fully on-premises LLM deployments
  • check_circle Hardware sizing, GPU selection, and local inference server configuration
  • check_circle Custom Retrieval-Augmented Generation (RAG) and domain database indexing
  • check_circle Middleware development to connect open-weights models to legacy software
  • check_circle Security audits, data boundary enforcement, and privacy compliance reviews

Core Capabilities

Targeted technical services for deploying, evaluating, and customizing language models.

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On-Premises Deployment

Setup open-weights models using vLLM, Ollama, or llama.cpp directly on your servers or private cloud instances.

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Custom RAG Architecture

Build robust retrieval pipelines over your internal documents, code repositories, or relational databases with pgvector.

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Hardware Sizing & Optimization

Evaluate GPU requirements, memory bandwidth needs, and model quantization levels to optimize cost per token.

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Privacy & Data Governance

Ensure sensitive queries, customer data, and internal knowledge bases never leave your controlled network boundary.

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API & System Integration

Develop custom Python, REST, or gRPC services to wrap model inference into clean endpoints for existing applications.

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Model Selection & Benchmarking

Test open-source models on your domain-specific tasks to measure accuracy, speed, and resource usage before committing.

Engagement Process

A structured path from initial technical assessment to production integration.

1

Assessment

We review your existing infrastructure, data flow, privacy boundaries, and target use cases.

2

Architecture

I design the model deployment, hardware specs, database extensions, and integration pipeline.

3

Implementation

Deploying inference runtimes, building RAG components, writing connector APIs, and tuning performance.

4

Handoff

Thorough documentation, ops playbooks, code walkthroughs, and optional ongoing maintenance support.

Tech Stack & Tools

Implementations focus on stable, production-tested open-source components. Depending on requirements, setups can run fully air-gapped on hardware or utilize private cloud instances.

By leveraging open-weights models and established vector storage frameworks, your organization avoids long-term vendor lock-in and retains complete ownership over infrastructure and data.

Python llama.cpp vLLM PostgreSQL + pgvector Ollama Kubernetes FastAPI 
$ tailor-made-ai status

Inference Engine: vLLM (Local GPU)
Vector Store    : Postgres + pgvector
Network Access  : Private / Air-gapped
Data Egress     : 0 bytes external

Discuss your LLM integration

Whether you need a full technical roadmap, a custom proof-of-concept, or hands-on assistance setting up local inference servers, get in touch to start the conversation.

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