Under the Hood

The Technology Stack

Every system we ship is built on a carefully selected, production-proven set of tools — spanning language AI, computer vision, data engineering, and secure deployment. Here is exactly what we use and why.

Technology Stack

Private AI. Your Infrastructure. Your Data.

A quick reference of every tool we use across language AI, vision, data, and deployment.

Private SLMs

Llama 3.3Mistral 7BPhi-3DeepSeekGemma 3

LLM Serving

vLLMOllamaTGILangChainLlamaIndex

Vision

YOLOv10YOLOv11Detectron2SAM 2RT-DETRViT

Data & Pipelines

dbtAirflowPineconepgvectorWeaviate

Deployment

On-premisePrivate cloudDockerK8sAWS / GCP / Azure

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Your data never leaves your infrastructure

We default to open-source SLMs deployed on your own servers — not shared cloud APIs. Every tool in this stack can run entirely within your environment, on your hardware, under your control.

Data Engineering

Data Infrastructure Built to Train

Most AI projects fail at the data layer. We build the ingestion, labeling, and versioning infrastructure your models actually need — before a single training run starts.

Annotation Tools

Label StudioCVATRoboflowSuperAnnotate

Pipeline & ETL

Apache AirflowdbtPrefectCustom ETL

Storage & Versioning

DVCMinIOS3Delta Lake

Training Infra

PyTorchHuggingFaceCUDAA100 / H100

Synthetic Data

Stable DiffusionGANsBlenderProcUnity Perception

Deployment & Security

Shipped to Production. Secured by Design.

We don't hand off a trained model and leave. Every system is deployed, monitored, and documented — with a clear handover and an optional retainer for ongoing support.

🏢

On-Premise Deployment

Full system delivery to your own servers or data centre. No external API calls, no cloud dependency. You run the model on your hardware, in your network.

☁

Private Cloud

Dedicated instances on AWS, GCP, or Azure — isolated to your account. We architect the network boundary, IAM, and VPC before any model touches production.

🐳

Containerised & Reproducible

Every system ships as Docker containers with compose or Helm charts. Reproducible builds, versioned artifacts, clean rollback — no snowflake deployments.

📊

Monitoring & Observability

Prometheus, Grafana, and custom dashboards for model latency, throughput, and accuracy drift. You see exactly how the system performs in production.

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Data Privacy by Design

We default to private SLMs on your infrastructure. Where cloud LLMs fit your risk profile, we architect the data boundary, access controls, and DPA first.

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Retrain & Refresh Pipelines

Production models degrade. We build automated retraining pipelines triggered by accuracy drift, new data, or a scheduled cadence — keeping your model current.

Why Private AI

Why We Default to Private Over Public AI

Your data never leaves your environment

We default to SLMs deployed on your own servers or a dedicated private instance. No training data, business logic, or customer information reaches a shared cloud API.

No vendor lock-in

Open-source models (Llama, Mistral, Phi-3) and open tooling (LangChain, pgvector, Airflow) mean you own the full stack. Swap providers, retrain, or migrate — without renegotiating a contract.

Compliance-ready architecture

On-premise or private-cloud deployment supports GDPR, SOC 2, HIPAA, and sector-specific data residency requirements. We document the data flow before the first line of code.

Custom fine-tuning on your data

Generic foundation models are accurate for generic tasks. Your domain — your terminology, your edge cases, your quality bar — requires fine-tuning on your data. We build and run that pipeline.

Ready to build?

The right stack for your use case.

We scope the technology to fit your requirement — not the other way around. Start with an AI Audit or go straight to build.

shipai@spinacle.net — audit buildai@spinacle.net — build

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