Skip to content
Inspire AI Lab

AI Engineering & Consulting · New Jersey

The hard part isn't the demo. It's production.

Firms run AI on their own infrastructure for two reasons: to keep data private, and to control cost at scale. We help with both — from assessment through ongoing operations.

Book a 30-min consultationSee what we do

Why private AI

Two reasons to bring AI in-house. We handle both.

Most firms self-host for one of these — and end up caring about both. Everything we do ladders back to one of them.

Services

From assessment to operations

From the assessment that produces a real plan, through the engineering that gets your system into production, to the operations that keep it healthy.

  • Private AI Assessment

    Cost

    Size the hardware, model the true cost, and get a concrete plan for running AI in-house.

    What we deliver

    • Use-case and workload analysis
    • Model selection and fit assessment
    • Hardware and VRAM sizing
    • On-prem vs cloud vs API cost modeling
    • Self-host break-even analysis

    Tools & technologies

    Hugging Face · NVIDIA GPUs (A100, H100, L40S) · AWS · GCP · Azure · Open models (Llama, Qwen, Gemma, Mistral, DeepSeek)

    Open Blueprint

  • Private LLM Deployment

    PrivacyCost

    Stand up self-hosted models on your hardware or cloud, configured and verified against your targets.

    What we deliver

    • On-premise and private-cloud deployment
    • Engine selection and configuration
    • Quantization and model setup
    • Performance and accuracy validation
    • OpenAI-compatible endpoint delivery

    Tools & technologies

    vLLM · llama.cpp · TGI · SGLang · Ollama · Docker · GGUF / AWQ / FP8

  • Inference Optimization

    Cost

    Make deployments faster and cheaper through deep performance engineering.

    What we deliver

    • Quantization (weights and KV-cache)
    • Continuous batching and PagedAttention
    • Speculative decoding
    • Latency profiling (TTFT, TPOT, throughput)
    • Cost-per-token reduction

    Tools & technologies

    vLLM · TensorRT-LLM · SGLang · CUDA · FlashAttention · Quantization toolkits

  • RAG & Knowledge Systems

    Privacy

    Retrieval pipelines built over your own documents and data.

    What we deliver

    • RAG pipeline architecture
    • Vector database selection
    • Chunking and embedding strategy
    • Hybrid search (semantic + keyword)
    • Retrieval-latency optimization and ingestion at scale

    Tools & technologies

    pgvector · Qdrant · Weaviate · Milvus · LlamaIndex · LangChain · sentence-transformers

  • Agentic AI Systems

    Privacy

    Multi-agent, tool-using workflows with human oversight built in.

    What we deliver

    • Multi-agent orchestration
    • LangGraph and CrewAI pipeline design
    • MCP server development
    • Agentic latency profiling
    • Human-in-the-loop workflow integration

    Tools & technologies

    LangGraph · CrewAI · Model Context Protocol (MCP) · LangChain · Claude & open models

  • Document Intelligence

    PrivacyCost

    Audit-grade, multilingual document pipelines that run on your own models.

    What we deliver

    • Multi-tier OCR for multilingual and degraded scans
    • Verbatim, zero-hallucination extraction
    • Classification and structured-field extraction
    • Human-in-the-loop review and audit trails
    • High-volume ingestion pipelines

    Tools & technologies

    PaddleOCR · Tesseract · Vision-language models · AWS Textract · Fine-tuned open models

    See the demo

  • Voice & Conversational AI

    Privacy

    Private voice agents and phone-based workflows.

    What we deliver

    • Voice agents and IVR systems
    • Speech-to-text and text-to-speech integration
    • Call intake and routing automation
    • Multilingual voice support
    • Telephony integration

    Tools & technologies

    Twilio · ElevenLabs · Whisper · STT / TTS pipelines · FastAPI

  • AI Application Development

    Cost

    Complete production AI applications, built end to end.

    What we deliver

    • Production services in Python and FastAPI
    • API design and third-party integrations
    • Application interfaces and user workflows
    • Background jobs and data pipelines
    • Deployment and release

    Tools & technologies

    Python · FastAPI · Next.js · PostgreSQL / Neon · Inngest · Docker · Vercel

  • Managed AI Operations

    Cost

    We monitor, maintain, and keep your private AI infrastructure healthy and current.

    What we deliver

    • Live monitoring (latency, throughput, GPU, cost)
    • Alerting and incident response
    • Model and engine updates
    • Capacity and scaling management
    • Ongoing optimization

    Tools & technologies

    Prometheus · Grafana · Monitoring / observability stacks · CI/CD pipelines

Demos

See the work, then talk to us

Two interactive demos show how we think about private AI infrastructure. Use them before you ever fill out a contact form.

Interactive demoblueprint
VRAM · Qwen 32B Q4 · 32K ctx · 25 users≈ 46 GB
~16 GB · weights
~24 GB · KV
~6 GB · runtime

Inspire Blueprint

Pick an open model. See the VRAM math, three hardware tiers, on-prem vs cloud cost, and the self-host break-even — live.

See what running AI in-house would cost
PreviewComing soon
Invoice · scan_03.pdf · de
VendorMüller GmbH0.99
Invoice no.INV-2026-00421.00
Total€ 18,427.500.97
Due date2026-07-210.94

Document Intelligence

Watch a document become a record where every field is proven — multilingual OCR, verbatim extraction, audit trail.

See the preview

See what running AI in-house would actually cost you

Use Blueprint to size a model, the hardware to run it, and the real spend — on your own infrastructure or in the cloud.

Open BlueprintBook a consultation