Private & DisconnectedLLM Engineering
Enterprise-grade AI that runs entirely on your hardware — no cloud, no per-token bills, no data ever leaving your perimeter. Architected, fine-tuned, quantized, and delivered as a complete product: model, API, and UI.
Zero data egress
Prompts, documents, and outputs never touch a third-party cloud
Flat, predictable cost
Own the hardware; retire the per-token meter
Open-weight freedom
Mistral, Gemma, Llama, Qwen, Phi & more — no vendor lock-in
Right-sized infra
Quantization engineered across the full Q4→Q8→FP16 spectrum for your quality bar
01 — The problem
When the Cloud Is the Wrong Answer
Cloud LLM APIs are remarkable — until they collide with your reality:
Your data can’t leave.
Patient records, legal case files, financial models, defense specifications, unreleased IP. For many organizations, sending this to an external API isn’t a risk calculation — it’s a regulatory or contractual impossibility.
The meter never stops.
Per-token pricing that looked trivial in the pilot becomes a six-figure line item at production scale. Costs grow with your success.
You don’t control the stack.
Model deprecations, rate limits, policy changes, outages, and price revisions all happen on someone else’s schedule.
Connectivity isn’t guaranteed.
Factory floors, ships, field operations, secure facilities — plenty of real work happens where reliable internet doesn’t.
The obvious answer — “just run a model locally” — hides the hard part. Downloading a model is a command. Making it production-grade requires model selection, fine-tuning, quantization strategy, memory engineering, serving architecture, API design, application integration, and hardware that matches the workload. Get any layer wrong and you end up with a system that's too slow, too expensive, or too dumb.
Running a local model is easy. Running the right model, at the right quantization, on the right hardware, wired into your actual applications — that's engineering.
02 — The solution
The Complete Private LLM Stack — Delivered as One Solution
We don't hand you a model and a manual. We deliver a working AI product on your infrastructure, engineered layer by layer:
Application & UI Layer
Custom chat interfaces, document workbenches, and dashboards your teams actually use
API Layer
OpenAI-compatible REST APIs, so your existing apps and tools connect without rewrites
Intelligence Layer
Fine-tuned open-weight models (Mistral, Gemma, Llama, Qwen, Phi) + RAG over your documents
Serving Layer
Ollama, LM Studio, vLLM, or llama.cpp — chosen for your concurrency, latency, and ops profile
Optimization Layer
Quantization (GGUF Q4/Q5/Q8), KV-cache tuning, context management, GPU offload strategy
Hardware Layer
From NVIDIA DGX Spark desktops to RTX PRO 6000 workstations to multi-GPU servers
One constraint
Maximum output quality on minimum infrastructure.
Every layer is engineered against one constraint: maximum output quality on minimum infrastructure. That trade-off is our specialty.
03 — Capabilities
What We Build
Model Strategy & Architecture
The right model beats the biggest model.
We benchmark candidate open-weight models — Mistral, Gemma, Llama, Qwen, Phi, GPT-OSS and beyond — against your tasks, not leaderboards. A well-chosen 8B model with RAG often outperforms a struggling 70B on domain work, at a tenth of the hardware cost. You get a documented model selection with measured accuracy, latency, and memory numbers.
Business-Specific Fine-Tuning
Your terminology, your formats, your judgment — baked into the weights.
LoRA and QLoRA fine-tuning on your data (contracts, tickets, reports, transcripts), executed entirely inside your environment so training data never leaves. Instruction tuning for your workflows, output-format enforcement, and evaluation suites that prove the tuned model beats the base model on your metrics before it ships.
Quantization & Memory Engineering
The art of fitting more intelligence into less silicon.
Quantization decisions make or break local deployments — and there is no universal right answer, only a spectrum. From aggressive Q4-class quantization (smallest footprint, highest artifact risk) through the Q5/Q6 midrange to Q8 and full FP16 precision, every step trades memory and speed against fidelity. We run your actual workload across that spectrum and measure where quality genuinely degrades for your tasks, not in theory. Some workloads sail at Q4; others demand Q8; most land somewhere in between — and the difference can mean one GPU instead of two, or a desktop instead of a server. We also engineer KV-cache quantization, context-window sizing, layer offloading, and batch strategy — squeezing every gigabyte of VRAM.
End-to-End Product Engineering (APIs + UI)
A model without an application is a science project.
OpenAI-compatible API endpoints so anything built for GPT works against your private model unchanged. Custom UIs — secure chat interfaces, document Q&A workbenches, review dashboards — plus integration into your existing systems (ERP, CRM, ticketing, SharePoint, databases) with role-based access control and full audit logging.
Multimodal & Analysis Pipelines
Beyond chat: your files, footage, and feedback — processed on-prem.
CUDA-accelerated pipelines for video processing and frame/scene extraction, image extraction and OCR, document parsing, and speech-to-text. Downstream analysis engines: sentiment analysis on reviews and support tickets, entity extraction, classification, and summarization — all running on the same private hardware, no external API calls.
Hardware Architecture & Blocker Resolution
We unstick the deployments others abandoned.
Hardware selection and build-out — NVIDIA DGX Spark, RTX PRO 6000 workstations, multi-GPU servers, or optimization of hardware you already own. And when deployments stall — CUDA/driver conflicts, OOM crashes, thermal throttling, models that load but crawl, quantization artifacts corrupting outputs — we diagnose and resolve. Many clients come to us mid-failure; rescue engagements are a specialty.
04 — Topologies
One Model or Many? Shared or Isolated? We Architect for Your Reality.
There's no universal topology. The right answer depends on workloads, teams, compliance boundaries, and budget. Common patterns we deploy:
Single Model, Single Instance
One capable general model serving the whole organization via one endpoint. Simplest ops, lowest cost.
Fits: small teams, unified use case, first deployment.
Multi-Model, Single Machine
one machine · memory budget
Several specialized models (a coder, a summarizer, an extraction model) co-resident on one high-memory machine — e.g., a 128GB DGX Spark — hot-swapped or served in parallel with engineered memory budgets per model.
Fits: diverse workloads, constrained hardware budget.
Isolated Instances per Department / Client
Separate model instances with hard resource and data isolation — finance’s model never sees legal’s documents. Essential for internal compliance walls or agencies serving multiple end-clients.
Fits: multi-tenant needs, strict data segregation.
Clustered / High-Concurrency Serving
vLLM-class serving with continuous batching across multiple GPUs for hundreds of concurrent users with latency SLAs.
Fits: org-wide rollouts, customer-facing products.
05 — Hardware
Right-Sized Hardware, From Desk to Data Center
We spec hardware to the workload — never the other way around. Representative tiers:
Desk AI
NVIDIA DGX Spark (GB10)
Memory
Sweet spot
Models up to ~120–200B (quantized); prototyping to small-team production
Investment
~$4–5K
Workstation
RTX PRO 6000 Blackwell workstation
Memory
Sweet spot
Fast 30–70B serving, fine-tuning, multimodal pipelines
Investment
~$10–15K
Server
Multi-GPU rack (2–4× pro GPUs)
Memory
Sweet spot
High concurrency, multiple isolated models, latency SLAs
Investment
~$30K+
Existing fleet
Your current GPUs/workstations
Memory
Sweet spot
Quantization + offload engineering to unlock hardware you already own
Investment
$0 hardware
Often the cheapest upgrade isn't hardware at all — it's a smarter quantization and serving strategy on the machines you already have. We check that first.
06 — The trade-off
The Quantization Spectrum — Where Engineering Meets Economics
Every local LLM deployment lives on a three-way trade-off: output quality ↔ memory footprint ↔ speed. Quantization is the lever that moves all three — and it's a spectrum, not a setting.
At one end, full-precision and Q8-class formats preserve near-complete fidelity but demand maximum memory. At the other, aggressive Q4-class quantization fits the same model in roughly half the space — but on demanding tasks, compression artifacts can appear: degraded reasoning chains, format drift, subtle factual slips. Between them lies a ladder of intermediate levels, each with its own quality/footprint profile. There is no universally correct point on this spectrum. A code-generation workload, a legal summarizer, and a customer-sentiment classifier will each tolerate compression differently — sometimes on the very same model.
The professional question isn't “which quantization is best?” — it's “where on the spectrum does your workload stop noticing the difference?” That's an empirical question, and we answer it empirically: we run your representative tasks across the full quantization ladder, score outputs against your quality bar, and select the lowest-footprint level that passes. The result is a documented, defensible configuration — not a guess and not a default.
Quality ↔ memory ↔ speed
e.g. classification workload — tolerates heavier compression
e.g. complex reasoning workload — demands higher precision
Your workload sets this point — we find it by benchmark, not by default.
Illustrative outcome
~⅓
memory footprint cut
1 GPU
single RTX PRO 6000 instead of a dual-GPU server
5-figure
hardware savings, zero measured quality loss
A different workload on the same model might have demanded Q8 — that's precisely why we benchmark instead of defaulting.
07 — Economics & pipelines
Own the Hardware. Retire the Meter.
Cumulative cost · 24 months
Typical break-even — after which the meter stays off
+ your data never left the building
Illustrative shape only — the crossover point depends on your usage volume, model size, and hardware tier. We model it against your real numbers in Phase 01.
CUDA-accelerated processing
extraction · OCR · transcription
08 — Rescue
Already Tried and Hit a Wall? We Unstick Deployments.
Many clients come to us mid-failure. Typical culprits: wrong quantization for the task, VRAM overflow into system RAM, missing CUDA optimization, misconfigured context windows, or a model mismatched to the job. We diagnose, fix, and document — often on your existing hardware.
$ ollama run mistral-large
CUDA error: out of memory
tried to allocate 18.2 GiB
process killed (OOM)
▍
- Quantization mismatched to task
- VRAM overflow into system RAM
- Missing CUDA optimization
- Context window misconfigured
Often on the client's existing hardware.
09 — Framework
From Feasibility to Production in 5 Phases
Feasibility & Workload Analysis
Use cases, data sensitivity, quality bar, concurrency, and budget. Honest answer up front: is private LLM the right architecture for you? (Sometimes it isn’t — we’ll say so.)
You get: feasibility report + recommended model/hardware shortlist.
Model Selection & Benchmarking
Candidate models benchmarked on your tasks across quantization levels. Accuracy, latency, and memory measured, not assumed.
You get: benchmark matrix + selected model & quantization config.
Fine-Tuning & Pipeline Build
LoRA/QLoRA tuning on your data, RAG construction over your documents, multimodal pipelines (video/image/sentiment) as required.
You get: a tuned model that beats baseline on your eval suite.
Integration & Product Delivery
API layer, UI, application integrations, access control, audit logging, and deployment on production hardware with monitoring.
You get: a live system your team uses daily.
Optimization, Handoff & Support
Memory and throughput tuning under real load, admin training, runbooks, and optional managed support / model-refresh cycles as better open models release.
You get: an internally owned system with a clear upgrade path.
10 — Use cases
Where Private LLMs Earn Their Keep
Healthcare & Legal
Patient-record and case-file analysis where data provably never leaves premises; on-prem document Q&A with citation.
Financial Services
Research summarization, contract intelligence, and sentiment analysis on proprietary data behind the firewall.
Manufacturing & Field Operations
Offline-capable assistants and video/image inspection pipelines on the factory floor — no connectivity required.
Government & Defense
Air-gapped deployments in classified or restricted environments with full audit trails.
Agencies & ISVs
Per-client isolated model instances — sell AI capability without routing client data through third-party APIs.
11 — Questions
Frequently Asked Questions
For focused business tasks — document analysis, drafting, extraction, support, code assistance — modern open-weight models, properly selected and fine-tuned on your domain, routinely deliver production-grade results. The 2024 quality gap has narrowed dramatically. We prove fitness with benchmarks on your tasks before you commit to hardware.
12 — Get started
Your Data. Your Hardware. Your AI.
Find out — concretely — what a private LLM deployment would look like for your organization: recommended model, quantization strategy, hardware tier, and budget range. A no-cost feasibility assessment with a senior engineer, not a sales call.
Get Your Private LLM Feasibility Assessment
Tell us your primary driver — data privacy, cloud API costs, offline needs, or rescuing a stalled deployment — and a senior engineer will map the options.