Loadella Private AI that knows your business.
Put AI to work on your own documents and systems, without sending your data to an outside AI company.
Open AI models sized to what you actually need, tuned to your work, tested against attack, and run on hardware you control, wherever that is: your offices, your field or plant sites, or the data center that already hosts your systems, so your data never crosses the public internet.
Based in Denver–Boulder, Colorado and on-site locally · remote anywhere in the US · confidential by default.
Is private AI right for you?
Not always, and you'll hear that on the first call. The best first project is usually one task where staff read or write a lot of text.
- can't send client, operational, health or financial data to public AI
- must keep data on your own premises, in a facility you choose, or fully offline
- want AI that knows large document collections, with sources cited
- need a model that follows your rules, not a vendor's defaults
- plan to buy AI hardware and want it sized to real workloads
- already run AI and need it tested for leaks and attacks
- use AI lightly and have no data restrictions: a business plan from a major AI provider will cost less
- don't yet have a task in mind: start with the free call, not a build
- need the very top commercial model for every task, regardless of where data goes
Private hosting pays off through control and data rules first; on cost alone it wins mainly at high, steady usage.
Confidential by default
Built for organizations whose projects stay need-to-know, where even the people running the building don't know what's being worked on.
NDA first
Signed before any detailed discussion of your data or projects. Yours or ours.
No names
Clients are never named or described publicly without written permission.
Hardware access isn't data access
Data is encrypted in transit and at rest, so the people who run the servers can't read what's on them.
Next to your systems
Run on your premises or in the same data center as the servers you already run, wherever it is, and data never crosses the public internet.
Offline if needed
Systems can run with no internet connection at all, on your premises or in a dedicated data center cage.
Your data trains nothing else
Never shared with another client or an outside AI company. You set log retention and access.
Ways to work together
Several ways in, starting small. Work under a master service agreement, yours or ours, with each project as its own statement of work. Fixed prices where the scope is fixed; a written quote where it depends on what the assessment finds. Hardware is bought or rented in your name at cost, never marked up.
AI quick-start review
A short written answer before a bigger commitment: a 60-minute working session on how your team works today, then a 3–5 page memo.
- The three tasks most worth handing to AI, and why
- Whether private AI fits, or why public AI is fine for you
- A rough hardware tier and budget range
- A recommended next step, including "not yet"
Fee credited toward an assessment started within 60 days.
AI readiness assessment
Which tasks are worth it, which model, and what hardware: candidate models tested on your own examples, hardware sized from load tests, and a written plan with costs.
- Model comparison report on your tasks
- Hardware or rental recommendation, assumptions written down
- Build plan, budget and success targets
Fee credited toward a build started within 60 days. Hardware sizing on its own: $2,500.
Pilot, then rollout
One task, one team, real users first: for example a knowledge assistant over one document collection. Includes red-team testing before anyone relies on it. Rollout is quoted from the pilot's measured results.
- Working system on your data
- Test set and measured results against the agreed targets
- Full documentation and handover
Nothing goes live until it meets agreed targets for quality, speed and security.
AI support plan
One monthly plan covers the AI layer and a block of hours for new work, changes and reviews: monitoring of the models and the applications built on them, security updates, model upgrades as better open models appear, and re-tests so quality doesn't drift. Hardware, power and facility monitoring stay with your IT team or data center. Support during business hours, with a next-business-day response.
Extra hours are approved in writing before any work starts. Unused hours roll over one month.
AI security test
Red teaming for any AI system, ours or not, including ones built on public AI services: from $7,500 for one chatbot, from $15,000 for systems that search documents or use tools. Findings ranked by severity, a fix for each, one re-test, and a report your compliance team can use as evidence.
Rate schedules are available for master service agreements, for work called off as needed.
- Free call
- →
- Quick-start review (optional)
- →
- Assess
- →
- Build
- →
- Run
- · stop after any step; everything built is yours
What we build
Every project starts from a proven open model and adds only what your task needs, which keeps costs and upkeep down.
Answers from your own files, with sources
Retrieval-augmented generation (RAG) over files, wikis, tickets and databases. New documents are usable as soon as they're indexed, and permission-aware retrieval means each person sees only what they're already allowed to open.
Open models on hardware you control
Compact models up to the largest open ones, served with proven open-source serving software, with a private chat app and an OpenAI-compatible API that tools built for OpenAI can usually use unchanged.
Trained on your proprietary data
Efficient fine-tuning on your own documents and examples for your formats, terminology and tone, or so a smaller, cheaper model can do a larger one's job. Quantization to run faster on less hardware. The tuned model is yours.
You set the model's limits
Off-the-shelf models refuse some legitimate professional work, such as a clinician asking about overdose thresholds or a security team writing attack tests. Others need tighter limits than they ship with, for public-facing or regulated use. Models are adjusted in either direction to your written policy, by targeted retraining, preference tuning and added guardrail models, and every change is verified against your test set and red-teamed before release.
Multi-step work, with a person in the loop
Assistants that carry out multi-step tasks in your systems through secure tool connections, each limited to the access it needs, with a person approving key steps.
Buy what the workload needs
GPU and server recommendations from load tests of your chosen models, with the assumptions written down (model size, context length, users at once, response times) and power, cooling and growth covered. Many teams find a mid-size model with good document search does the job on a fraction of the hardware they expected.
AI security testing
Attacks on AI target the model, the documents it reads and the tools it can use. Tests probe all three the way an attacker would, with automated attack scanners plus hands-on expert testing, organized around the OWASP Top 10 for LLM Applications and MITRE ATLAS, and end with findings ranked by severity, a fix for each, and a re-test.
Data leakage
Private data, other users' files or system prompts leaking through answers, retrieval, vector stores, logs or caches.
Prompt injection and jailbreaks
Rule overrides, including instructions hidden in files, emails or web pages the AI reads.
Agent and tool abuse
Permission scoping and action chains, so agents can't be steered into unauthorized actions.
The rest of the stack
APIs, plugins, insecure output handling, training-data extraction and the model supply chain.
Guardrails and hardening
Input and output filtering, vector store access controls, rate limits and attack monitoring.
Evidence for your compliance program
Findings and system documentation mapped to the NIST AI RMF and ISO/IEC 42001, ready for your compliance team and auditors. Which laws apply to you is a question for your counsel; we work alongside them.
Sizing at a glance
A starting point, not a quote: approximate GPU memory for a few well-known open models, worked out from their published sizes. Real needs depend on document length, users at once and response times, which is what the assessment measures on your workload.
| Model | Parameters | Weights, 16-bit | 8-bit | 4-bit | One way it fits |
|---|---|---|---|---|---|
| Qwen3 8B | 8B | ~16 GB | ~8 GB | ~4.5 GB | one 24 GB GPU |
| gpt-oss-20b | 21B (3.6B active) | — | — | ~12 GB as released | within 16 GB of memory* |
| Qwen3 32B | 33B | ~66 GB | ~33 GB | ~18 GB | one 48 GB GPU at 8-bit |
| Llama 3.3 70B | 70B | ~140 GB | ~70 GB | ~39 GB | one 48 GB GPU at 4-bit; two at 8-bit |
| gpt-oss-120b | 117B (5.1B active) | — | — | ~64 GB as released | one 80 GB GPU* |
| Qwen3 235B | 235B (22B active) | ~470 GB | ~235 GB | ~130 GB | two 80 GB GPUs at 4-bit; four at 8-bit |
Approximate. Weights are estimated with the rule of thumb in Resources below (parameters × bytes per parameter) and exclude conversation memory, which needs headroom on top. “Active” models (mixture-of-experts) need memory for every parameter but run at about the speed of their active size. *As stated by the publisher. Sizes from the model cards: Qwen3 8B, gpt-oss-20b, Qwen3 32B, Llama 3.3 70B, gpt-oss-120b, Qwen3 235B.
As of . Speed and capacity results measured on our own test hardware will be added here.
Resources
General guidance, free to use. Sizing for your workload is what the assessment is for.
As of .
How much GPU memory does a model need?
The model's weights take roughly parameters × bytes per parameter: 2 bytes at 16-bit, 1 at 8-bit, about half a byte at 4-bit. Conversation memory (the KV cache) comes on top and grows with document length and users at once.
Some large models (mixture-of-experts) need memory for all their parameters but run at the speed of a much smaller model, which changes the math.
The frameworks findings are mapped to
- OWASP Top 10 for LLM Applications · the most common AI app risks
- OWASP Top 10 for Agentic Applications · risks when AI takes actions
- MITRE ATLAS · real attack techniques against AI
- NIST AI Risk Management Framework · and its generative AI profile
- ISO/IEC 42001 · AI management systems
Questions to ask any AI vendor +
- Where is our data processed and stored, and who can see it?
- Is any of it used to train models?
- How long are prompts and answers logged, and can we set that?
- Does each user only see documents they're already allowed to open?
- Which model and version is running, and who decides when it changes?
- How was quality measured, and on whose examples?
- Has it been tested for prompt injection and data leakage? Can we see the findings?
- What can its tools or agents do without a person approving?
- What does it cost at twice our expected usage?
- If we leave, what do we take with us?
How private AI compares
You get the control of running AI yourself, with one point of contact and nothing to build or staff.
| Public AI services | Do it yourself | Private AI with Loadella | |
|---|---|---|---|
| Where data goes | The provider's cloud | Your hardware | Your premises or your data center, next to your systems; offline possible |
| Model's limits | Provider's policy | Yours, if you can tune it | Yours, tuned and tested |
| Model changes | When the provider decides | When you decide | When you decide, after re-tests |
| Cost | Per seat or per use | Hardware plus staff time | Hardware at cost plus a flat monthly fee |
| Security testing | Provider's, not your setup | Up to you | Red-teamed before launch, re-tested after |
| Staff needed | None | AI and infrastructure skills | None |
Questions
What does a typical first project cost? +
An assessment and a pilot together usually land between $20,000 and $35,000, plus hardware. Hardware ranges from one workstation-class GPU to a multi-GPU server depending on model size and users; the assessment tells you which before you spend it.
Will you work under our MSA and NDA? +
Yes. Most clients sign an NDA before the first detailed conversation, then a master service agreement with each project as its own statement of work. An AI support plan can sit under the same agreement, or work can be called off at agreed rates. If you don't have one, ours is short.
We already use ChatGPT or Copilot. Why change? +
You may not need to. Private AI makes sense for the data you can't send out, the rules you need the model to follow, and the workloads where flat costs beat per-seat pricing. Many organizations run both. If you keep public AI, the security test still applies to anything you've built on it.
Is our data used to train anything? +
Only your own model, and only if you ask for fine-tuning. Nothing is shared with another client or an outside AI company. You set log retention and who can access logs.
Can you adjust what the model will and won't do? +
Yes, in both directions, to a written policy you approve. A model can be tuned to stop refusing legitimate work in your field, or given stricter limits than it ships with. Each change is tested so it does only what the policy says, and the result is red-teamed before release.
Can we use hardware we already own? +
Often, yes. The assessment load-tests candidate models on it and tells you what it can handle and what, if anything, to add.
What if you're unavailable? +
Systems are built to keep running without anyone touching them, and on-site data center staff handle hardware. Every system comes with documentation, runbooks and credentials that you hold, so your team or another engineer can take over. Larger projects can add vetted specialists under the same NDA.
Do you work on-site? +
Yes, across the Denver–Boulder area as a matter of course. Everywhere else in the US, work is done remotely, with on-site visits arranged under your master service agreement: installs, workshops and kickoffs are typical reasons to visit. Travel terms are set in that agreement.
What about model licenses? +
Open models come with different licenses, some with limits on commercial use or company size. Licenses are checked for your use during the assessment, and only models that fit are recommended.
Start with one task
Book a free 30-minute call about one task you'd like AI to handle, or one system you'd like tested. Happy to sign your NDA first. You'll leave with an honest take on whether private AI fits, even if the answer is no.
Based in Denver–Boulder, CO · on-site locally · remote across the US, on-site by arrangement