01
Discover and size
Pick one or two high-value use cases, estimate return on investment, and decide whether to build, buy, or configure what you already own.
Custom & Private AI
Yes, we build custom AI assistants and apps, securely.
Nexus Security Advisors designs, builds, tests, and supports custom artificial intelligence (AI) chatbots, assistants, and agents for small and mid-sized businesses. Because we are security and governance specialists first, your AI is built to protect proprietary information, personal data, and health data from day one, and a named person on your team stays in control.
Why Build Your Own
When your own AI makes sense, and when it does not.
Benefits of your own AI
- Proprietary knowledge stays yours: pricing, designs, playbooks, and client files are not sent to a public chatbot.
- Personal and health data protection: personally identifiable information (PII) and protected health information (PHI) stay in systems you control, on services covered by a business associate agreement (BAA) where required (Microsoft, Amazon Web Services, Google Cloud).
- Answers from your own documents, with citations, so staff can check where an answer came from.
- Permission-aware: employees only get answers from documents they are already allowed to see.
- Fits your workflow: connect to your customer relationship management (CRM), ticketing, document, or scheduling systems.
- Predictable control: you choose the model, the version, where data lives, and how long logs are kept.
When buying is smarter
- Your needs are general (writing, summarizing, brainstorming) and involve no sensitive data.
- Fewer users mean per-user licenses cost less than building and running your own.
- You have no one to own the system after launch.
- The tools you already pay for (for example Microsoft 365 or Google Workspace) can be configured to meet the need.
We will tell you plainly which path fits. Use the build vs. buy calculator below for a first look.
Deployment Options
Three ways to deploy AI: vendor service, private cloud, or on-premises.
| Factor | Ready-made service | Private cloud AI | On-premises AI |
|---|---|---|---|
| Where your data goes | Vendor's systems, under contract terms | Your own cloud account and region | Your own servers |
| Best for | General productivity | Most custom business assistants, including regulated data | Highly sensitive or offline environments |
| Examples | ChatGPT, Claude, Microsoft 365 Copilot | Microsoft Foundry, Amazon Bedrock, Google Cloud AI | Ollama, vLLM, Hugging Face open models |
| Upfront effort | Low | Medium | High (hardware and setup) |
| Ongoing cost driver | Per-user licenses | Usage and hosting | Hardware, power, and staff time |
How It Works
Inside a secure custom AI assistant.
Most business assistants use retrieval-augmented generation (RAG): the system finds the right passages in your approved documents and gives them to the model, so answers come from your information instead of the model's memory.
How We Build It
Our six-step build process.
02
Classify the data
Identify proprietary, personal, health, or controlled information, set rules for what the AI may see, and confirm contract and regulatory requirements.
03
Design the architecture
Choose the platform (private cloud or on-premises), model, identity and access controls, logging, and guardrails.
04
Build and integrate
Build the assistant, connect your documents and business systems, and create a simple interface your team will actually use.
05
Test and red-team
Measure accuracy, consistency, and hallucinations against a test set built from your real questions, then attack it the way an adversary would.
06
Launch, monitor, improve
Train users, hand over ownership, monitor quality and cost, and re-test whenever the model, data, or vendor changes.
Testing & Validity
How we test AI for accuracy, consistency, and made-up answers.
AI models can “hallucinate,” producing confident answers that are wrong or invented. You cannot eliminate this completely, but you can measure it, reduce it, and catch it before it reaches a customer.
What we test
- Accuracy: a “golden” set of real questions with approved answers, scored before launch and after every change.
- Groundedness: every answer must be supported by the cited source; unsupported claims are flagged.
- Consistency: the same question asked many ways and many times should get the same substance.
- Honest refusals: when the answer is not in your documents, the assistant says so instead of guessing.
- Safety and security: prompt injection, data leakage, and attempts to see documents a user should not.
- Regression: re-run the full test set whenever the vendor updates the model.
How we reduce made-up answers
- Ground answers in your documents with citations (retrieval).
- Instruct the model to answer only from provided sources.
- Lower randomness settings for factual tasks.
- Add automated groundedness checks before an answer is shown.
- Keep a human reviewer on high-impact outputs.
- Track a hallucination rate on a monthly quality report.
Methods and free tools we draw on: NIST AI 600-1 Generative AI Profile, OWASP AI Testing Guide, Ragas, promptfoo, garak, and the OWASP GenAI Red Teaming Guide.
Build vs. Buy Calculator
Should you build your own AI or buy a ready-made one?
Compare the total cost of per-user AI licenses with building and running your own assistant over several years. Cost is only one factor: data sensitivity, control, and fit often decide the question.
Total cost to buy–
Total cost to build–
Per user per month (buy)–
Per user per month (build)–
Difference over the period–
Users needed for build to break even–
Illustrative estimate only. Default values are placeholders, not quotes; real costs depend on scope, data, integrations, and the platform chosen. Compare current vendor pricing: ChatGPT, Claude, Google Workspace with Gemini, Microsoft 365 Copilot.