AI systems, tested like an attacker would test them.
Three disciplines in priority order, and four ways to start.
Build AI that answers from your data
Most AI projects fail at the input, not at the model. Documents are messy, intake is inconsistent and nobody can see why an answer was given. These systems are built to fix that first.
- TYPICAL STACK
- Docker, FastAPI, Qdrant, Ollama, Open WebUI, NGINX, Python, OpenAI structured output
- Private RAG assistants over internal documentation, with sources shown for every answer
- A knowledge preparation pipeline: PDF and DOCX cleaned, split by topic, tagged
- AI triage that classifies requests into strict JSON, with a confidence threshold and a human review path
- Answer evaluation, so quality is measured rather than assumed
Find the holes before users do
An AI system with access to documents and tools has a new attack surface. This service tests it deliberately and reports what broke.
- WHAT YOU GET
- A report with findings ranked by severity, reproduction steps, recommended fixes, and a retest after fixes. Methodology is aligned with the OWASP Top 10 for LLM Applications.
- WEB SECURITY OVERVIEW
- Read-only checks of HTTPS and TLS, security headers, cookies, CORS, login forms, HTTP methods and more, as shown in CipherBeam.
- RULES
- Written authorization, systems you own, agreed scope. No production data without agreement.
- Direct and indirect prompt injection (including through uploaded documents)
- Leakage of system prompts, documents and personal data
- Retrieval problems: exposure of documents a user should not see
- Tool and permission abuse by agents
- Unsafe handling of model output
- Logging, rate limits and abuse handling
Connect it to the way work actually happens
Automation is how the AI and security layers reach real processes. It is kept simple, visible and maintainable.
- n8n workflows and integrations between the tools a team already uses
- Intake forms, routing, follow-up and notifications
- Dashboards and status views
- Monitoring and ongoing improvement
FOUR WAYS TO START
- 01 · audit
AI and process audit
A fixed, one-off look at the process and the data. Output: bottlenecks, priorities and a concrete plan.
- 02 · mvp
Build (MVP)
From problem to a first working system, tested and put into use.
- 03 · security
Security assessment
Adversarial testing of an AI system or a website, with a report and a retest.
- 04 · support
Ongoing support
Monitoring, small fixes, improvements and periodic retesting.