Securing AI workflows and language model deployments against prompt injections, data poisoning, and model manipulation.
Talk to security team
85%
Long-term Partnerships
75%
Mid-to-Senior Engineers
5+
Avg. Years of Engineer Experience
98%
Would Recommend Us
You're probably here because one of these is true.
What we build into your AI stack
Most providers use AI to power security tools. We secure the AI itself, treating prompt injection as the core discipline, not an afterthought, with every prompt boundary and safeguard designed around your actual deployment.
Real attack techniques tested against your live deployment.
Prompt injection testingJailbreak simulationAttack surface mappingOWASP LLM Top 10
We test your live LLM application against real prompt injection and jailbreak techniques, mapping exactly where an attacker could override instructions or extract data. This kind of testing finds the gaps before an attacker does.
Layered defenses that separate instructions from input.
Input validationOutput filteringInstruction isolationLeast-privilege tools
Prompt injection sits at the biggest blind spot in most LLM deployments today. We architect layered defenses that separate trusted instructions from untrusted input, so a crafted prompt cannot hijack your model's behavior between every request and the model.
Sources verified before they ever reach retrieval.
Source verificationIngestion filteringEmbedding anomaly detectionNamespace isolation
We secure the retrieval layer itself, verifying sources before ingestion and screening for poisoned documents before they influence generation. These controls stop knowledge base poisoning at the point of entry, not after a bad answer ships.
our cyber security practice, where AI and LLM security is one piece of the stack.
Why teams pick us for this.
Most providers use AI to power detection tools. We treat securing the model as a distinct discipline, testing for injection and manipulation directly.
We do not ask you to switch models or providers. Defenses get layered on top of whatever LLM, vector database, or orchestration framework you already run.
You work with the engineers architecting your AI security defenses, not a junior learning this discipline on your deployment.
From a single chatbot to a fleet of agentic workflows, the controls scale with how many models and pipelines you run, not the other way around.
Senior and hands-on from the first call. Our approach puts you with the engineers testing and hardening your deployment, not an account manager.
Start a security audit{ 01 }· Week 1
We trace how your LLM application handles input, retrieval, and output, then mark where prompt injection or manipulation could realistically succeed.
{ 02 }· Week 2 to 3
We run a security audit against your live deployment, then design the guardrails, input validation, and RAG security controls the results call for.
{ 03 }· Build
We implement the guardrails in tested increments, wiring input validation, output filtering, and retrieval verification into your model pipeline.
{ 04 }· Launch
We cut the guardrails over feature by feature, brief your team on the new controls, and stay on to adjust defenses as new attack patterns emerge.
Questions, answered.
A prompt injection attack happens when crafted input tricks a language model into ignoring its original instructions and following attacker-controlled commands instead. Defense catches this by separating trusted instructions from untrusted input before either reaches the model.
This kind of audit tests your live deployment against real attack patterns, including direct prompt injection, indirect injection through retrieved content, and attempts to extract system prompts or sensitive data, mapping exactly where those attempts succeed.
Data poisoning happens when an attacker gets malicious content into the documents your RAG system retrieves, so the model generates answers based on tampered information. Verifying sources before ingestion stops poisoned content from entering the knowledge base.
Modern input and output guardrails run in milliseconds alongside the model call, not as a separate blocking step. The latency cost is small compared to the cost of an undetected prompt injection or data leak reaching a user.
You do not need to switch models or providers. We architect these security controls around whatever LLM, vector database, or orchestration framework you already use, whether that is OpenAI, Anthropic, an open-source model, or a mix.
Tell us how your model is deployed today and what data it can access. A senior engineer replies within one working day, no sales script.
Tell us your deployment
Your model, provider, and what data it can access. NDA available.
A senior engineer replies
A senior engineer responds within one working day, not a support queue.
Get a clear approach
An attack-surface map, guardrail design, and rollout timeline, upfront.
Tell us how your model is deployed today and what data it can access. A senior engineer replies within one working day, no sales script.
