Custom AI you can actually trust — scoped, built, and handed over by the same team that pentests production systems.
// Scope this projectFour focused offerings under this practice. Scope as small as one delivery, as broad as end-to-end ownership.
Multi-step agents that act across your systems — tool-equipped, bounded by explicit authority scopes, and fully observable. Automation that leaves an audit trail, not just a demo.
// scope an agentCustomer-facing and internal conversational AI, grounded in your data and routed to your tools. Hardened against prompt injection and shipped to production, not stopped at a PoC.
// scope a chatbotBespoke models when off-the-shelf LLMs don't fit — fraud detection, anomaly scoring, domain classifiers. Training pipelines, honest eval harnesses, and reproducible performance reporting.
// scope an ML projectThe ops layer under any AI system you deploy: eval pipelines, observability, guardrails, and red-team surfaces. If you're running AI in production, this is what you don't want to build yourself.
// let's buildMid-market companies are adopting AI faster than they can staff for it: a customer-support agent, an internal RAG copilot, a workflow bot that touches three systems. Most of it is built by teams that treat security as a sprint-31 ticket — or by a contractor who delivered a demo and moved on. The thing ends up wired into your Okta, your CRM, and your Slack with zero threat modeling.
// delivery lifecycle
// same team at every phase