RedCell Build
AI applications built to survive production
We do not just advise on AI risk. We design and build secure AI applications that survive contact with real users, real data, real workflows, and real attackers — because the same team that red teams these systems knows exactly how they fail.
Shipping the demo is not the same thing as safely operating the system. The demo impresses the room. Production inherits the liability.
Why secure-by-design
Retrofitting security into an AI system costs more than building it in
Most AI application problems we are hired to fix were design decisions: retrieval without authorization, tools without scoping, logging added after the incident. Building it right the first time is cheaper, faster to approve, and considerably less exciting for your incident response team.
The prototype trap
Pilots get approved on capability, then inherit production data and production consequences with prototype controls.
The permissions gap
Assistants that read everything the service account can read will eventually say something the user was never cleared to hear.
The observability debt
If the first time you need prompt-level logs is during an incident, you will be reconstructing events from screenshots and memory.
What we build
Eighteen build services, one engineering standard
From focused components to complete platforms — every build ships with the controls, evaluation, and operational tooling production demands.
Secure RAG Application Development
Retrieval systems designed with access control, document classification, and tenancy enforced at the retrieval layer — so the model can only repeat what the user was entitled to read.
Internal Copilots
Employee-facing assistants grounded in your knowledge and constrained by your permissions, built to be useful without becoming an internal data breach with a chat interface.
Secure Agentic Workflow Development
Agents that plan and act within explicit budgets, scopes, and approval gates — autonomy where it earns its keep, human control where it matters.
SOC Automation
AI-assisted triage, enrichment, and case summarization for security operations, designed so analysts stay in command and every automated step is logged and reviewable.
Threat Intelligence Automation
Pipelines that collect, deduplicate, and summarize threat intelligence into products your team will actually read, with provenance preserved.
Compliance Automation
Workflows that map controls, track obligations, and draft assessment responses — cutting the manual grind out of GRC without cutting the rigor.
Audit Evidence Automation
Systems that collect, organize, and version audit evidence continuously, so audit season becomes retrieval instead of archaeology.
Secure Customer-Facing AI Assistants
Production assistants for customers and constituents, engineered for the adversarial reality of public exposure: abuse handling, data boundaries, and graceful failure.
Knowledge Management AI Systems
Search and synthesis over institutional knowledge with classification-aware retrieval and clear source attribution.
AI Assessment Tools
Custom tooling for evaluating AI systems — question banks, scoring harnesses, and reporting pipelines for your risk and audit teams.
Custom AI Platforms
Multi-application AI platforms with shared guardrails, logging, evaluation, and cost controls — build the paved road once, then ship on it.
LLM Evaluation and Observability
Evaluation suites and runtime observability so you can measure quality and safety continuously, not just at launch.
Prompt and System Instruction Engineering
System instructions engineered and versioned like the security-relevant code they are — tested against adversarial inputs, not just happy paths.
Guardrail Design
Layered input, output, and action controls matched to your risk profile — designed with the assumption that any single layer will eventually fail.
Human-in-the-Loop Workflow Design
Approval and review flows placed where human judgment changes outcomes, designed so reviewers stay engaged instead of rubber-stamping.
Model and Vendor Selection
Structured selection across capability, security posture, data handling, and cost — with an exit plan documented before the contract is signed.
Secure AI SDLC Integration
Threat modeling, evaluation gates, and abuse testing integrated into your existing development lifecycle so AI features ship through the same discipline as everything else.
Prototype to Production Hardening
Takes the prototype that impressed leadership and rebuilds what must be rebuilt: identity, logging, retrieval controls, evaluation, and operational readiness.
Engineering discipline
Engineered in from the first commit
These are not add-ons quoted separately. They are the default properties of anything we ship.
Access control and tenant isolation
Authorization enforced at retrieval and tool boundaries, with tenancy separation designed in — not filtered in the prompt.
Data classification
Classification decisions made before ingestion, so sensitive data never depends on the model's discretion.
Logging and monitoring
Prompts, retrievals, tool calls, and decisions logged with retention and privacy constraints — enough to reconstruct any incident.
Prompt security
System instructions treated as a security boundary: versioned, tested, and defended in depth.
Retrieval controls
What gets indexed, who can retrieve it, and what reaches the context window — governed explicitly.
Evaluation and abuse testing
Quality and safety evals run continuously, with adversarial test suites drawn from our red team practice.
Cost controls
Budgets, quotas, and alerts designed in — because unbounded inference is a denial-of-wallet condition waiting to be found.
Privacy by design
Data minimization, retention limits, and regional constraints handled in architecture, not in the privacy policy alone.
Secure deployment
Secrets handling, network boundaries, and least-privilege service identities from the first environment onward.
Fallback and failure design
Defined behavior when the model is wrong, unavailable, or under attack — degradation you chose, not discovered.
Incident response readiness
Runbooks, kill switches, and escalation paths delivered with the system, not promised after go-live.
How we work
Design partnership, not dependency
We build with your engineers, not around them. Systems arrive with runbooks, evaluation suites, logging, and documentation your team can own — and where it strengthens the result, our own AI red team validates the build before launch. Few vendors volunteer to attack their own work; we consider it quality control.
Discuss secure AI development
Bring us a use case, a struggling prototype, or a system that needs hardening before it meets real users. We will tell you what production-ready actually requires.