AI-native software for teams ready to operationalize their best work
We build secure agentic systems that organize information, model business processes, and help people execute inside the tools and environments they already use.
Agentic workflows inside customer environments
- Information architecture before automation
- Security, governance, and observability by design
From scattered work to executable workflows
- Agents
- Tool-using
- Data
- Permissioned
- Actions
- Auditable
- Execution loop
- Human governed
- IngestMapped
- Docs, tickets, APIs, events
- ReasonGuarded
- Policy, context, constraints
- ActReviewed
- Tools, drafts, reports, alerts
Development is becoming the design of intelligent operating systems.
The old request was "build an app." The new request is usually more interesting: organize the information, model the process, connect the tools, and give AI agents enough context to help the organization execute.
That is where we are strongest. We build real AI systems that survive messy data, permissions, security requirements, customer environments, and human judgment.
What this usually unlocks
- Start with the business process and information model, not a chatbot prompt.
- Design for agent autonomy only where the organization can safely delegate.
- Keep humans in the loop for judgment, risk acceptance, approvals, and customer commitments.
- Make every important action observable, reviewable, and reversible where possible.
We build this kind of thing for ourselves
The best evidence is shipped software: security automation, AI research systems, open agent infrastructure, monitoring, and production API platforms.
Pwnie.ai
Details:
Continuous exposure management that blends AI-assisted collection with offensive-security analyst review, reporting, and customer workflow integrations.
What we built
- AI-assisted security triage and exposure workflows for analyst review
- Customer-facing reporting, prioritization, and remediation collaboration
- Custom enterprise implementations for SOC analyst workflows
CTEM.org
Details:
The open exposure identifier standard we founded and maintain for classifying, routing, trending, and reporting exposure management work.
What we built
- A practical standard for describing exposures in operational language
- Reusable classification patterns for CTEM routing, ownership, and reporting
- Community-facing documentation that turns methodology into implementation guidance
RedCaller
Details:
Automated red teaming for phone-based AI agents, including prompt injection, jailbreak detection, data extraction prevention, and continuous AI security testing.
What we built
- Voice-agent attack workflows for testing phone-based AI systems
- Automated red-team scenarios for prompt injection, jailbreaks, and data exposure
- Managed platform patterns for dashboards, reporting, and ongoing AI security assessment
OpenGraph.io
Details:
Production APIs for link previews, web scraping, screenshots, extraction, oEmbed, and MCP-based integrations.
What we built
- High-volume developer APIs with authentication, billing, and documentation
- Web scraping, metadata extraction, screenshot, and oEmbed workflows
- Operational infrastructure for reliability, scaling, and customer support
AI Down
Details:
Real-time monitoring for AI provider outages using official sources, synthetic probes, user reports, and supply-chain correlation.
What we built
- Provider status monitoring and incident correlation for AI services
- Synthetic checks and user-signal patterns for operational awareness
- A focused product for teams whose workflows depend on model uptime
Parallect.ai
Details:
A research platform that runs multiple AI engines in parallel, reconciles contradictions, cites sources, and turns expensive research into reusable reports.
What we built
- Parallel research execution across multiple AI providers
- Source-aware synthesis, contradiction handling, and reusable outputs
- A product workflow for turning one-off research into durable knowledge
prxhub.com
Details:
An open registry for signed, searchable AI research bundles that agents can search, extend, cite, and publish back to.
What we built
- Searchable public registry for AI research artifacts
- Bundle formats that agents can claim, cite, extend, and reuse
- Infrastructure for making research outputs portable across workflows
AI systems that do real work
We still build applications and APIs. The difference is that the application is now often the operating surface for agents, workflows, and structured organizational knowledge.
Agentic business workflows
Agents that operate inside real business processes, not demo chatbots bolted onto a website.
- Map the work, decisions, approvals, and handoffs before choosing tools
- Connect agents to internal systems, documents, tickets, APIs, and data stores
- Define escalation paths where humans need control or accountability
Knowledge and research systems
Systems that turn scattered documents, prior analysis, and external sources into usable organizational intelligence.
- Multi-provider research, citation, provenance, and contradiction handling
- Private knowledge bases and reusable evidence artifacts
- Agent-friendly APIs, registries, and retrieval patterns
Security and AI operations
Automation that supports analysts, triage, monitoring, and response without losing expert review.
- SOC triage and exposure management workflows
- AI provider monitoring, dependency awareness, and operational alerting
- Security controls, auditability, and data-boundary design from the start
Product and API platforms
Production-grade software with the reliability, billing, integrations, and developer experience needed to survive contact with users.
- SaaS platforms, API products, dashboards, and customer portals
- Secure cloud architecture, observability, billing, auth, and admin tooling
- Developer documentation, SDK patterns, and integration support
Useful AI is mostly workflow design, information design, and secure tool use.
Models matter, but the model is not the system. The system is the business process, data boundary, permissions model, tools, observability, and escalation path around it.
- Start with the business process and information model, not a chatbot prompt.
- Design for agent autonomy only where the organization can safely delegate.
- Keep humans in the loop for judgment, risk acceptance, approvals, and customer commitments.
- Make every important action observable, reviewable, and reversible where possible.
- Treat security, privacy, and operational resilience as product requirements.
- Build systems your team can understand, maintain, and improve after launch.
How we build AI-native systems
We do not start by asking which model to use. We start by understanding the work.
Model the work
We map the people, systems, documents, decisions, exception paths, and business outcomes that matter.Organize the knowledge
We structure the information agents need: data sources, evidence, retrieval patterns, permissions, and provenance.Build the agent loop
We create the workflows, interfaces, tools, APIs, and review gates that let agents do useful work safely.Operate and improve
We instrument the system, monitor outcomes, tune prompts and tools, close failure modes, and help teams adopt it.
AI software development FAQ
Straight answers for teams trying to turn AI capability into operational advantage.