AI systems we design and ship
A map of the systems we build — not slide-ware, not demos. Each one is engineered for production: observable, evaluated, secured, and integrated with the systems your teams already use. The shape of a solution is always constrained by your data, your architecture, and your risk tolerance; the categories below describe the territory.
Knowledge & Assistants
Systems that ground answers in your knowledge — with citations, access control, and refusal behavior when evidence is missing.
AI Assistants
Conversational assistants grounded in your knowledge and tools.
RAG Systems
Grounded retrieval-augmented systems with citations and access control.
Internal Knowledge AI
Internal knowledge assistants grounded in policies and documentation.
Customer Support AI
Tier-1 resolution with safe automated actions and human handoff.
Agents & Automation
Reasoning agents and event-driven automations that act safely inside bounded permissions with human checkpoints.
AI Agents
Reasoning agents that use tools safely under bounded permissions.
Workflow Automation
Event-driven automations with retries, queues, and human checkpoints.
AI Data Processing
Pipelines that clean, enrich, and structure data with AI.
Intelligence & Documents
Extraction, classification, and validation from high-volume documents and live production signals.
Document Intelligence
Extraction, classification, and validation from high-volume documents.
AI Monitoring
Observability, drift detection, and quality tracking for AI in production.
Systems & Security
Production AI systems and the guardrails, filters, and monitoring that keep them safe in real deployments.
AI Security Systems
Guardrails, filters, and monitoring for production AI systems.
System / Software Dev
Full-stack software development around your AI capabilities.
A 9-stage process from problem to production
Every solution we ship runs through the same engineering discipline — discover, analyze, design, build, integrate, evaluate, secure, deploy, optimize. No black boxes. No vibe-driven releases.
- 01Stage
Discover
We start by understanding the business: goals, constraints, existing systems, and the people the AI will serve. We don't start with models — we start with the problem.
- 02Stage
Analyze
We audit the data, systems, and workflows that surround the problem. Quality of AI is bounded by quality of inputs — we make those explicit.
- 03Stage
Design
We design the system architecture: model, retrieval, tools, guardrails, human-in-the-loop points, and observability. Design before code.
- 04Stage
Build
We build the system with engineering rigor: versioned prompts, structured outputs, tests, and reproducibility. Not a notebook — a system.
- 05Stage
Integrate
We connect the AI to your real systems: APIs, identity, data, and existing tooling. The AI lives inside your stack, not beside it.
- 06Stage
Evaluate
We measure the system against real success criteria with automated and human evals. We don't ship on vibes — we ship on evidence.
- 07Stage
Secure
We attack the system before deployment: prompt injection, retrieval poisoning, tool abuse, sensitive disclosure. Find weaknesses first.
- 08Stage
Deploy
We ship with safety: gradual rollout, monitoring, rollback, and clear ownership. Production-grade, not demo-grade.
- 09Stage
Optimize
We iterate: cost, latency, quality, and new capabilities. AI systems drift; we keep them sharp with ongoing evaluation and improvement.
Have a system in mind?
Tell us the problem you are trying to solve, the systems you already run, and the constraints we need to respect. We will come back with a scoped proposal — a pilot, an implementation, an assessment, or a red team exercise.