Agentic AI

Agentic engineering, with the guardrails written down.

We ship production software with Claude Code, Codex, and self-hosted models under an agent autonomy policy and a verification harness. Then we install the same setup in your team.

Agent autonomy policy Tests gate agent output Human review on every diff

Process

Assess

Audit your codebase and AI readiness.

Configure

Build CLAUDE.md, MCP servers, and slash commands for your stack.

Build

AI writes, humans review, tests validate.

Scale

Multi-agent workflows with your team trained to operate.

For engineering teams

Agentic engineering, installed in your team

An agent autonomy policy, permission gates on agent tool calls, CLAUDE.md and MCP setup, a verification harness where tests gate agent output, a prompt-injection guard, and training. We run the same setup ourselves: 1,700+ commits from a five-person team plus agents, under 190+ Vitest and Playwright spec files.

Agentic engineering for dev teams AI in your product instead

Flagship Service

AI-Augmented Development

We lead engineering AI-first. Multi-agent workflows, human verification, 5x velocity. Not vibe coding—directed AI development.

  • All The AIs — Claude, GPT, Copilot, Gemini, local models—we use what works.
  • Multi-Agent Workflows — Run 3-5 AI agents in parallel on complex features.
  • Human Verification — Every line reviewed by engineers who understand the code.
  • Context Engineering — CLAUDE.md configurations and MCP servers tuned for your codebase.

AI Consulting FAQ

Common questions about our AI and agentic development services.

What is agentic AI and how is it different from regular AI? +

Agentic AI refers to AI systems that can autonomously plan, reason, and execute multi-step tasks rather than just responding to single prompts. Traditional AI answers questions; agentic AI completes workflows. For example, an agentic system can research a topic, draft a report, verify facts, and send it for review, all without human intervention at each step. We build these on Claude, OpenAI, and self-hosted open-weight models, connected to your existing systems through tool calls and MCP servers.

What is NIST AI RMF and why does it matter? +

The NIST AI Risk Management Framework is the leading standard for responsible AI deployment in the US. It provides guidelines for identifying, assessing, and mitigating risks in AI systems. We build governance into every AI system from day one: bias monitoring, data privacy controls, audit trails, and human oversight mechanisms. This protects your business from regulatory risk and builds trust with your users.

Can you integrate AI into our existing Laravel application? +

Yes. We wire AI features into existing applications: Laravel / PHP, Node, Go, and .NET. This includes adding LLM-powered features (intelligent search, content generation, automated moderation), building RAG systems on top of your existing data, and deploying AI agents that interact with your application's APIs. We do this without requiring a rewrite, integrating into your current queue systems, events, and service architecture.

How do you handle AI data privacy and security? +

We implement layered security: data isolation so your proprietary information never leaves your infrastructure when needed, access controls on AI endpoints, prompt injection prevention, output filtering, and comprehensive audit logging. For sensitive industries like healthcare or finance, we deploy private model instances or use API providers with BAAs and compliance certifications.