Technology Platforms
Three layers.
Every AI deployment
needs all of them.
Security and oversight for AI agents. Practical automation for day-to-day operations. Cloud infrastructure that scales and gives you access to the leading AI models. We build across all three.
Layer 01
Agentic AI
Enterprise · Governed · Secure
Layer 02
Workflow Automation
SMB · Low-code · N8N-first
Layer 03
Cloud & Infrastructure
Scalability · Compliance · AI-ready
Layer 01 · Agentic AI
Agents that access corporate data need strong security and oversight from day one.
In 2026, enterprises are moving agents into production — granting them access to CRMs, financial systems, internal knowledge bases and customer data. We build AI agents with their own identities, access controls, audit trails and human approval steps designed in from the start.
⚡
48%
of cybersecurity professionals in a Dark Reading poll named agentic AI the top attack vector for 2026.
Source: Dark ReadingEnterprise security architecture
How we secure agentic AI deployments
Every AI agent we build follows four rules. They're the difference between an agent that works for you and one that becomes a liability.
🔐Identity & least-privilege access — Every agent gets its own login with only the permissions it needs, managed through your existing access controls.
📋Full audit trail and action logging — Every action taken by an agent — every system call, data access and decision — is logged with timestamps and context.
🛑Human approval and an off switch — High-impact actions need a person’s approval. Every agent has an off switch you can use at any time.
🏢Corporate data integration with guardrails — Agents only see the data they’re authorized to use.
Anthropic Claude
Frontier LLM
Primary LLM for production AI systems. Claude's tool-use API, extended thinking and up to a 1M-token context window make it our first choice for complex, multi-step agents working across large sets of company data.
Tool use APIExtended thinking1M contextAmazon BedrockMicrosoft FoundryGemini Enterprise
LangChain / LangGraph
Agent Framework · Orchestration
A widely used framework for building AI agents. LangGraph lets an agent branch, retry and keep track of context across dozens of steps.
Agent chainsLangGraph statefulTool bindingMemoryRAG
Hermes
Agent Platform · Memory & Approvals
An agent platform built on Nous Research's open-source Hermes Agent, with custom agents added on top. It gives agents persistent memory, approval steps before they act and a chat interface on the web or Telegram. Nous uses an open-core model: the software stays open, you pay for hosting, security, management and support.
Open sourcePersistent memoryApproval stepsTelegram/chat UI
Layer 02 · Workflow Automation
Most SMBs don't need a big AI program.
They need processes that run themselves.
Most small and mid-size businesses are drowning in manual, repeatable work. The fix isn't a six-month AI transformation. It's targeted automation that shows results in weeks. N8N is our platform of choice: more flexible than Zapier, able to run on your own servers for privacy, and able to add AI steps in the middle of a workflow.
🔗
1,000+
ready-made integrations in N8N, plus any tool with an API
Our platform
N8N — why we chose it
Self-hostable — run it on your own servers
AI nodes built in — call Claude, OpenAI or Gemini models mid-workflow
Code nodes for custom logic when visual nodes aren't enough
Workflows saved in Git, so every change is tracked
1,000+ ready-made integrations: CRMs, databases, communication tools and more
Runs as soon as it’s triggered, with no plan-based polling delays
No per-task pricing when self-hosted, so costs stay flat as volume grows
Compared with Zapier and Make
Why clients move off them
Costs rise with every task run, so high volume gets expensive fast
Your data passes through their servers, which isn’t ideal for sensitive information
Pricing tiers with hard task limits on each plan
No self-hosting option — you can’t run it on your own servers
Layer 03 · Cloud & Infrastructure
Cloud lets you scale.
It's also where the AI models live.
Every AI agent and automated workflow runs on cloud infrastructure. We work across all three major cloud providers, and each now offers the leading AI models and tools for running agents: Amazon Bedrock, Microsoft Foundry and Google's Gemini Enterprise Agent Platform (formerly Vertex AI). We recommend what's right for your workload, compliance needs and budget.
🌐
Claude is available natively on Amazon Bedrock, Microsoft Foundry and Gemini Enterprise Agent Platform — so you can use the cloud provider you already have.
💡
Infrastructure as code
Every cloud environment we build is set up with Terraform scripts, so it’s repeatable, auditable and versioned
→Amazon Bedrock — Claude, Llama, Mistral, Cohere and Amazon Nova models behind one API
→Bedrock AgentCore — managed runtime, memory, policy controls and evaluations for production agents
→Bedrock Knowledge Bases and Guardrails — grounded retrieval and content safety
→EC2, ECS and EKS for containerized AI workloads
→Microsoft Foundry (formerly Azure AI Foundry) — OpenAI, Claude, Llama, Mistral and DeepSeek models in one catalog
→Foundry Agent Service — hosted agents with sandboxed sessions, memory and tool access
→Model router — sends each request to the best-fit model for cost and quality
→Entra ID and the Azure compliance stack
→Gemini Enterprise Agent Platform (formerly Vertex AI) — Gemini, Claude, Gemma and 200+ models in Model Garden
→Agent Development Kit, Agent Studio and Agent Engine — build, run and govern agents
→GKE and Cloud Run for containerized AI services
→BigQuery as the data layer for analytics and retrieval
Partners & full stack
The rest of the stack
we build on.
We work with leading AI labs, cloud providers and automation platforms, so every project is built on proven technology, with support from the companies that make it. These are the tools we build with every day — not logos we've collected.
LLM
OpenAI
OpenAI models for multimodal tasks and API integrations, when a project calls for them.
Database · Backend
Supabase
Database and backend for AI apps that need real-time data and search.
AI · Vector Database
Pinecone
The search index behind our document Q&A builds. It finds passages by meaning, not just keywords.
Object Storage
Cloudflare R2
Secure storage for your source documents, with no fees to move your data out.
Frontend · Deployment
Vercel · Next.js
Production web and AI product delivery. Fast global hosting with automatic deployments.
Not sure which stack
is right for your build?
Start with the AI Readiness Assessment. We'll map your current tools and deliver a concrete technology recommendation.