Technology Platforms
Three layers.
Every AI deployment
needs all of them.
Enterprise-grade security and governance for AI agents. Lightweight automation for SMB operations. Cloud as the scalability and AI model access layer. We build across all three.
Layer 01
Agentic AI
Enterprise · Governed · Secure
Layer 02
Workflow Automation
SMB · No-code · N8N-first
Layer 03
Cloud & Infrastructure
Scalability · Compliance · AI-ready
Layer 01 · Agentic AI
Agents that access corporate data need enterprise-grade governance 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 agentic systems with identity, access control, audit trails, and human-in-the-loop gates designed in from the start.
⚡
48%
of security professionals rank agentic AI as the #1 enterprise attack vector in 2026 (Dark Reading)
📈
12×
more AI projects reach production when AI governance tools are in place
🚨
63%
of organizations cannot enforce purpose limitations on their deployed AI agents (Kiteworks 2026)
Enterprise security architecture
How we secure agentic AI deployments
Every agentic system we build for enterprise clients is architected around four non-negotiable pillars — the difference between an agent that works for you and one that becomes a liability.
🔐Identity & least-privilege access — Every agent gets a distinct identity with minimum permissions. Non-human identity management via IAM, RBAC, and ABAC controls.
📋Full audit trail and action logging — Every action taken by an agent — every API call, data access, decision, and tool invocation — is logged with timestamps and context.
🛑Human-in-the-loop gates & kill switches — High-impact actions require explicit human approval. Every deployed agent has a defined kill switch you can trigger instantly.
🏢Corporate data integration with guardrails — RAG pipelines scoped to authorized data sets. Data never leaves your environment to train models.
Anthropic Claude
Frontier LLM · Claude Partner Network
Our primary reasoning model for enterprise agentic deployments. Claude's tool-use API, extended thinking, and 1M token context window make it the strongest choice for complex, multi-step agents operating over large corporate data sets.
Tool use APIExtended thinking1M contextAWS BedrockAzure AIVertex AI
LangChain / LangGraph
Agent Framework · Orchestration
Industry-standard framework for building production agents. LangGraph adds stateful, graph-based orchestration — critical for complex multi-step agents that need to branch, loop, and maintain context across dozens of tool calls.
Agent chainsLangGraph statefulTool bindingMemoryRAG
CrewAI
Multi-agent Orchestration
Role-based multi-agent framework. We use CrewAI when a task benefits from specialist agents collaborating — a Researcher, Analyst, Writer, and Compliance Checker — each with defined roles, tools, and scope.
Role-based agentsSequential tasksTool sharingProcess types
Layer 02 · Workflow Automation
SMBs don't need enterprise AI.
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 workflow automation that delivers ROI in weeks. N8N is our platform of choice: more powerful than Zapier, self-hostable for data privacy, and capable of embedding AI decision nodes mid-workflow.
⏱
40+ hrs
saved monthly by customer service automation agents at small teams (Gartner 2026)
💰
30–50%
faster finance and ops close processes from automated invoicing and forecasting
🔗
400+
native integrations in N8N — more than any other self-hostable automation platform
Our platform
N8N — why we chose it
Self-hostable — your data never touches a third-party SaaS server
AI nodes built in — call Claude, GPT-4o, or Gemini mid-workflow
Code nodes for custom logic when visual nodes aren't enough
Version-controlled via Git — workflows treated as code, not config
400+ integrations: CRMs, databases, APIs, communication tools
Sub-second execution on self-hosted vs. Zapier's queued model
No per-task pricing — fixed cost regardless of automation volume
Why clients migrate away from
Zapier / Make limitations
Costs scale with every task execution — gets expensive fast at volume
All data passes through Zapier/Make servers — not ideal for sensitive data
Limited logic — complex branching requires multiple zaps and workarounds
No native AI integration — have to use HTTP requests to call models
No version control — changes are irreversible and hard to audit
Locked into SaaS pricing tiers with hard task limits per plan
No self-hosting option — you cannot run it in your own infrastructure
Layer 03 · Cloud & Infrastructure
Cloud is the scalability layer.
And the AI model access layer too.
Every agentic system and automation workflow needs cloud infrastructure underneath it. We're platform-agnostic across all three hyperscalers — and all three now offer first-class access to frontier AI models via Bedrock, Vertex AI, and Azure AI Foundry. We recommend what's right for your workload, compliance posture, and budget.
🌐
3
hyperscalers we deliver on — the only AI model available on all three is Claude (Anthropic)
🔒
FedRAMP
authorized AI model access on GovCloud and Azure Government for regulated workloads
💡
IaC-first
Every cloud environment we build is provisioned via Terraform — repeatable, auditable, versioned
→EC2, ECS, EKS containerized workloads
→Amazon Bedrock — Claude, Titan, Llama
→GovCloud for FedRAMP workloads
→Migration and managed services
→Azure AI Foundry — Claude, GPT-4o, Phi
→Azure Government for ITAR/FedRAMP
→Azure OpenAI Service (enterprise)
→Entra ID and compliance stack
→Vertex AI — Claude, Gemini, Llama
→GKE and Cloud Run containerization
→BigQuery data platform builds
→Assured Workloads for gov compliance
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.