Build AI agents that handle repetitive work — invoice extraction, ticket routing, customer chat, document processing. Custom-built around your existing systems, not off-the-shelf bots.
Most "automation" platforms force you to fit your business into their template. We build the other way around — start from a repetitive task that's eating your team's time, design an agent that does it well, and integrate it cleanly with the tools you already use (M365, Google Workspace, your CRM, your database).
Half-day workshop to map the candidate processes — how often it runs, who does it now, what data it touches, how often it goes wrong. Output: ranked list of automation opportunities with estimated ROI.
Custom agents built on top of Claude, GPT-4o, or Gemini APIs, with proper guardrails: structured output, validation, human-in-the-loop for risky actions. Hosted on AWS, Azure, or your own infrastructure.
Invoice/receipt extraction (PDF or image → structured data), contract review (clause flagging), KYC document verification, and report generation. Trained on your document formats for accuracy above 95%.
Khmer/English customer-facing chatbots with knowledge-base grounding (no hallucinations on your prices/policies), Telegram and Messenger integration, and seamless handoff to human agents.
Usually: invoice/receipt extraction (saves 30+ hours/month per accountant), or customer-facing FAQ chatbot (handles 40–60% of incoming queries). Both have clear ROI and don't carry catastrophic risk if the agent gets something wrong.
Those are great for connecting APIs with rules. We use them where they fit, plus add LLM agents on top for tasks that need judgment — reading a contract, summarizing a meeting, drafting a customer reply. Hybrid approach.
Discovery + small first agent: $3,500–$8,000. Mid-complexity (document processing, multi-step agents): $10,000–$30,000. Ongoing hosting + improvements: typically $300–$1,500/month.
We design around it — agents get structured tools (database queries, search) rather than open-ended generation, and we validate output against rules before any action is taken. For customer-facing chat we ground in your real knowledge base so it can't invent policies.
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