Two costs that compound as your business matures
Workflow automation tools like Zapier and Make charge per task execution. Early on, when you have a handful of automations running occasionally, the cost is negligible. As your business matures and automation becomes central to how you operate: CRM sync, lead routing, invoice processing, onboarding sequences, and reporting pipelines. Task counts compound quickly. Teams running serious automation workflows regularly hit $200 to $500 per month on Zapier before they realise it.
AI API costs follow the same pattern. A team experimenting with ChatGPT or Claude API pays little. A team that has integrated AI into daily workflows (document processing, customer communication drafts, internal knowledge search, and code review. These teams can spend significant amounts monthly on token usage. And every query, every document, every piece of context sent to those APIs leaves your environment.
Workflow automation without per-task billing
We deploy a self-hosted workflow automation platform, a visual tool your team uses from a browser to build, test, and manage automations without writing code. The platform connects to most tools your business already uses: CRM systems, email platforms, spreadsheets, databases, communication tools, payment processors, and hundreds of other services via their APIs.
Workflows you have built in Zapier or Make can typically be recreated with modest adjustments. We migrate your active automations, run old and new in parallel until outputs are verified, then cut over. You stop paying per task.
For teams that want to go further, the platform supports custom code steps in JavaScript or Python for cases where pre-built integrations do not cover the requirement. Most business automation workflows, even complex ones with branching logic and error handling, can be built entirely without code.
Private AI on your infrastructure
We deploy a private AI environment that gives your team access to capable language models running on your own server. Your team uses a clean browser interface, the same general experience as ChatGPT, with models you select, on infrastructure you control. No query leaves your environment.
The environment supports multiple open-source models suited to different tasks: general writing and reasoning, code generation and review, document analysis, and structured data extraction. For teams that need AI to work with their own documents: answering questions about internal processes, searching across client files, surfacing relevant information from accumulated documentation. We configure a retrieval-augmented generation pipeline. Your documents are indexed and made queryable. The AI answers questions grounded in your actual content.
We deploy and manage Ollama as the model serving layer, Open WebUI as the team-facing interface, and AnythingLLM for document knowledge base configuration.
Who manages models and keeps automation running
Both automation and AI infrastructure require ongoing attention. New automation platform versions are released. Better AI models are released regularly. Open-source model quality has improved dramatically over the past year and the pace of improvement continues. Infrastructure needs monitoring.
Our support retainer covers all of it. We apply automation platform updates, evaluate and deploy improved models when they are meaningfully better, monitor server resources, and respond to incidents. Your team builds and runs automations. We keep the infrastructure underneath them current and stable.