Production, Not Pilots

Organizations of every size are stuck in the same two failure modes when it comes to operations.

The first is drowning in manual, repetitive work. A controller spending Friday afternoons copying invoice data between tools. A support lead pasting the same follow-up email for the hundredth time. It is slow, error-prone, and it burns out the people doing it.

The second failure mode is the opposite extreme: a six-month AI pilot that consumes budget, generates decks, and never ships anything to production. The team ends up worse off than when they started. Same manual work, less trust in technology, and a lighter bank account.

For larger organizations, the friction compounds differently. The manual work is the same, but it sits on top of fragmented data, inconsistent schemas across business units, and legacy systems that were never designed to talk to each other. A government department processes thousands of forms through three disconnected systems. A professional services firm runs invoicing through an ERP that nobody fully understands anymore. Before you can automate a workflow in these environments, you have to clean the data, normalize the inputs, and map the approval chains across organizational layers. AI deployments at scale tend to stall not because the model is the bottleneck, but because the data preparation and organizational readiness are.

DecarbDesk exists because there is a straightforward middle path that most vendors ignore. Pick one workflow. Build it with guardrails (approvals, logs, caps) baked in from the start. Prove it works with real numbers in the first month. Then expand. For a 30-person firm with clean integrations, that first workflow ships in 7 days. For a large organization with enterprise systems and messy data, a scoped discovery phase comes first. The method is the same. The timeline adapts to the complexity of the environment. Computable beats transformational.

That is the entire philosophy. Strip the drag out of your operations one workflow at a time, and show the receipts along the way. No decks. No roadmaps. Just working plumbing.

There is one more thing we believe: you should own your infrastructure. We do not want you dependent on us, or on any single vendor. We build on open standards and open-source tools wherever possible. SMTP for email, HTTP for APIs, PostgreSQL for data, Git for version control, Docker for deployment. If you stop working with us, your tools, your data, and your processes stay exactly where they are. No export wizard. No migration project. You just go back to running things yourself.

We build on open-source infrastructure wherever possible. PostgreSQL for data, Docker for deployment, Git for version control, open-weight models for AI where they meet the quality bar. Not because it is always cheaper (though it often is), but because you should be able to inspect, modify, or replace every piece of the stack without asking us for permission.

Your AI spend is yours to control. Whether your workflows run on a cloud API (Anthropic, OpenAI, Google Gemini) or a fine-tuned open model on your own hardware, you hold the keys. The typical cost is $1-2 per hour of workflow operation. We do not mark up token costs, bundle them into opaque fees, or lock you into a single provider. You see what you spend and you decide where it goes.

For organizations that want full data sovereignty, we can deploy workflows on local hardware: a self-contained box that processes everything on-premise. Open-source models, fine-tuned on your data, running on infrastructure you own. Nothing leaves your network. This requires more setup effort than a cloud deployment, but for some businesses it is the only acceptable option. We support both paths.

Under the hood, workflows use retrieval-augmented generation, structured agent patterns, and production-grade orchestration. That means they can read and extract data from PDFs and scanned documents (OCR), search your internal knowledge base by meaning, query databases in plain English, classify and route based on AI judgment, draft personalized communications in your voice, and sync data across your tools. They connect natively to Gmail, Outlook, Google Sheets, Excel, HubSpot, Salesforce, Slack, Teams, Shopify, Stripe, Zendesk, SharePoint, Google Drive, and more.

If you want us to handle all of this, we handle all of it. You interact with the workflow through Slack, Gmail, and Sheets, and the monthly ops report shows what happened. If you want to understand how it works, we will teach you. We offer 1-on-1 training sessions tailored to your specific workflows and use cases. Some clients want full managed service. Some want to learn enough to tune and extend workflows themselves. Some want both at different stages. The depth is yours to choose. See how our reliability layer works.

The goal is not to replace anyone on your team. It is to give them leverage. The same way a good financial model frees an analyst to focus on judgment instead of data entry, a good workflow frees your ops team to handle exceptions and relationships instead of copy-paste. Automation is a force multiplier, not a headcount reduction strategy.

For organizations sitting on large proprietary datasets, the same infrastructure does something additional: it turns your data into an operational advantage. When your historical records, domain knowledge, and business rules are indexed and wired into the workflow, the system makes decisions that a junior analyst would need hours to research. The differentiator is not the AI model. It is your data, structured and embedded into automation that runs on it every day.

This is the "small data" advantage. Generic AI models are trained on the internet. Your workflows are tuned on your 3 years of invoicing behavior, your specific customer segments, your approval patterns, and your team's communication style. A model that knows what "normal" looks like for your business catches anomalies that a general-purpose tool misses entirely. The longer a workflow runs, the more it learns about your specific patterns, and the more valuable it becomes.