Production, Not Pilots
We build managed workflows that go live in 7 days, with guardrails from day one. No six-month proof-of-concept. No slideware. Just working automation.
What de-friction actually looks like
Five tools that do not talk to each other, so someone copies data between them by hand. That is friction.
A subscription you pay for because you need one feature, but the rest sits unused. That is friction.
A person entering the same information into two systems because nobody wired them together. That is friction.
We remove it. Not by adding another tool to your stack, but by connecting what you already have, automating the repetitive parts, and eliminating the subscriptions and manual steps that should not exist in the first place.
Own your outcomes, not your vendor's roadmap.
The De-Friction Thesis
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.
Who Builds This
Two decades of data strategy, architecture, and AI across industries
My name is Hammad Shah. I have spent the past two decades as a data strategist, architect, and engineer, building data solutions across financial services, insurance, energy, pharma, telecom, retail, manufacturing, government, and global mobility.
Through HSE Analytics, the consulting practice I founded in 2006, I have designed and delivered data architectures for billion-dollar organizations including American Express, Nationwide Insurance, Biogen Pharma, Takeda Pharma, PetSmart, Sallie Mae, American Tower, the Canadian Association of Petroleum Producers, and Ohio State Government. The work spans data strategy and roadmapping, data governance programs, master data management, data quality and literacy initiatives, and taking AI-driven data solutions to production.
The hands-on technology stack covers the range you would expect at that scale: Azure Synapse, Databricks, Data Factory, and Data Lake Storage on the cloud side. Oracle, SQL Server, and Snowflake for relational data. MongoDB and CosmosDB for document stores. dbt for transformation pipelines. The tools change with the engagement; the pattern does not.
At a 50-person energy advocacy organization, I inherited a brittle economic model written in 1,500 lines of R that took hours to run. I rebuilt it as a production API that returned results in seconds. I built an agent-based simulation modeling 880 assets across 47 companies and $180B in revenue to test how policy changes would cascade through the system. I built investment screening tools that forced discipline into capital allocation decisions. In every case the pattern was the same: find the manual bottleneck, understand the constraints, automate the repeatable parts, and keep humans in the loop for judgment calls.
DecarbDesk came from watching small and mid-size teams drown in the same operational drag I had already automated away at enterprise scale. Invoice follow-ups, reconciliation, intake routing, reporting. None of this is hard. It is just tedious, and it compounds. A controller spending Friday afternoons copying data between tools is not a technology problem. It is a plumbing problem. The technology exists. Someone just has to wire it up and keep it running.
That is what we do. I am not interested in building a platform you log into or selling you a dashboard. I want to take the specific, concrete, manual work that drags your team down and make it disappear into infrastructure that runs reliably in the background. The philosophy is simple: menial work can be automated away intelligently, and the people freed up should be spending their time on things that actually require human judgment.
The data architecture background is not incidental to the automation work. Two decades of designing data strategies, implementing governance programs, and connecting enterprise systems for billion-dollar organizations teaches you how messy real operational data is and what it takes to make it usable. The same discipline that designs a current-and-future-state data architecture for a Fortune 500 designs the data flow for a collections workflow. You have to understand the schema, map the integration points, handle the edge cases, and build controls that hold up in production.
Every engagement adds to our pattern library. The edge cases we handle for one client's collections workflow improve the next client's collections workflow from day one. The integrations we build for one QuickBooks setup make the next one faster. The prompts we tune for one industry's communication style inform the next. Each build makes the infrastructure better, each client makes the patterns deeper, and the cadence of improvement accelerates over time.
Industries
Representative Clients
American Express, American Tower, BGRS Mobility Services, Biogen Pharma, Boston Financial Data Services, Canadian Association of Petroleum Producers, Nationwide Insurance, Ohio State Government, Ohio State Hospitals, PetSmart, RCI (Resorts and Condominiums International), Sallie Mae, Takeda Pharma, Toys R Us, and others.
Take a complex operational system, understand it at a mechanistic level, automate the repeatable parts with controls, and keep humans in the loop for judgment. That is the through line from enterprise data architecture to workflow automation.
Why DecarbDesk
Six principles that shape every workflow we build and manage.
Constrained Tool Stack
We work with a focused set of tools (QuickBooks, Gmail, Slack, Google Sheets, HubSpot) instead of trying to integrate everything. Fewer integrations means faster builds, fewer failure points, and more reliable automations. Every build adds to our pattern library, so the next one is faster and handles more edge cases out of the box. For organizations running enterprise systems (SAP, Salesforce, Oracle, custom portals), we expand the integration surface and scope a discovery phase accordingly. The principle holds: fewer integrations per workflow means more reliable automation, regardless of the tools involved.
Guardrails by Default
Human approval gates, complete audit logs, and hard caps on run volume are not add-ons or premium features. They ship with every workflow on day one. Your team stays in control.
Managed, Not DIY
We build and operate the workflows so you do not have to. No hiring an automation engineer. No forcing your team onto a new platform. You focus on your business; we keep the automations running.
Things Break. We Fix Them.
APIs change. Vendors update auth flows. Edge cases appear. A collections email gets a bounce-back from a new spam filter. These things happen weekly in production automation. You never see them because we handle them. Your monthly report shows what we caught and what we fixed.
You Own Everything
We build on open standards and open-source infrastructure: SMTP, HTTP, PostgreSQL, Docker, Git. Your data lives in your tools, not ours. If you leave, there is nothing to export and nothing to migrate. We deliberately avoid building dependency. The goal is a client who stays because the workflow works, not because their data is trapped.
Every Build Makes the Next One Better
We do not start from scratch each time. Edge cases handled for one client's collections workflow improve the next client's from day one. Integrations we build for one QuickBooks setup make the next setup faster. Prompts tuned for one industry inform the next. Our pattern library deepens with every engagement, so build quality and speed compound over time.
Why This Works Now
Software has gone through three phases. The first was hand-coded rules: forms, CRUD apps, static automations. You defined every step explicitly. The second was data-driven: analytics, personalization, recommendations. The system learned patterns but you still clicked through screens to get work done.
We are now in the third phase. Language models turned natural language into a control layer. That means you can describe the outcome you want, and the system breaks it into steps, pulls data from your tools, takes actions, asks for approval when it needs to, and executes. Fewer clicks, more delegation.
That is what "agent-first" actually means. Not a chatbot bolted onto an existing product. Not an AI assistant that suggests things for you to go do manually. It means the system reads your invoices, drafts the follow-up, checks the aging schedule, sends the reminder, and logs every step. You review and approve the ones that matter.
The problem is that most vendors use this language without delivering the substance. They add a chat window to an existing product and call it agent-first. The way to tell the difference: does the system actually execute inside your tools, or does it just give you suggestions? Can it read and write across your CRM, invoices, schedules, and email, or is it trapped in one screen? Are there real guardrails (approvals, audit logs, caps, a kill switch) or is it just running unsupervised?
DecarbDesk is built for this phase. Every workflow we build is an agent that operates across your tools end-to-end, with you supervising. Not a dashboard you check. Not a platform you learn. A system that does the work, shows you what it did, and stops when it needs your judgment. And because the agent does the work, you should pay for outcomes delivered, not for access to a screen.
This applies at every scale. A 30-person firm connects QuickBooks and Gmail and builds a collections workflow in a week. A 3,000-person organization connects SAP, Salesforce, and a custom procurement portal and builds the same collections workflow, but the discovery and data normalization phase expands to match the complexity of the environment. The deployment model that enterprises need for AI and agents today looks like the deployment model they needed for Salesforce and SAP a decade ago: someone has to redesign the business flows, clean and connect the data, and implement at scale with real controls. That is the work.
What Powers It
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.
See if DecarbDesk is the right fit
Book a 15-minute call. We will ask about your workflow, estimate the impact, and tell you honestly whether we can help.