Tan Gravam

Tan Gravam

Enterprise technology, architecture, AI and practical software. Amsterdam.

I've spent 18 years working inside complex enterprise systems, finance technology, architecture and transformation.

Over time, one thing became increasingly clear: the most interesting problems are often not in the system itself. They are in the work people do around it.

  • The spreadsheet someone maintains because the official system is missing context.
  • The approval that lives in email.
  • The expert everyone asks when the process reaches an exception.
  • The manual reconciliation between systems.
  • The recurring workaround that quietly became part of the operating model.

I'm increasingly focused on turning those observations into practical software.

What I work on

Enterprise technology
Complex systems, architecture, finance processes and real operational constraints.
AI and software building
Using modern AI tools to turn domain knowledge into useful applications faster — without treating every problem as an AI problem.
Workflow and product discovery
Finding hidden decisions, context gaps, manual work and possible product opportunities inside real business processes.
Technology leadership
Helping teams make better decisions, reduce unnecessary complexity and build systems that work in practice.

Where the expertise comes from

The edge is not simply that I can build software. It is the years behind the problems I choose — hands-on across SAP FI, Treasury and Risk Management, ABAP, Fiori and SAPUI5, from configuration to custom development.

  • SAP Treasury & Cash Management — TRM, One Exposure from Operations, liquidity items, bank statement integration and BCM.
  • Corporate treasury and enterprise systems architecture — how ERP, TMS, banks and market data fit together, and which system should own which data.
  • Integration and controls — bank connectivity and file formats, segregation of duties, and the evidence a finance process has to leave behind.
  • Finance-systems delivery — turning vague demands into clear delivery decisions on large programmes: intake, scope, UAT, cutover and governance.

What I'm building

Workflow X-Ray is a free tool for analysing a real workflow and surfacing hidden decisions, missing context, manual work, automation opportunities, human judgment and possible product signals.

Workflow X-Ray is part of Gravam Tools: small, practical utilities for enterprise technology, AI workflows, architecture, finance and product building.

I also maintain other products and experiments; their current status is on the products page.

How I build

I start with a real operational problem, not an AI capability. AI has made implementation dramatically cheaper, which means domain experts can test ideas that once required a team. But faster implementation does not remove the need for product judgment.

I use AI heavily in the build workflow while keeping problem selection, product decisions, trade-offs and final review human.

What I write about

Enterprise AI and workflow design, systems architecture and controls, technology leadership and delivery, and building products — with the deep finance end of it: treasury management systems, SAP treasury, cash and liquidity, treasury risk and bank connectivity and architecture.

How this site's content is made

  • AI-assisted, reviewed before it goes live. I write and update with AI in the loop — some updates are drafted and edited by an AI editor working to my brief. Nothing publishes without passing a review: an editorial pass and, for technical pages, a check against the primary sources listed at the foot of the page. A review gate keeps anything unreviewed out of the sitemap, topic hubs and search index — draft work stays draft.
  • Sourced where it's technical. SAP and standards-based articles cite verified primary sources (SAP Help, SWIFT, the Basel Committee) and carry an explicit release-scope note, because behaviour differs by version, edition and channel — so verify against the docs for your system.
  • No invented specifics. I don't fabricate client names, first-hand anecdotes or metrics. Where a number is used to illustrate a method, it's labelled as illustrative, not a real figure.
  • Reasoning, not borrowed case studies. The enterprise AI writing applies first-hand systems experience to workflow redesign, controls and architecture — it is not client AI-transformation case studies I have not run. I don't give personalized investment or financial advice, and I don't endorse vendors.

Start here

  1. Workflow X-Ray — paste a workflow and see what is really happening in it.
  2. AI Workflow Design — what stays a rule, what a model can do, where a person decides.
  3. Gravam Tools — every free tool, no signup.
  4. Treasury Systems Architecture — the deep systems end: banks, formats, controls.

Elsewhere

If the work above is the work you have, here is how we could work together — or just tell me about the workflow.