Work that survives the room.
One repeatable four-step process turns a messy question into a deliverable that holds up: a paid assessment that scopes the problem, a build against agreed success criteria, integration into your existing systems, and handover with documentation. You own the output, including the source code.
Discover
Before a single number is written, we pin down the decision the work must support, the audience who will read it, and the exact standard it will be judged against.
- The real decision, not the brief
- Who signs off — and what convinces them
- The bar: CFO, fund or panel
Source
We pull from filings, primary disclosures and credible market data. Every assumption is written down and defended in the open, never buried inside a cell.
- Primary filings & disclosures
- Credible, dated market data
- Assumptions documented, not hidden
Build
We build to institutional standards — fully linked models, auditable formulas and a thesis that holds together when someone traces it all the way back to source.
- Linked models, zero hard-codes
- Auditable, traceable formulas
- A thesis that hangs together
Stress-Test
We attack our own work first — balance checks, sensitivity on every driver and peer benchmarking — so what you present has already survived the toughest question.
- Balance & integrity checks
- Sensitivity on every driver
- Benchmarked against peers
From a vague ask to an answer you can defend
A high-stakes question, scattered data and no single standard to be judged against.
A traceable, auditable deliverable where every number defends itself under scrutiny.
Four principles behind every deliverable
Click any figure and defend it — every number ties back to a named, dated source.
Linked, legible formulas. No hard-coded cells, no black boxes, no surprises.
We argue the other side first, so your version survives the hardest question in the room.
AI does the gathering and drafting; humans own the judgement and the risk.
Have work that has to hold up?
Tell us the decision it needs to support. We will map these four steps to your problem and show you the standard before you commit.
The adoption curve we work inside.
References: NASSCOM AI Adoption Index · EY–NASSCOM AI Adoption Index. Figures carry the year they refer to. Where sources disagree we show both rather than picking quietly.