Galen Quant for Finance
Financial Intelligence, Built to Be Audited.
Galen reads your accounting and operational data and returns the analysis you'd expect from a senior consultant — what moved, who caused it, what it cost in Rupiah, and what to do about it.
How it works
From a raw export to a decision you can defend.
Drop in the exports you already produce. Galen profiles the roles, types, and grain itself — no modeling project, no column mapping.
It names the accounts behind a movement, separates volume from price, and sizes the impact in Rupiah. Minutes from a raw file.
Open any customer, supplier, branch, or account and follow what it connects to, with its history and its risk signals.
The decision your team makes is recorded with the metric it should move — then checked against the data next period.
Every figure along the way is checked against your source data, and stays traceable back to the row it came from.
Grounded in your data
An AI that invents a number is worse than no answer at all. Every figure Galen shows is checked against your source data before you see it, and stays traceable to the row it came from months later.
Checked before you see it
Every figure is verified against your source data first. Where the data does not support an answer, it refuses rather than filling the gap with an assumption.
Traceable to the row
Open any number and follow it back through the metrics that produced it to the original record in your export.
Same input, same output
The analysis is deterministic. Re-run it on the same export next year and you get the same figures — which is what makes a trail worth keeping.
Any export, no integration project
Your data sits in six systems and nobody has time to map it.
Galen reads the exports you already produce and works out the structure, the meaning, and the accounting conventions itself.
Talk to us about your stack- Roles, types, and grain are profiled automatically — no column mapping
- Reads Indonesian accounting structures and conventions natively
- Handles inconsistent, incomplete, and messy real-world data
Today Galen works from exports. Live database connectors are in progress.
Analyze
A dashboard tells you receivables rose. It doesn't tell you who, or why, or what it cost. Galen decomposes the movement into volume, price, and mix, names the accounts responsible, and sizes the impact in Rupiah.
What Galen reads
Your data
General ledger, receivables, payroll, sales, inventory
Your business context
PeriodsEntitiesAccounting conventions
What it does
Grounded analysis engine
Reads the structure and the meaning, then does the math itself.
Finance reasoning
Correctness controls
What comes out
Findings
The movement, the driver, and the Rupiah it cost.
Recommendations
An owner, an action, and the metric to watch.
Audit trail
Every figure back to the row it came from.
REST API & MCP
Anatomy of one finding
“Receivables rose this quarter, and almost all of it sits with a handful of accounts in one branch.”
- Driver
- The accounts and branches responsible, ranked by contribution —each one opens
- Decomposition
- How much came from volume, how much from price or rate, and how much from mix.
- Impact
- Sized in Rupiah, not only as a percentage.
- Confidence
- What the data supports, and which fields were too incomplete to lean on.
What lands on the desk
A thesis, not a list of observations.
Executive summary
Written in board language, not query output.
Recommendations
Each with an owner, an action, a timeline, and a metric to watch.
Data reliability
What is trustworthy, and what needs review before you act on it.
Concentration risk, anomalies, and period-over-period movement are analysed the same way — scoped correctly, explained in plain business language.
Explorer
Every customer, supplier, branch, and account is connected to something. Open any entity and follow the relationships — its dossier, its history, its risk signals, and why it matters to the rest of the business.
Decisions
Most platforms stop at the recommendation. Galen keeps going — checking whether the decision your team made actually produced the outcome it was meant to.
Built for
The people who get asked to explain the number — and who should not need a data team to answer.
Proven across
Same platform, no customization project.
Comparison
Assessed on what each tool does out of the box. Not on what unlimited custom work could eventually reach.
- Full— native / automatic
- Partial— possible with effort or add-ons
- Limited— not a fit / heavy manual work
| Galen QuantGrounded finance analysis | General AIChatGPT / Claude data analysis | BI platformsTableau, Power BI, Metabase | AI analyticsThoughtSpot, Julius, text-to-SQL | |
|---|---|---|---|---|
| Built-in finance intelligence | Stock vs flow, periods, non-additive measures, NPL, aging | Generic; depends on the prompt | Generic; modeled by hand | Generic unless customized |
| Modeling and prep effort | Minimal — auto-profiles roles, types, and grain | Per-session cleanup and prompting | Heavy — ETL and semantic modeling | Requires a modeled semantic layer |
| Output correctness controls | Deterministic math; every figure traced; refuses if ungrounded | No built-in guard; hallucination possible | Exact on the modeled data | The generated query can be silently wrong |
| Reproducibility and audit trail | Same input, same output — and traceable | Stochastic; answers vary run to run | Deterministic, but built by hand | Depends on the generated query |
| Narrative and recommendations | Findings, the why, and the actions to take | Strong free-form narrative, unguarded | Charts only; little narrative | Brief answer-level synthesis |
| Time to first insight | Minutes, from a raw file | Minutes, with prompt engineering | Days to weeks — setup and modeling | Fast, once the warehouse is modeled |
| Visualization and dashboards | Auto-charts inside reports; not a dashboard builder | Basic charts on request | Best-in-class interactive dashboards | Strong visualization and dashboards |
| Maturity and ecosystem | Early-stage; small footprint | Large and fast-moving | Mature, broad ecosystem | Growing |
| Typical users | Non-technical finance and ops, plus the CFO | Technical users and analysts | Data and BI teams | Analysts and data teams |
FAQ
The questions buyers ask first.
Contact us
Send us one export and the question you'd ask a consultant.
- Send an export, we'll tell you what Galen can find in it
- Every figure comes back traceable to its source row
- We reply within one working day