Financial Services
Enterprise Financial Intelligence Platform
RAG-powered financial reporting with an automated intelligence layer.
- RAG
- NL-to-SQL
- LangChain
- Vector DB
- Agentic Workflows
70%
reduction in manual effort across reporting workflows
< 2 mo
to full financial consolidation, end to end
1 prompt
replaces multi-hour report compilation
- Plain-English queries over balance sheet, P&L and trial balance, with answers in seconds.
- Zero hallucination, every answer source-cited to the underlying record.
- Automated executive summaries with variance analysis, exported as PDFs.
The reduction is in assembly and first-pass commentary. Review, judgement and sign-off remain with the finance function.
Recurring financial reporting consumes a large share of skilled finance capacity, and very little of that effort is analysis. The work is assembly: pulling figures from several systems, reconciling definitions that differ between them, building the pack, and then writing the commentary that explains what moved and why.
Because it recurs monthly or quarterly, the same assembly is performed again from the beginning each cycle. Institutional knowledge about which number comes from where lives with a small number of people, and the reporting calendar is the constraint on how quickly anyone finds out how the business is performing.
Why it is hard
Traditional business intelligence addresses part of this and stops short. A dashboard can show that a figure moved; it cannot usually say why, because the explanation lives in documents, contracts and commentary that sit outside the warehouse.
That leaves the hardest and most valuable part of reporting — the narrative connecting a number to the operational reality behind it — as manual work, performed under deadline by the people who could otherwise be acting on it. It is also the part most exposed to being wrong, because it depends on whoever writes it remembering every relevant contract and commitment.
What we built
The platform applies retrieval-augmented generation across financial data and the surrounding document corpus, so a reporting question is answered from both the figures and the source documents that explain them.
An automated intelligence layer assembles the recurring reporting and surfaces variances with the supporting evidence attached, with every figure and statement traceable to the system or document it came from. Finance reviews and signs; the assembly and the first-pass explanation are no longer done by hand.
- Retrieval across financial systems and the surrounding document corpus together
- Automated assembly of the recurring reporting pack
- Variance surfacing with supporting evidence attached to each movement
- Full traceability from every figure back to its source system, document and period
- Human review and sign-off retained on all reported output
Questions about this engagement.
Can figures produced this way be used in reported financials?
With full provenance and human sign-off, which is how the platform is built. Every figure traces to its source system, document and period, and the assembly logic is inspectable rather than buried in a prompt. Finance reviews and signs exactly as before — what changes is that the pack and a first-pass explanation already exist when they start.
How is this different from a BI dashboard?
A dashboard reports figures from a warehouse. This retrieves across the figures and the documents that explain them, which is what allows it to attach a reason to a movement rather than just displaying it. The two are complementary — the dashboard remains the right tool for monitoring, and this addresses the explanation work sitting on top of it.
What happens when the underlying systems change?
Integration is built per source system, so a change affects that connector rather than the platform. Because provenance is recorded on every figure, the effect of a definitional change is traceable — you can see which previously reported figures were built on the old definition, which is considerably harder to establish in a spreadsheet-assembled process.
What this was built on.
Tell us what you're trying to solve.
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