BitWiser
A financial intelligence platform we built to turn messy, high-volume financial data into clear, explainable insights, pairing a deterministic analytics engine with an LLM reasoning layer.
- Domain
- Financial Intelligence
- Discipline
- Generative AI · Data
- Type
- In-house R&D build
Business Problem
Finance teams sit on enormous volumes of transactional and market data, yet extracting a clear answer to a simple business question can take days of manual spreadsheet work.
The obvious move is to point an LLM at the data. The obvious problem is that a model confidently inventing a revenue figure is worse than no answer at all. We built BitWiser to find out whether a system could answer plain-language questions about a financial position with numbers that are correct, explainable, and always traceable back to source data.
The AI Pipeline
We separated deterministic computation from language reasoning, so the LLM never invents a number. It only explains numbers the analytics engine has already computed.
Retrieval Layer
Relevant metrics, definitions, and prior context are retrieved from a vector store to ground every response.
Deterministic Compute
All figures are produced by an auditable analytics engine, never by the model, eliminating numerical hallucination.
Reasoning & Narrative
An LLM composes the explanation, highlights drivers, and frames trade-offs in the language of the business.
Citations & Provenance
Every claim links back to the exact records and calculations that produced it, so trust is verifiable.
Data Processing
Financial data arrived from multiple systems in inconsistent formats. A Kafka-based ingestion layer normalizes, validates, and deduplicates records in a streaming fashion, so insights reflect near-real-time reality.
A carefully modeled PostgreSQL warehouse stores the canonical, reconciled figures, while a vector store holds the semantic context the reasoning layer needs to interpret them.
Analytics Engine
Streaming ingestion feeds a reconciled data warehouse. Queries flow through a deterministic compute core, then an LLM reasoning layer turns results into sourced narrative insights.
What It Demonstrates
The core idea held up: separate the arithmetic from the language. Once the analytics engine owns every number and the model is only allowed to explain figures it has been handed, numerical hallucination stops being a tuning problem and becomes structurally impossible.
That constraint is the whole design. It is also the part most teams skip, and it is the first thing we would bring to a finance or reporting product, where an insight nobody can trace back to source data is not an insight at all.
Technologies Used
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