Project Overview
Financial analysts spend most of their working day stitching data together — pulling from market feeds, internal models, PDF research reports and scattered notes — before they ever get to the work of actually forming a view. Our client wanted to collapse that prep layer with a copilot that could understand financial context, reason over their proprietary and external data, and deliver analyst-grade answers on demand.
The Challenge
- Ingest and normalise a wide variety of inputs — market data, earnings transcripts, research PDFs, internal models — into a single queryable knowledge layer.
- Reason over numbers, not just language: combine LLM fluency with deterministic calculation, unit handling and time-series awareness.
- Produce answers that analysts could trust and verify — every insight needed to be traceable back to its underlying source.
- Maintain compliance-grade auditability for every query, response and action in a regulated environment.
Our Approach
Unified research knowledge layer
We built a retrieval index over market data, filings, transcripts and internal notes, with structured metadata so queries could be scoped by issuer, sector, period or document type.
Numeric reasoning with tool use
The copilot planned multi-step answers and delegated calculations, comparisons and model runs to deterministic tools, keeping the LLM focused on interpretation rather than arithmetic.
Grounded, cited responses
Every insight returned to the analyst came with inline citations and a traceable evidence trail back to the source documents and data points.
Analyst-native interface
The copilot lived where analysts already worked — inside their research and modelling tools — surfacing recommendations in-line rather than forcing a context switch to a standalone chat.
Technology Stack
The solution was engineered with a carefully chosen set of tools and frameworks, balancing maturity, performance and fit to the problem domain.
Results & Impact
Hours of research prep collapsed
into minutes, especially for recurring workflows like earnings reviews and sector scans.
Faster, more confident decisions
because analysts could interrogate data in natural language and get source-linked answers.
Better coverage per analyst
as the copilot absorbed the repetitive parts of preparation, letting the team track more names and themes.
Full auditability
with every query, retrieval and answer logged for compliance review and post-hoc analysis.
Conclusion
A useful financial copilot is not one that sounds smart — it is one an analyst will actually trust with a client call. By coupling LLM reasoning with deterministic tools and strong citation discipline, the copilot became a force multiplier for the research desk while remaining transparent about exactly how each answer was produced.