Partnering with a client to deliver a M&A insights app

Our client’s M&A client team needed to accelerate the production of sector and company insights at the start of engagements with parties involved in mergers and acquisitions. However, their data landscape was fragmented and slow which presented a major challenge. Critical information was scattered across proprietary systems and licensed sources, such as in application programming interfaces (APIs), files, and databases. This spanned hundreds of data tables and gigabytes of documents, with uneven API performance. 

Much of the content was numerical financial metrics. This was potentially valuable, but not directly usable by large language models without careful restructuring and context. This made AI-assisted analysis unreliable. Our client also faced a tight deadline, leaving little margin for rework. Our Team was engaged from the outset and immediately prioritised a focused discovery phase. 

Faced with an immovable deadline, we focused on rapid, iterative delivery with clear communication and quick decisions. Mapping proprietary and third‑party sources and setting up a shared taxonomy for sectors, companies, and key metrics allowed us to connect APIs, files, and databases. This approach also meant we could standardise definitions and make numerical data usable for the AI assistant, with basic controls for freshness and reliability.

The main hurdles were data volume/variety and uneven external API performance. We addressed these with frequent stakeholder touchpoints, and ‘show‑and‑tell’ demos. These prioritised the highest‑value sources and used smart data handling to keep responses fast and consistent, while avoiding over‑engineering. 

Collaboration was key to this rapid delivery. Working directly with client subject matter experts (SMEs) resolved ambiguities. Also, the team’s seniority allowed us to quickly address bottlenecks and make pragmatic tradeoffs. 

We delivered a business‑friendly interface with an AI assistant that answers natural‑language questions, provides transparent sourcing, and reliable sector and company insights. This allows our client to ship products in time and lays a scalable foundation for future sources and features. 

The app is now used by several M&A practice teams to kickstart engagements insights and metrics. It is now actively used by practice teams to accelerate kickoffs, reduce analyst ramp‑up time, and standardise insights. 

The app has now evolved into an enterprise M&A data hub that integrates multiple data sources to provide a governed, scalable ‘single source of truth’ for intelligence to inform deals. 

Building reusable AI agents and services that are consumed by other products has extended the platform’s impact across our client’s broader M&A and research ecosystem. Our client has also achieved fast, reliable performance with predictable response times despite variable third‑party APIs, improving trust and day‑to‑day usability. 

A key takeaway from this project to inform future engagements include that initial investment should be concentrated in a focused discovery phase. This should combine senior talent, SME support, and clear communication to deliver quickly while maintaining high quality.