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Building an AI-Powered Finance Productivity Platform

From Market Gap to Full-Stack Product
End-to-End Design, Development & AI Integration
Region: United Kingdom
CLIENT
YASH
Sector
Information Technology
Scope
Full-stack product partnership
AI platform architecture, design and delivery
services
AI Solutions
UI/UX, Web App Development

Contents

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00
01

The challenge

A UK-based fintech innovator had identified a structural gap in how finance professionals work. The industry runs on information, but collecting it, structuring it and presenting it still takes enormous manual effort, with analysts spending hours sourcing data, formatting slides, reconciling figures and hunting for documents across Excel models, PowerPoint decks, dense PDF research and fragmented email threads. The vision was a single intelligent platform — an AI co-pilot for research, a smart deal document hub and an automated slide-building engine — and the work was turning that market insight into a shippable product.

What had to be solved

  1. A conversational AI that understands financial language and can interact with the user’s own documents, not a generic chatbot.
  2. A multi-module platform where AI is native to every workflow rather than bolted on afterwards.
  3. LLM integrations capable of handling proprietary financial documents through a retrieval-augmented generation pipeline.
  4. Slide-building automation, the most time-intensive craft in an analyst’s day.
  5. Enterprise-grade security, for a platform holding sensitive client financial data.
02

What we did

Deliverydevs came in not as a development house but as an embedded technical and product partner, deploying a cross-functional squad directly into the client’s product workflow across product design, frontend and backend engineering, AI and ML integration, DevOps, QA and cybersecurity. Before a line of production code was written the team ran a structured discovery and proof-of-concept phase to validate the architecture, confirm the AI integration approach and settle the tech stack, so no expensive rework was waiting downstream. Every architectural decision was treated as a product decision, made for the scale of the data and the scale of a growing professional user base at the same time.

The co-pilot was never built to replace the analyst, only to take back the hours that do not need one.

On the build
03

The toolkit

Four areas carried the platform, each against a named part of the problem.

Platform architecture and system design

An end-to-end architecture for a modular, AI-native platform, structured around three core beta modules with clear API boundaries so each can scale independently and new modules can be added without unpicking the others.

LLM integration and financial intelligence

A full retrieval-augmented generation pipeline built with LangChain, connecting the LLM to the documents held in the platform’s own document management module and to knowledge articles written by the client, with GPT-4 as the primary engine and the architecture in place to plug in finance-tuned models such as Palmyra Fin for domain accuracy.

Intelligent document management

Metadata tagging across file type, geography, deal type, date range and content classification, so a team’s models, decks, research reports and term sheets can be filtered with precision and any document surfaced directly into a conversation.

AI-powered presentation automation

The most technically ambitious module, built as a Microsoft PowerPoint add-in on the Office.js SDK so users work inside the application they already use. A backup generator extracts data from raw Excel and PDF sources with Pandas and PyPDF2 and constructs the slides programmatically; a template generator recommends layouts dynamically from the slide’s intent rather than offering a static library; and a tick-and-tie assistant reviews a finished deck for internal inconsistencies, flagging figures referenced differently across slides or values that do not reconcile.
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04

Results

Through end-to-end ownership of design, engineering and infrastructure the client got a fully designed and developed beta platform on an accelerated timeline, with a financially-aware AI behind presentation automation, a document hub built for contextual retrieval, and an AWS deployment with a fully automated CI/CD pipeline for rapid, low-risk releases. Security was never a post-launch consideration: a cybersecurity specialist was embedded from day one, with encryption applied to data at rest and in transit. The presentation automation module alone had the potential to recover hours of analyst time per deck, which is the benchmark it was designed against.
05

Conclusion

Building an AI-native product for financial professionals is an order of magnitude harder than building a general-purpose tool: the data is sensitive, the workflows are precise, the users are demanding and the stakes are real. It takes product thinking that starts from the user’s workflow rather than the technology, AI integration that goes past API calls into real RAG pipelines and fine-tuning readiness, and full-stack ownership across frontend, backend, DevOps and security. The platform stands as proof that when the right technical partner is embedded at the right moment, a vision becomes a product.
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