Let Us Build the Core, You Build the Unique Value: Winning with Enterprise AI

Recap of Our Webinar: “Buy vs. Build Enterprise AI for Financial Services” with Experts Cyrus Daruwala David Hannibal and Dorian Selz - Watch the replay directly here.

At Squirro, we’ve witnessed the transformative impact of generative AI on large banks and asset managers. We've seen off-the-charts results with our customers, including a 200% increase in employee productivity and a 96% reduction in the time required to complete complex tasks. During our last webinar, our experts shared how these results were achieved and what the key was to unlocking this potential.

Straight to the point, the key can be summarized here: By partnering with an enterprise AI provider like us to orchestrate the core stack, businesses can focus on building their unique value, AI-powered and tailored to their customers' needs. This approach delivers two key benefits:

1. Higher Return on Investment: Solutions are more aligned with specific company and customer needs.
2. Longevity: Creating unique value that differentiates your company.

A concrete example: For one client, we realized a 64% reduction in cost per support ticket. Achieving reductions in time and/or costs of up to 50% creates a game-changing moment—not just in cost savings but in fundamentally rethinking business processes.

This transformation goes beyond simple chat solutions. When executed well, enterprise AI allows companies to reimagine their entire business processes holistically, focusing on what really matters: enhancing customer value and optimizing the cost-service equation.

However, such transformative results are only achievable with the right core technology stack.

The Critical Components of a Successful AI Stack

Many companies are currently conducting experiments, pilots, or proofs of value (PoVs). Some may even have production runs with a retrieval-augmented generation (RAG) system.

Simply put, a RAG system combines a search component and a large language model (LLM) to generate answers within the context of a company. Here’s the catch: both search and LLMs are probabilistic. When combined, they don't automatically yield certainty.

To achieve the accuracy levels required for enterprise use cases, reference data and GraphRAG elements are essential.

Only with these components can businesses reach accuracy rates as high as 99%, the number needed at an enterprise level for financial services, manufacturing, and many other industries.

Build vs. Buy: Lessons from the Past

While building a platform from scratch might sound appealing, it often leads to prolonged timelines and increased costs. HSBC’s experience highlights this—they spent years building in-house solutions due to the lack of available tools. Today, the situation has changed—off-the-shelf solutions are available, offering a faster, more cost-effective path to innovation. Ready to Build? Take our self-assessment for in-house GenAI development

How We Orchestrate the Core and Complex Stack

McKinsey, in their report “Moving Past Gen AI’s Honeymoon Phase: Seven Hard Truths for CIOs to Get from Pilot to Scale,” emphasized the importance of orchestration: “It’s about how the pieces fit together, not the pieces themselves.”

Let us explain the Squirro value proposition, based on the McKinsey framework:

1. Front-End Application: Integration into enterprise applications like Salesforce and Jira, as well as providing our own UI and dashboards.
2. Orchestration: The Squirro platform comprehensively covers orchestration, including access controls, guardrails, prompt enhancement, agents, and other fundamental elements.
3. Data ETL: Squirro’s advanced "Extract, Transform, Load" pipeline, known as the "Gather, Understand, Act" framework, includes over 100 out-of-the-box connectors.
4. Databases (e.g., vector stores): Squirro's semantic and hybrid search is powered by a vector index on Elasticsearch, tested to scale fully permission-enabled for millions of documents and TBs of data.
5. API Gateway: Squirro offers the flexibility to be LLM-agnostic, allowing other AI apps to be built on this API.

As you can see, building a secure, privacy-first, and permission-enabled Enterprise GenAI platform that delivers accuracy and quality at scale is no easy feat.

Our experts concluded that this co-creation approach lets you go to market faster (without having to build what’s already been built) with a better Total Cost of Ownership (TCO), as the cost of the core platform is shared, allowing your corporate investment to focus entirely on differentiation.

More on the topic here

Explore all our resources on the Build vs. Buy page. Weight the critical factors to consider in our quick self-assessment, or discover in our latest guide why buying accelerates results.

Recognized by Gartner as a visionary company, Squirro stands at the forefront as an enterprise-ready generative AI solution for search, insights, and automation. Our clientele includes prestigious organizations such as:? the European Central Bank, the Bank of England, Henkel, Mubadala.

Thank you for being part of our journey. Stay tuned for more updates as we continue to bridge the AI reality gap!