Our partnership with OpenAI is an important milestone for Evalueserve, but the announcement itself is only the starting point. The next step is putting that technology to work in practical ways that solve real business problems and deliver measurable results.
That takes more than access to a powerful platform. It requires teams that understand the technology, domain experts who know how work actually gets done, and delivery experience to build, deploy, and manage AI in complex enterprise environments.
Evalueserve has been investing across each of these areas.
From Certification to Application
Evalueserve is expanding its OpenAI expertise through certification and hands-on application. Today, we have 274 OpenAI PartnerU badges, with more employees continuing to develop their skills across the company.
That learning is being put into practice through a company-wide innovation initiative focused on solving real business challenges with AI. Employees have built agents on ChatGPT for patent research and patentability assessment, market and central-bank intelligence monitoring, document analysis, proposal creation, lead qualification, and data migration.
Building these agents gives our teams firsthand experience defining the right use case, grounding outputs in relevant information, and keeping expert judgment in the workflow. Those lessons strengthen how we design and deliver AI solutions for clients.
Applying OpenAI to Financial Services Workflows
Several of our financial services solutions already demonstrate what this approach looks like in practice.
Accelerating Early-Stage Credit Reviews
For a global development bank, Evalueserve developed and deployed a live financial spreading agent that supports quick-pass credit reviews.
Rather than attempting to automate the entire underwriting process, the agent focuses on the specific financial data points a credit officer needs to determine whether an opportunity warrants deeper analysis. OpenAI models process and structure the relevant financial information, which is then used to calculate borrower credit ratios.
This deliberately focused design reduces unnecessary processing, improves response time, and optimizes token usage and operating costs.
The solution also illustrates why deploying an agent is only the first milestone. When changes to the underlying models affected output behavior, Evalueserve's monitoring processes and credit specialists identified the variation before it disrupted users. The team recalibrated the solution and transitioned it to an alternative model within the existing architecture while the workflow continued operating.
For the client's credit officers, nothing changed. Behind the scenes, continuous lifecycle management helped preserve output quality and business continuity.
Making Enterprise Research Conversational
Evalueserve's Research Bot uses OpenAI models alongside expert-curated, domain-specific information from platforms such as Insightsfirst.
Instead of relying solely on predefined searches or taxonomies, users can ask questions conversationally, receive concise answers, explore supporting sources, and refine their questions as they work. This helps research and competitive intelligence teams move more quickly from a broad information set to the insights most relevant to a decision.
Research Bot has also been integrated with Spreadsmart as part of Evalueserve's Credit Memo Automation capabilities. Within a lending workflow, analysts can use it to retrieve borrower information, investigate trends, and ask follow-up questions beyond the content generated for the memo itself.
Modernizing Credit Memo Preparation
Evalueserve has developed agentic pipelines to support credit memo creation, a process that can require analysts to assemble and interpret information from financial statements, filings, research reports, presentations, and internal systems.
Agents powered by OpenAI models can support financial data extraction, narrative generation, validation, formatting, and reference tracing. Structured information can flow from Spreadsmart or another internal system, while unstructured documents provide the context required for financial and qualitative analysis.
The result is not an autonomous credit decision. It is an auditable first draft that analysts can review, edit, and trace back to its sources. In one global banking engagement, this approach reduced the manual effort required for first-draft creation by 30-40% while improving scalability during peak periods.
Modernization and Credit Memo Automation pipelines within Spreadsmart are currently being developed with OpenAI models as the default models, while maintaining a model-agnostic architecture that can accommodate client requirements.
Strengthening Disclosure Monitoring
For a top global investment bank, Evalueserve developed an AI-enabled automation solution to streamline compliance and disclosure reviews across approximately 2,400 equity and fixed-income research reports.
The solution uses OpenAI models to extract disclosures and additional information from research PDFs, compare the results with the bank’s research portal, and highlight discrepancies in a structured dashboard. It also supports proactive compliance checks by tracking dispersion-chart data and flagging material variances for review.
By automating activities that previously required teams to validate thousands of web links, disclosures, and chart data points manually, the solution delivered a 90–95% efficiency gain in the disclosure validation and reporting process. Structured dashboards and historical tracking also improved accuracy, auditability, and the ability to identify issues before publication.
Supporting Cyber Insurance Underwriting
Evalueserve has also incorporated OpenAI models into a custom underwriting solution developed for a cyber insurance client.
The application brings together internal and external data, risk models, underwriting parameters, and generative AI-supported document analysis. Tasks that previously required days of manual research can now be completed in less than five minutes.
The solution gives underwriters a consolidated view of the company being assessed and generates suggestions based on information extracted from relevant documents. Underwriters remain responsible for validating or overriding the outputs, keeping the final decision in the hands of experienced professionals.
What These Use Cases Have in Common
These solutions address different business problems, but they reflect a consistent approach:
- Start with a specific workflow and measurable business need.
- Combine OpenAI's models with relevant enterprise data and domain knowledge.
- Give users access to sources and preserve human judgment.
- Build governance, testing, and monitoring into the solution.
- Design for continuing model and business-process change.
This is what it takes to move AI from an impressive demonstration into day-to-day enterprise work.
Our partnership with OpenAI gives us an opportunity to extend this approach to more clients and more workflows. By combining OpenAI's technology with Evalueserve's domain expertise, AI engineering, analytics, and managed services, we can help organizations identify the right opportunities, deploy them responsibly, and continuously improve the value they deliver.
The announcement was the starting point. The work, and the measurable impact it can create, is what comes next.
Ready to identify where OpenAI can create measurable value in your workflows?
