Category: Axys


Earlier this year, when software stocks were taking a beating related to the heights of AI enthusiasm, a client reached out to me for my opinion on whether AI might be used by domain experts, such as myself, to create competing products capable of supplanting established software firms like Advent and others at a fraction of the price due to a lower cost base.

An abstract representation of a cosmic explosion, with rays of light emanating from a bright central core against a dark starry background.

At the time, I acknowledged that it was possible, but more likely to happen at smaller firms. Investment professionals are stereotypically risk-averse in their business practices and the production software they utilize to run their businesses. While the largest investment firms are better suited to tolerating risk from a resource standpoint, they are also the least likely to take on what they might consider unnecessary risk. As a result, larger firms as a whole are slower to replace mainstream products with upstart offerings that have an unproven track record.

However, a more likely possibility has emerged: firms currently running vendor software—motivated by the costs and limitations of those products—may simply create their own systems using AI to aid them in the process. I have already seen signs that some firms are doing this and feel comfortable doing so. I am not necessarily recommending it, but I understand the motivation.

If you are paying substantial fees for systems you believe you could replace, why wouldn’t you? The key here is understanding what these systems actually do. A vendor with a forty-year track record has built a product that almost certainly handles details that an AI-assisted or ‘vibe-coding’ alternative (with a relatively short window of development and testing) does not; however, that may not matter much if you are only looking for 60% to 80% of the functionality of the product you are replacing.

When I compare products in the investment space, there are clear differentiators between older-class solutions and newer-class solutions. Those differentiators once would have represented a significant hurdle for a competing product to match, but AI makes it much easier.

As an example, consider the HTML attachment below, which summarizes hypothetical investments for a fictitious company and client in a format typical of newer-class solutions. I used Claude to help me produce this last month. This is something I could have done previously if a prospective client had asked me to, but I was never tempted to do so because it would have been tedious and time-consuming to perform manually. With the assistance of frontier AI models, I was able to achieve the following result, which can easily be populated from portfolio accounting data within Axys or APX. Please note that all reporting exhibits in this blog were produced using Anthropic, OpenAI, and Perplexity models and based on fictional data; disclosures on each exhibit also restate this.

The prompt was the result of a test I performed one afternoon where I asked the best-known publicly available frontier models in the cloud, as well as the best local models I can run on my Alienware Area-51 desktop, to create what I consider to be a decent report package by today’s standards. I would love to take more credit for it, but I cannot; I simply asked Claude to do it and it was done. Perplexity also did a respectable job, though it fell slightly behind Claude’s effort, while ChatGPT’s report was clean but relatively simplistic.

The local models didn’t put up much of a fight, although I may be able to improve their performance by using frontier models to design the prompts for them. It would be nice to rerun these tests using GLM-5.3 Q4 or GLM-5.3-Flash on a Mac Studio with an M5 Ultra chip and 512GB of unified memory, but I don’t have one of those yet. Naturally, if you write better prompts than I did or possess superior hardware, your results may vary.

Claude didn’t just create a pretty mockup of data in HTML like ChatGPT and Perplexity; it created JSON blocks within the HTML that I can populate programmatically from data feeds I have already created using xPort with Axys and APX. If I wanted to build an alternative system, I would be well on my way with this type of reporting right out of the gate.

Here is what I produced with Perplexity’s assistance.

Here is what I produced using ChatGPT.

The pros and cons of building versus buying remain. AI can potentially help you maintain the code, but will your staff truly understand everything they are using? Probably not. And what will it cost to maintain that code in the future? If the system is predominantly AI-coded and not fully understood by those at your firm, that cost will be determined by the “AI gods” in power at the time.

Can a true domain expert with deep knowledge of specific software utilize local and/or frontier models to build a competing system in a fraction of the time and cost required years ago? Yes. Absolutely—and every day, it is becoming more feasible.


About the Author: Kevin Shea is the Founder and Principal Consultant of Quartare; Quartare provides a wide variety of agile technology solutions to investors and the financial services community at large.

For details, please visit Quartare.com, contact Kevin Shea via phone at 617-720-3400 x202 or e-mail at kshea@quartare.com.

As a technology consultant specializing in automation and integration of all things Advent, I have helped hundreds of firms make the financial reports and statements they envision a reality. In the past, those projects may have been slowed to some degree by the more obvious technical challenges: creating the mockups, subsequent report development, and testing/validation, but those aren’t the only things that make revamping statements time-consuming.

This statement mockup was created in about five minutes using Claude. No data provided here is real.

It is also what occurs within an organization as a precursor to any possible statement redesign and in tandem with an ongoing statement redesign that takes time. Changing statements requires that principals buy into the idea and believe that changing the statements, which very likely have been in use for many years, is a priority. Statement redesign doesn’t happen in a vacuum. Along the way, a lot can happen. These types of projects can be put on hold, as firms planning to update their reports may also be considering changing to another platform that might offer better reporting as a feature.

There are several factors that go into choosing a new platform, and I wouldn’t make a generalized recommendation related to that. However, if moving to a new platform is primarily about improved reporting, it is prudent to see what you can do with your existing platform before you change it.

Historical Mockups

Over the years, I have been provided with an assortment of exhibits cut out from other firms’ reports, sketched approximations of what the reports should look like, and entire report packages from other firms with a request to reproduce them. In the past, mocking up reports was something that could take hours or days. If a client didn’t already have something very specific in mind, I would provide them with report samples to review. Working from those samples as general ideas of what we could do, clients would create mockups of the reports they wanted.

Today, creating your next-generation report packages can be quicker than ever thanks to a wide variety of AI tools that are well-suited to the task. In some cases, you can create presentation-quality mockups in minutes. AI should not be used as a substitute for creative human thought and ingenuity, but working with AI tools undoubtedly speeds the process of taking your ideas and transforming them into a presentation-quality visualization that can be shared with your peers.

When done with the proper approach, tools, and infrastructure in place, you can go from having mockups to having live reports you can use in a fraction of the time it would have taken years ago, and you don’t need to change your PMS platform from Axys or APX to get these types of reports.

Data and Reporting Processes

The key is having the underlying data from Axys or APX in a modern data warehouse and access to qualified tech resources to update any additional queries and data feeds you may need for the reports you want to design. That infrastructure, along with workflows that leverage it, empowers you to own your data and reporting processes. The image below illustrates my chosen approach to providing high-end investment reporting solutions to investment professionals for many years.

Next Gen Reports

While that fundamental approach hasn’t changed, the way we create reporting solutions has transformed with improvements in the technology available to us. Traditional report-writing tools could certainly be used to develop reports that are first produced as mockups, but it is no longer necessary. Instead, we can simply prompt the AI tool of our choice to create a program to populate the reports.

So long as you have the Advent data necessary to produce the reports in an acceptable database format, you should be able to populate the new reports almost immediately. If you don’t have the necessary Advent data in a data warehouse yet, that process has also never been easier than it is today. Advent subject matter experts like myself should expedite that process significantly and ensure that the data extracted is correct, but once again, AI tools are making ETL and data warehouse projects much easier than they have been in the past.

That said, pulling the data en masse is a larger, more complex project that, for now, may still be best left to those with specific expertise doing it. A data warehouse isn’t just a bunch of tables. The structure of the tables, queries, and relationships between them facilitates your ability to create additional queries and reports more efficiently.

Reports like the Portfolio Statement included in this blog are just a sample of what is possible. If you can imagine it and create a detailed system prompt describing what you want, AI tools like Claude can help you make it a reality.

There really is no time like the present to update your client statements.


About the Author: Kevin Shea is the Founder and Principal Consultant of Quartare; Quartare provides a wide variety of agile technology solutions to investors and the financial services community at large.

For details, please visit Quartare.com, contact Kevin Shea via phone at 617-720-3400 x202 or e-mail at kshea@quartare.com.

I have been creating useful content for Advent users and the financial services firms they work with for many years now.  Part of creating the blog is therapeutic for me, part of it is a well-intentioned effort to foster goodwill by sharing the lessons I have learned with other users to reduce pain points, and some part of it is an effort to get the word out about what I do in case users find that they need someone with my unique skillset.

A watercolor painting of a pensive sculpture with a hand on its chin, surrounded by a colorful abstract background.

When ChatGPT and similar services arrived on the scene, I was somewhat concerned about what would happen if those engines sucked up the knowledgebase I have created and presented it to my audience as their own information.  There are documented ways to discourage AI bots like robots.txt, Cloudflare AI Crawl, WAF blocking and CMS-specific settings.  I know this, but early on I made a choice to allow it, rather than fight it.

Initially, I remained most worried that my knowledge would be presented without credit to where it came from, but this past week I realized that there is another issue altogether.  In the process of troubleshooting an Advent Software use case, I queried Perplexity.  I was rewarded with a page of summary information that cited Advent, AdventGuru and 13 other sources.  Some of those sources were relevant, most were not.

As I drilled down on the problem, most of the sources cited were in fact me.  So here I was querying Perplexity for assistance, and it was attempting to assist me in troubleshooting the issue using information I provided.  Some part of this makes sense and could be helpful if I were losing my faculties or wanted to query my own digital footprint related to the issue.  Neither of these apply.

I wound up resolving the issue with the user and their IT consultant in less than an hour with no part of the credit for doing it attributable to using Perplexity with its “best” model. Our solution was collaborative.  The client arranged a Teams meeting with me, and their IT consultant.  The three of us worked together to try a few things.  Eventually, we found a solution as a result of us all working together – not an AI query.  We solved it because we found the time to have a meeting and made that a priority.

In the process of writing this, I ran the same query on Opus 4.6 and GPT-5.4.  The results were very similar. My blog and other online sources of current and historical information are the data that empowers these engines to respond to practical and esoteric questions with anything relevant beyond their training data.  However, as I read though their responses to the query, it became clear that I was heavily cited without any solution being provided to the problem.

While I am flattered that my subject matter expertise is held in such high esteem by AI inference engines, I am concerned that when AI models attempting to utilize what I have written – citing me repeatedly throughout their response as a source of information – fail to provide a solution that reflects poorly on me.  In the particular case we resolved, I am almost 100% certain that a solution does not currently exist online.  I have already written a separate blog post detailing the problem and its solution, but now the question for me is, do I put that solution up on my blog? 

By doing so, I continue to provide access to users that have relied on me as a source of information that may not otherwise be discovered, documented or publicly available, but I am also empowering AI inference to parody my expertise in more meaningful ways that may make users think their favorite AI chatbot is a substitute for getting knowledge directly from the source. The latter is problematic because written works contain meaning and nuance that are lost when information is taken selectively and presented out of context.

Chatbots cannot be trusted to provide the best possible answer – only the best possible answer based on their training, parameters, capabilities, available data, and the prompts we use to query them.  My blog posts are representative breadcrumbs of my experience that I have chosen to share.  In this case, the chatbots reviewed my blogs to determine if and how something can be done, asserted that it could not be done, and then provided instructions on possible workarounds, but there was a solution that could be found without the assistance of a chatbot all along and it makes a lot more sense than the workarounds that were recommended by Perplexity and its cohorts.


Kevin Shea Impact 2010

About the Author: Kevin Shea is the Founder and Principal Consultant of Quartare; Quartare provides a wide variety of technology solutions to investment advisors nationwide.

For details, please visit Quartare.com, contact Kevin Shea via phone at 617-720-3400 x202

Over the years I have published many blogs.  Almost none of them are as frequently visited as Getting Data In and Out of Advent APX and Axys. That is a good indicator that the topic remains relevant, but a long time has passed since it was published. If you have a recent version of APX today, there is another option that was not available back then – RESTful API.   Though I knew the functionality existed within more recent versions of APX, I hadn’t had the chance to implement it with a client yet.

Abstract illustration of interconnected blue and orange lines resembling data flow or network connections on a dark background.

Last year, an APX user approached me, seeking to utilize Advent Software’s API to create data pipelines between APX and their in-house MS SQL Server Data Warehouse (DW).  The APX user identified this work as a prerequisite for them to fully migrate from Axys to APX.  They wanted to maintain the high-level integration they had created between their DW and Axys in APX.

I was somewhat concerned because I had researched previous APX versions related to the API and knew of some of the issues early adopters had encountered. Given that information, I had some trepidation about obligating myself to a project that required implementing the API. At the time I was not convinced, via first-hand experience, that the API would work reliably for their planned application of it.

I tempered the prospective client’s expectations and proposed a flat-rate job focused on determining the feasibility of doing APX API integration in their environment. Our end goal was to develop a couple of data pipelines between the DW and APX, with the caveat that those deliverables, developed in Python and/or JavaScript, would be proof-of-concept work and not necessarily ready for production use.

Together we successfully completed the project in an APX v21.x environment self-hosted by the client. The majority of the work was done over the course of a couple of months and the deliverables were ready to be moved into production almost immediately afterwards, but there were some challenges along the way.  In most of the instances detailed below, we looped Advent in for assistance, and they did a commendable job helping us resolve the issues promptly.

  • Error 403 – Initially, we were getting an error when attempting to use the API.  We reached out to Advent, and they noted that the most recent Cumulative Hot Fixes (CHF) update wasn’t applied and recommended that we install it.  Applying the CHF update resolved the error, and the API worked as expected.

  • Postman functionality – There were a couple of days where Postman was completely unresponsive.  During that brief period, we had difficulty doing even the most basic API testing.  This issue seemed to resolve itself, but we may also have logged out of Postman and logged back in.

  • Error 500 writing data to APX – During dev the functionality to read APX data was working very well, but we found that attempting to write data to APX generated an Internal Server Error.  I assumed that this meant that the data was not being written to APX.  After looping Advent in for another call, we discovered that although the error was being generated, the data was being successfully written to APX. Advent indicated that they would put a fix request in, but it might not happen because v21.x was sunset. With some reservations, I updated my code to ignore the error 500 when we wrote selective data to APX via the API.

If you do reach out to Advent for assistance, make sure you have Postman installed.  Advent has no desire to review your code.  They will want to test the functionality of the API with you using Postman.

Screenshot of a code editor displaying a Python script for updating data via an API. The interface includes files and folders related to API components, test logs, and a main script for API interaction.
Visual Studio screenshot of Python code sample illustrating API use.

What is Required to Get Started with the API?

Utilizing the API requires some detailed set up and work to get up to speed.  It probably won’t be something that just works without some troubleshooting, and there is a bit of a learning curve.  The following list may not cover everything you need to do to get up and running with the API, but it is a good place to start.  I wish there had been a better resource for me when I started working with APX’s REST API.

Here are some tips that should help those interested in implementing the API:

  1. Make sure you are on the latest CHF for your current version of APX.  If the latest hot fixes have not been installed, you may have problems trying to utilize the API.
  2. Download the Advent Portfolio Exchange REST APIs Postman Collection from the Advent Community website.
  3. Create a Postman account if you don’t already have one, and locally install the Postman software.
  4. Load the collection into your Postman profile and review the documentation completely.
  5. Do a search on the API in the Advent community site and read through some of the threads.  The code samples there were simple, but helpful.
  6. Create the client/credential and verify its existence via SSMS.  The client is persistent, so once you have created it, you shouldn’t have to create it again unless you update APX.  Verify the existence of the client (e.g., cc.postman) in the APX dbo.clients table.  If you have trouble creating the client using your code, try using the PowerShell script to create the client.
  7. The user profile you are using needs to have appropriate rights.  Though we escalated my individual user rights in all the documented required areas, I eventually started using the admin user profile, which worked more reliably in our environment.  I believe Advent recommends using the admin user profile if possible.
  8. Test basic APX API functionality in Postman to make sure it works before attempting to create code via C#, Python, JavaScript, et cetera that leverages the API.

Once you have completed the set up required and can use the API to read and write data to APX, you are ready to build out your solution.  If you have trouble with your implementation, validate specific functionality of the API with Postman.

Calling the API

Almost any use case of the API to write or read data from the APX requires the following steps:

  1. Get IdentityServer base address from APX authentication configuration.
  2. Get token endpoint from IdentityServer configuration.
  3. Get token with client_credentials grant type.
  4. Perform whatever API action you want (multiple calls to the API with the access_token are fine).
  5. End your API Session.  The API utilizes one of your APX seats while the session is active.

Those familiar with API use and Python are likely aware that manipulating data can necessitate working with JSON as well as Python dictionaries.  As an example, in order to read data from APX and write data from the DW into APX that is different from what is already in APX, you may need to:

  1. Query APX for the relevant data via the API, which creates a JSON file.
  2. Query the DW for the relevant data.
  3. Load the JSON data received from APX into a Python dictionary.
  4. Parse and compare the APX data from the Python dictionary with the records from the DW.
  5. Add the records that meet the criteria to the JSON payload.
  6. Send a patch request via the APX API.

The following diagram details this workflow.

Flowchart illustrating the data pipeline between Advent APX and a Data Warehouse using REST API, detailing various processing steps and data storage interactions.

To wrap up the project, I created a PowerPoint presentation summarizing and detailing what we did and how it all works to empower the internal development team to understand, troubleshoot, and replicate my work if they need to in the future. I am always available to support the solutions I create, but I prefer that my customers call me because they want to, not because they need to.

Why would you want to use the API instead of IMEX?

There are pros and cons to using the API. It presents an opportunity to use a single unified methodology to integrate data in your environment but may fall short of that depending on the specific needs of your firm.

The pros of using the API include the fact that is is a more modern approach to extracting and importing data at a granular level. The API can be used to pull data such as holdings, select time period performance, etc. In some use cases, APX users are likely extracting and transforming data that they drop into a DW. Some of those transformations, such as recalculating performance figures, may not be necessary when utilizing the API. The API has the potential to be more secure but given that the default password for admin user in APX frequently doesn’t get changed, it probably isn’t any more secure than IMEX in most self-hosted APX environments.

The cons of using the API are that some data elements may still be in flux. Reading and writing certain data points may not be possible via the API, which could force you to use IMEX or other methods (e.g., Replang, public views, stored procedures, SSRS) in addition to the API. It also may be difficult developers that aren’t Advent APX Subject Matter Experts (SMEs) to bridge this gap. Conversely, it may be difficult for those SMEs that are not developers familiar with the API use to implement it on their own.

Using well-established APX import and export methods like IMEX may still be the most efficient and reliable way to import and extract certain data elements from APX. However, going forward, the growing maturity of Advent’s REST API should force tech-savvy management, users, and integrators to ask “Should we be using the APX API to do this?” as they look to forge a modern data stack that integrates APX data, and meets AI-driven demands for more robust data access.


Kevin Shea Impact 2010

About the Author: Kevin Shea is the Founder and Principal Consultant of Quartare; Quartare provides a wide variety of agile technology solutions to investors and the financial services community at large.

To learn more, please visit Quartare.com, contact Kevin Shea via phone at 617-720-3400 x202 or e-mail at kshea@quartare.com.