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Table of Contents
What We’re Working On: Agentic AI that Works For You
Think of agentic AI as a highly capable digital employee. Like a skilled analyst who can’t deliver insights with incomplete data or disconnected systems, agentic AI requires clean, accessible, and well-structured data to perform effectively. But there’s a fundamental truth about agentic AI that often gets lost in the hype: it’s only as good as the data it can access.
dbSeer’s Proof of Concept
Consider our recent proof of concept with a financial services client whose analysts spent countless hours reviewing asset valuation documentation. Their process involved multiple review stages. The challenge wasn’t just time-consuming manual work—it was also subjective (and therefore error-prone), creating bottlenecks that delayed critical business decisions.
Our agentic AI solution served as an intelligent reviewer, automatically validating documentation, cross-checking calculations against baseline data from previous quarters, identifying anomalies in competitor comparisons, and flagging inconsistencies for human attention and additional insight. This led to faster review cycles, increased accuracy, and minimized manual workload for our client’s analysts.
Here’s the crucial change: this agentic AI needed to navigate Excel files, PDFs, and external data sources—deciding on data quality, calculation accuracy, and when to escalate. Without structured data pipelines and integrated systems, it would be unable to perform these complex tasks.
It’s certain that Agentic AI takes initiative, it navigates workflows, makes informed decisions, and coordinates actions across multiple data sources, but achieving this level of autonomy requires something critical: strong, well-designed data foundations supporting every operation. This is how dbSeer builds your agentic AI: reach out to us today with any questions.
What We’re Brainstorming: Solving the Stripe Revenue Recognition Puzzle
Imagine trying to assemble a financial report where the pieces are scattered across multiple rooms, some pieces are in different languages, and a few are missing entirely. That’s what revenue recognition feels like for many companies using Stripe. dbSeer aims to provide trusted, reconcilable revenue insights.
The Challenge: Revenue Data in Disarray
Stripe is an incredibly powerful payments platform, but it wasn’t designed to be your complete financial system of record. Companies working with Stripe typically face several painful challenges.
dbSeer has noticed a few particular challenging pain points that it hopes to solve:
- Evolving Payment Models and Calculation Complexity: Stripe has transitioned between different charging methodologies each requiring distinct calculation methods and reconciliation processes. Organizations must adapt their revenue recognition practices to accommodate these different models, creating complexity in maintaining consistent financial reporting across payment approaches.
- Payment Timing and Reconciliation Challenges: Revenue data becomes a moving target when customers pay through different mechanisms—credit cards clear immediately, while other payments can take days to process. Run a report today and again three days later, and your numbers won’t match because new payments have cleared for transactions from days prior. This timing mismatch makes reconciliation particularly difficult and creates uncertainty around real-time revenue visibility.
- Multi-Party Revenue Distribution Complexity: Stripe reconciliation becomes exponentially more complex in marketplace or platform environments where revenue must be dispersed to multiple parties. When you’re a software platform charging both a subscription fee and taking a percentage, while your customer also receives a portion and a third party (like a driver or service provider) gets their cut, tracking and reconciling these multiple revenue streams requires sophisticated data management that Stripe alone doesn’t provide.
The dbSeer Solution: Unified Revenue Intelligence
Our approach transforms this scattered puzzle into a cohesive, automated system. As a result, you gain complete revenue visibility, faster reporting, and fewer manual tasks, empowering your team to make data-driven decisions confidently.
The most important thing is building an automated data pipeline. All your revenue data lives in one place, properly structured and ready for analysis. Finance teams can trust the numbers, executives get real-time visibility, and everyone stops asking “which report is the right one?” With clean, centralized data, we deliver dashboards and reports that provide actionable insights into your business. This architecture isn’t just technically elegant—it’s practical, maintainable, and built to scale as your business grows. If Stripe revenue recognition has become a monthly headache for your team, let’s discuss how to turn that scattered puzzle into a clear picture.
What We’ve Accomplished: Telarix Performance Breakthrough
Some transformations are measured in percentages. Others are measured in multiples. Our work with Telarix falls firmly in the latter category—delivering performance improvements up to 25x faster processing times while simultaneously reducing infrastructure costs.
The Challenge: Scale Meets Legacy Infrastructure
Telarix, a telecommunications solutions provider, processes hundreds of millions of call detail records every month for telecom operators worldwide. These aren’t simple data files—they require extensive processing, including data enrichment, trend detection, insight discovery, error checking, and frequent reprocessing.
Telarix partnered with AWS, dbSeer, and Intel to evolve its application suite by refactoring the workload from SQL to Python, delivering unmatched performance and scale to operators worldwide. The solution involved transitioning from traditional and legacy on-premises servers to Amazon EC2 R5 instances optimized for memory-intensive workloads and transitioning from fixed infrastructure costs to AWS’s agile commercial model, which will deliver significant performance enhancements and unprecedented scale on demand.
The Solution: Cloud-Native Transformation
dbSeer played a critical role in the solution by combining deep AWS expertise with intimate knowledge of Telarix’s legacy systems. This unique combination allowed us to design a cloud migration strategy that delivered faster processing, reduced costs, and minimized business disruption. Our selective, high-impact approach to code refactoring ensured a smoother transition and accelerated time to value for Telarix’s operations.
The initiative focused centrally on refactoring legacy, on-premises SQL workloads into Python-based applications optimized for AWS. One of the ways in which dbSeer distinguished itself in this process was our ability to build a centralized Customer Data Hub alongside big data analytics to process trends and patterns.
By migrating to the cloud and adopting a modular architecture, Telarix significantly increased operational efficiency, reduced infrastructure costs, and positioned itself for scalable growth.
To dive deeper, access the full press release here or contact us for more details about the Telarix solution and how dbSeer can deliver similar results for your organization.
dbSeer’s AWS Milestones
We’re excited to share two significant milestones that reflect our deepening partnership with AWS and our commitment to driving innovation in the data and AI space.
Gen AI Executive Roundtable
This past quarter, dbSeer was proud to host an exclusive Gen AI Executive Roundtable featuring Sreenath Gotur, AWS Senior Gen AI Specialist, who shared powerful insights on AI and its business impact. We were also joined by our client, The Goddard School, where Dr. Ali Tafreshi offered a unique and practical perspective. The discussion was expertly moderated by our Partner, Jalpa Ramwani. This intimate gathering brought together technology leaders to discuss the real-world challenges and opportunities of implementing generative AI in enterprise environments.
The conversation moved beyond the hype to focus on what matters: understanding business readiness, building the right data foundations, and ensuring AI delivers measurable outcomes rather than just impressive demos. dbSeer understands the importance of this at the core of its work.
We’ll be hosting another invite-only roundtable discussion in mid-January in Washington, DC. If you’re interested in an invitation, please message us on LinkedIn; we’d love to hear from you!
AWS Glue Service Delivery Designation
dbSeer has achieved the AWS Glue Service Delivery designation, joining an elite group of AWS Partners recognized for proven customer success and deep technical expertise in AWS Glue. This designation validates our ability to help organizations build sophisticated data integration pipelines that form the backbone of modern analytics and AI initiatives.
AWS Glue has become central to our approach for building scalable, automated data pipelines. Whether we’re helping clients consolidate data from dozens of sources, transform complex datasets, or prepare clean data for machine learning models, Glue enables us to architect solutions that are both powerful and maintainable. If you’d like to read a few of our latest case studies around Glue, read them here and here.
These milestones aren’t just badges—they represent our proven ability to deliver transformative solutions backed by the strength of the AWS ecosystem.
Read our press release here.
Ready to turn your data challenges into competitive advantages? Schedule a consultation with us now—visit dbseer.com or connect with us on LinkedIn. Let’s make data drive your success.
