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Table of Contents
What We’re Working On: The Rise of the Data Retrieval Agent
By now, most organizations have experimented with some version of an internal AI chatbot. Ask it a question, get an answer. The problem? Most of these tools are disconnected from the actual data that runs the business—and more importantly, they don’t know the business. They don’t know what your organization means by “active customer.” They don’t know how your team calculates project margin, or which KPIs your leadership actually cares about on Monday morning.
That gap is exactly what dbSeer has been thinking carefully about—and it’s why we frame this differently: not internal bots, but data retrieval agents. The distinction matters more than it might seem.
What OpenAI Built—and What It Signals
In January 2026, OpenAI published a rare behind-the-scenes look at its own internal data agent—a custom AI system built to help employees go from question to insight in minutes, not days. The agent reasons across hundreds of petabytes of data and tens of thousands of datasets using natural language. It lives where OpenAI employees already work.
What makes this system work isn’t the model—it’s the infrastructure underneath. The agent pulls enriched, contextualized metadata via retrieval-augmented generation (RAG) rather than scanning raw logs. It inherits and enforces existing access controls. It maintains a self-improving memory of corrections over time. In other words, the AI is only as capable as the data foundation that supports it.
This is exactly what dbSeer has been building toward with our clients.
Repositioning the Conversation: From “Bot” to “Agent”
When organizations think about internal AI tools as chatbots, they tend to underinvest in their foundation. The focus lands on the interface—how the tool looks and responds—rather than on whether the underlying data is clean, connected, and trustworthy.
A data retrieval agent is a different beast entirely. Think of it as a mini-me version of your most knowledgeable analyst—one who already knows your systems, speaks your business’s language, understands your definitions and KPIs, and has absorbed the logic of your specific domain. It doesn’t surface generic answers; it surfaces your answers, grounded in how your organization actually works.
That domain fluency is the differentiator. A well-built data retrieval agent doesn’t just answer questions on a surface level—it navigates your business data with the same contextual awareness a seasoned internal expert would bring. Your finance team can ask about revenue recognition without waiting for a manual report. Your operations leaders can surface project risks without a SQL query. Your executives can interrogate business performance in natural language and trust the answer—because the agent already knows what that answer should look like for your business.
But here’s the catch: that mini-me is only as sharp as the data it has access to. Fragmented systems, inconsistent definitions, and siloed storage don’t just slow down reporting—they make a data retrieval agent unreliable, which is often worse than having no agent at all.
The Smart Foundations Connection
This is where dbSeer’s approach comes in. Before any AI can reason over your data, that data needs to be unified, governed, and accessible. We help clients build the architecture—lakehouses, data pipelines, metadata layers, access controls—that transforms scattered operational data into a coherent, queryable foundation.
Once that foundation exists, a data retrieval agent isn’t just possible. It becomes genuinely powerful—and genuinely yours.
What We’re Brainstorming: Making Sense of Marketing Spend with Mixed Marketing Models
For any growth-oriented business, the fundamental marketing challenge is easy to state and fiendishly difficult to solve: you’re spending money across many channels, and you need to know which ones are actually working. The good news is that the field has two powerful tools for answering that question. The important thing to understand upfront: they work best together, not in place of each other.
Attribution traces individual leads back to the specific ad or campaign that generated them. It’s your tactical layer—week-to-week, campaign-to-campaign decisions. But it’s an incomplete picture on its own. A prospect might first encounter your brand through a YouTube ad, then a Facebook post days later, then finally convert through a Google local services ad. Which channel gets the credit? Even the best-instrumented organizations acknowledge they’re making educated guesses—and in categories with long decision windows, single-touch attribution misses most of the story.
That’s where Mixed Marketing Models (MMM) come in—not as a replacement for attribution, but as its strategic counterpart. Instead of tracing individual leads, MMM analyzes aggregate patterns over time, looking at how variations in weekly spend across channels correlate with actual revenue and lead outcomes. Run the model over a year or two of data, and you can calculate return on ad spend by channel, identify diminishing returns thresholds, and make confident decisions about next quarter’s budget.
The two approaches are designed to work in tandem:
- Attribution is your tactical tool: real-time, granular, campaign-level.
- MMM is your strategic tool: long-term portfolio optimization and budget planning.
Together, they give you both the speed to react and the confidence to plan.
The catch? Both depend on something most organizations underestimate: clean, connected data—built on defined business processes.
You can’t run a reliable attribution model if your CRM, web analytics, and call tracking systems aren’t integrated. You can’t build a trustworthy MMM if your historical spend and revenue data lives in disconnected spreadsheets with inconsistent definitions. And critically, no amount of technology solves this if the underlying workflows haven’t been standardized first.
This is exactly what we’re exploring in our upcoming White Paper, which we’ll be sharing with clients this spring. We argue that before your business asks, “What new system should we adopt?” the better question is “How do we make what we already have work together?”
Stay tuned.
What We’ve Accomplished: ATCS—Ending Dueling Data, Building a Foundation for What’s Next
ATCS had a term for what was holding them back: dueling data.
“Dueling data is not just bad because we’ve got multiple entries for the same information. It affects our ability to strategize and streamline ownership of data.”
— Tamir Sherif, Executive Vice President, ATCS
For a 30-year-old engineering and construction firm managing complex, multi-phase projects with compliance requirements and long-term documentation mandates, this wasn’t just an inconvenience—it was a strategic liability. Financial data lived in one system, payroll in another, construction site media technically preserved but difficult to access.
dbSeer designed and implemented a unified data architecture within ATCS’s environment that changed that. Today, ATCS operates from a single source of truth—one version of their data, consistently defined, accessible to the right people at the right time. Department leaders can pull their own numbers without waiting for someone to reconcile them. Executives have a complete view of organizational performance. And the manual Excel consolidation that once defined the reporting cycle has been replaced by live dashboards that reflect what’s happening across the business today.
Equally important is what this foundation enables going forward. By establishing clear data governance—documented definitions, consistent business logic, controlled access—ATCS is now positioned to apply Gen AI to their data in a way that will deliver trustworthy results. This is what dbSeer means by smart foundations: doing the work today that makes tomorrow’s capabilities possible.
Read our upcoming detailed Case Study on our website.
dbSeer’s Q1 2026 Milestones
AWS Generative AI Competency
We’re proud to share that dbSeer has achieved the AWS Generative AI Competency—a designation that recognizes AWS Partners with demonstrated technical proficiency and proven customer success in delivering production-ready generative AI solutions.
The context for why this matters: 80% of organizations report failing to see results from their AI investments. The gap between AI ambition and AI reality has never been wider. This competency positions dbSeer as a partner that bridges that gap.
Our Gen AI work spans both vertical-specific applications—healthcare documentation, franchise performance analytics, customer experience optimization—and horizontal capabilities that benefit organizations across industries: knowledge management, intelligent automation, and back-office AI integration.
Read the full announcement here.
Gen AI Roundtable: AI as an Amplifier, Not a Replacement
Last month, dbSeer brought together business leaders for a candid conversation about what Gen AI actually means for how we work. Our speakers brought real-world perspectives that moved the discussion well beyond the hype. One theme kept surfacing throughout the evening: AI as an amplifier, not a replacement.
When Gen AI is deployed correctly—with the right foundation and the right people—it becomes a genuine force multiplier. But without that foundation, AI generates noise instead of insight, and projects stall before they ever reach production.
The difference between those two outcomes is rarely the technology. It’s groundwork.
These conversations are exactly why dbSeer champions the way we work. The technology is only as valuable as the infrastructure underneath it—and the culture built around it. We’re grateful to everyone who joined and made the dialogue so sharp.
Ready to turn your data challenges into competitive advantages?
Schedule a consultation at dbseer.com or connect with us on LinkedIn. Let’s make data drive your success.
