AI Agents & Intelligent Systems
AI should accelerate decisions, not add complexity.
Most companies want AI but get stuck on the same problem—their data lives in too many places. Customer records in one system, sales data in another, product information in spreadsheets, operational metrics in a third database. Duplicate entries everywhere. Inconsistent naming. No single source of truth.
When AI tries to answer business questions across these fragmented systems, it fails. It hallucinates. It contradicts itself. It says "I can't find that information" for questions that should be simple. Teams end up with proof-of-concept demos that work in controlled environments but break in production—because the real problem isn't the AI models, it's the data underneath.
How we build AI systems
We start with the problem, not the technology—What decisions do you need to make faster? What insights are buried in your data? Where are humans bottlenecked by manual research or analysis?
The Database Foundation Problem: The biggest barrier to AI isn't the models—it's fragmented data. When companies have customer data in three different systems, sales data duplicated across databases, and processes that don't talk to each other, AI can't provide accurate answers. It hallucinates, contradicts itself, or requires so much manual data gathering that it's slower than just doing the research manually.
We often start AI projects by consolidating and cleaning data sources first. This might mean designing a unified database schema, building ETL pipelines to deduplicate information, or creating a data layer that gives AI a single source of truth. Without this foundation, AI integration fails—no matter how sophisticated the models.
Then we design AI integrations that work with your existing systems—no need to rebuild everything. Secure APIs expose the right data to AI models. Authentication ensures only authorized users get access. MCP (Model Context Protocol) servers let you swap AI models without rewriting integrations.
For production deployments, we handle the hard parts—rate limiting, error handling, monitoring, and graceful degradation when APIs go down. Your team gets AI that actually works in production, not just in demos.
Practical cases
Challenge
A company wanted AI to answer business questions across sales, operations, and customer data. But information was scattered—customer records in CRM, order history in a legacy database, product data in spreadsheets, and operational metrics in a separate system. Duplicate customer entries existed across systems with inconsistent naming. When they initially tried AI integration, answers were unreliable—the AI couldn't cross-reference data sources, gave contradictory answers, or simply said "I can't find that information" for questions that should have been answerable.
Solution
Before integrating AI, we consolidated their data infrastructure. We designed a unified database schema that connected customers → orders → products → operations. ETL pipelines deduplicated customer records and synchronized data across systems daily. The database became the single source of truth. Only after this foundation was solid did we integrate AI agents using MCP servers. Now when the AI answers "Which customers bought Product X in Q2?", it queries one clean database instead of trying to reconcile four messy ones.
Outcome
AI responses became reliable and consistent. Business teams started trusting the AI because answers matched their own data checks—something that wasn't possible when the AI was trying to reconcile fragmented sources. The time investment in database work paid off—AI that would have been abandoned as "unreliable" became a core business tool. The consolidated database also improved non-AI reporting and analytics as a side benefit.
Who this is for
Companies wanting to add AI but unsure where to start
Research teams drowning in data searches
Enterprises with data locked in proprietary systems
Teams needing AI-powered business intelligence
SaaS platforms wanting to offer AI features to customers
Anyone who needs AI that works in production, not just demos
Ready to deploy AI that actually works?
Let's discuss your AI integration challenges.