AI-Powered Data Platforms

Thousands of Data Sources.
None of Them Agree.
We Fix That.

You’ve got spreadsheets, APIs, GIS databases, government datasets, and thousands of websites — all describing the same things in slightly different ways. Someone above you wants this turned into one clean, queryable system with maps and scores and dashboards. Before AI, this was either a multi-year, multi-million-dollar project or just not feasible. Modern Logic builds systems that make it feasible — and affordable.

Case Study: Montana

What We Built for Montana

  • The Data Challenge

    We ingested data from tens of thousands of website pages, OpenStreetMap, ArcGIS, state databases, CSV dumps, Excel files, and REST APIs. Every source had its own format, naming conventions, and idea of what counts as a tourism asset.

  • What AI Made Possible

    AI reads website content and figures out what each business actually is — turning messy, real-world data into clean, categorized records. It handles ambiguity that breaks rules-based systems: synonyms, misspellings, hybrid businesses, incomplete addresses.

  • The Tourism Readiness Score

    One number per community that represents tourism readiness. We worked with the client to define and iteratively refine a scoring algorithm that balances quantity and diversity of assets — because 500 restaurants and no hotel doesn’t make a tourist destination.

  • Intelligent Deduplication

    We combine name matching, GPS coordinates, address data, and reverse geolocation to determine whether two records from different sources describe the same physical location — even when the data doesn’t match cleanly.

The Platform

What Montana Can Do Now

  • Data-Driven Grant Allocation

    When communities apply for tourism-related grants, the state has a data-driven way to evaluate which investments will have the most impact.

  • Discover Hidden Gems

    Surface communities that have strong tourism potential but aren’t currently getting marketing attention.

  • Historical Analysis

    Track whether past investments in specific communities actually moved the needle on tourism readiness.

  • Proactive Development

    If a town has most of the ingredients for a great tourist destination but is missing one thing — like lodging — the state can identify that gap and work with the community to fill it.

Cost Control

How We Kept AI Costs Under Control

  • Minimized Context Per Call

    We strip pages down to the relevant content before sending to the model. No nav bars, no footers, no boilerplate. The model only processes what matters.

  • Routed Around Bad Data

    Websites that won’t load, are stale, or have garbage content get flagged and skipped rather than burning tokens on useless processing.

  • Built-In Cost Monitoring

    A dedicated system for tracking token usage and cost per pipeline run, with alerts when spending trends upward.

  • Right-Sized Model Selection

    Not every task needs the most expensive model. We matched model capability to task complexity.

How We Build

AI-Assisted Development, Grounded in Fundamentals

We used AI in both directions on this project: AI powers the data platform itself, and AI-assisted development helped us build it faster and at lower cost than would have been possible three years ago.

But — and this matters — you can’t vibe-code a system this complex. AI-assisted development only works when it’s guided by engineers who know what good architecture looks like.

  • Architecture by humans, implementation accelerated by AI — The overall system design, data flow, and infrastructure choices were made by engineers with 50+ combined years on this stack
  • Code quality review — AI-generated code gets the same scrutiny as human-written code. We check for vulnerabilities, verify correctness, and ensure maintainability
  • Production-grade operations — Monitoring, alerting, failure handling, backups, and data integrity. The same DevOps rigor we’d apply to any production system
  • Right-sized infrastructure — Built to run on modest hardware because we broke the problem down correctly, not thrown at oversized servers because it “seems like a big data problem”

“If you’d asked me three years ago what it would cost to build a system like this — with a built-in search engine, AI analysis for automatic tagging, an integrated website with maps and rich searching — I would have said $10 million and two years.”

For Agencies

Built for Marketing Agencies Who Need a Development Partner

This project came to us through a partnership with a marketing agency. Their client needed a data-driven web application, and that’s a different skill set than building marketing websites — even though both involve the web.

If you’re a marketing agency and your clients are increasingly asking for data-driven tools, dashboards, portals, or AI-powered applications, we can help. We’re not trying to take your client. We’re a development partner:

  • We work within your timeline and client relationship — you stay in the driver’s seat
  • We bring the technical expertise — architecture, AI, data engineering, cloud infrastructure
  • You bring the design and client expertise — branding, UX direction, stakeholder management
  • The result is better than either of us could deliver alone

Frequently Asked Questions About AI Data Platforms

Modern Logic builds data platforms that ingest from APIs, databases, GIS systems, spreadsheets, and the open web. We handle the hard parts — deduplication, normalization, and reconciliation across sources that don’t agree with each other. For the Montana project, we unified data from tens of thousands of websites, OpenStreetMap, ArcGIS, state databases, and client-provided datasets into a single, queryable system.

Yes, with the right engineering. Large language models are good at understanding context and handling ambiguity — they can tell that a “medical center” and a “hospital” are the same kind of thing, or that a business selling burgers is a restaurant even if the word “restaurant” never appears. The key is building a system where AI handles the bulk of classification and humans review the edge cases. We processed roughly 100,000 assets this way.

By being deliberate about what you send to the model and which model you use. We built cost monitoring into the Montana platform from the start — stripping unnecessary content before processing, skipping bad data, tracking token usage per pipeline run, and matching model capability to task complexity. The result was a system that processes tens of thousands of data sources at a cost that fits a state government budget.

Start by talking to a team that’s built them. Modern Logic builds web applications, data dashboards, portals, and internal tools. We’ll help you figure out the right architecture for your problem — what data sources to integrate, what the user experience should look like, and what infrastructure keeps costs reasonable as you scale.

That’s exactly how the Montana project started. A marketing agency had a client asking for a data-driven web application, and they needed a development partner. We work alongside marketing agencies — you keep the client relationship, we build the technical product. We’ve done this successfully with partners and it works because we’re complementary, not competitive.

It depends on the scope, but AI-assisted development means we can move faster than you’d expect. The Montana platform — with tens of thousands of data sources, AI-powered categorization, interactive maps, dashboards, and a custom scoring algorithm — was built in a fraction of the time and cost that would have been required even three years ago.

Let’s Talk About Your Data Problem

Whether you’re trying to make sense of thousands of data sources, build an AI-powered platform, or just need a dashboard that actually works — we’ve built systems like this and we know where the hard parts are. Book a call and tell us what you’re trying to build.