ArtificialIntelligencePut AI to work where it matters.
AI creates value when it solves the right problem and works in the real world. We help you decide where AI fits, prepare the data and teams it depends on, and build and scale secure solutions.
Where Organizations
Need Help With AI
1. Deciding
Too many AI ideas, no clear priority
AI opportunities are everywhere, but teams need help deciding what is worth pursuing first.
AI risk is hard to manage
Security, privacy, legal, and operational concerns can make it hard to move forward with confidence.
2. Preparing
Data is not ready for AI
Disconnected, inconsistent, or poorly governed data limits what AI can reliably do.
Teams are not ready to use AI
People need practical guidance, workflows, and guardrails to use AI confidently and responsibly.
3. Building and Scaling
Proofs of concept stall before production
Promising AI ideas can break down when they meet real systems, workflows, security requirements, and scale.
Software teams need more capacity
AI coding agents can accelerate delivery, but teams need the right practices and engineering controls to use them well.
Expertise
Get AI working across your organization.
AI work does not start in the same place for every organization. We help you decide where AI can create value and how to manage risk, prepare the data and teams it depends on, then build and scale solutions that fit your systems, workflows, and engineering practices. Start where you are and move forward with a practical path.
Decide
AI Strategy
Focus AI investment on business needs most likely to create strategic value. Creating an AI strategy will turn AI ideas into a set of realistic, value-based choices. We work with you to assess readiness, map high value workflows, prioritize use cases by value and stack fit, and pressure test each idea against your overall strategy, systems readiness, workforce needs, and delivery capacity. An AI strategy includes a phased roadmap with ranked priorities, success measures, risks, accountable owners along with corresponding technology choices. The results are practical next steps for moving the strongest ideas from proof tests into scoped production work.
Decide
AI Governance and Risk Management
AI solutions come with legal, security, privacy, and operational risk. But when it comes to governance – it should support action, not paperwork. It is critical to start with a review of AI use, rank risk, and map regulatory requirements and internal policies. The next steps are to build governance steps, technical controls, human review, audit logs, and monitoring for how systems are designed, deployed, and run. You gain a practical control framework for accountability, audit readiness, and ongoing risk management in production as models, vendors, regulations, and business uses which will change over time.
Prepare
AI Data Foundations
Give AI systems trusted, comprehensive, up-to-date data so they can produce reliable results. We assess your data readiness, connect sources, design governed pipelines and platforms, and improve data quality for structured and unstructured information so that your data foundation supports real-world use cases. We also prepare metadata, lineage, access controls, and retrieval patterns such as vector search and RAG. This creates a secure, context-rich foundation for production AI, easier integration, better monitoring, and future growth without rebuilding the data layer for every new use case.
Prepare
AI Enablement
Help your teams use AI confidently and responsibly in the work they already do. We combine role-based learning with change and adoption planning, workflow redesign, hands-on practice, and clear usage guides and desired outcomes tied to day-to-day responsibilities. The learning stays practical and role-specific. Leaders receive adoption measures and change plans, while teams build repeatable practices, internal expertise, responsible habits, and stronger ways of working together so they can sustain and expand AI use as technology and business needs change across the organization.
Build & Scale
AI Solutions
Put AI to work on business problems that justify a production solution. We design and connect generative AI, RAG, intelligent agents, predictive models, and decision-support tools to your data, applications, and workflows you already operate. Start with a focused proof of value. Then we engineer, test, deploy, monitor, and improve each solution so it protects sensitive information, fits day-to-day work, scales with demand, and remains practical for your teams to govern and support across complex enterprise operating environments.
Build & Scale
Agentic Software Development
AI provides the promise of building more software without giving up engineering control, security, or production quality. We use AI coding agents across requirements, system discovery, coding, testing, deployment, and support while helping client teams understand, configure, govern, and deploy agentic development practices themselves. We can also help you build internal agentic software development capabilities where senior engineers set guardrails, review agent work, and transfer proven methods so your teams. The target outcomes are to accelerate modernization, preserve system knowledge, expand delivery capacity, and build a lasting in-house capability to use agentic programming across your own software teams.
Our Clients
Hundreds of clients. Thousands of data projects.
“With every sprint, we are doing something that is changing the industry in education. We want to blaze the trail for what’s coming up and what’s new and possible in education, and we couldn’t really do that without Resource Data.”
~ Gretchen Clarkson, Business Analyst, Epic Charter Schools
Public housing finance corporation
Boosting efficiency with an AI-powered data migration
A public corporation that helps provide community members with access to affordable housing wanted to scale up its operations, but its existing database infrastructure became a barrier to growth. Relying on Microsoft SQL Server required costly licensing and lacked the flexibility to meet their expanding needs. They were looking for a secure, cost-effective solution to migrate their databases to an open-source platform without risking sensitive data.
Resource Data implemented an innovative migration strategy using Meta’s open-source Llama 3.1 AI model, configured for local processing. This involved translating SQL Server code into PostgreSQL, enabling the client to migrate databases while avoiding expensive licensing fees and mitigating security risks associated with cloud-based AI tools. Llama AI streamlined the process, reducing manual workload and achieving in days what typically took months.
After migrating a few critical databases, the client initiated plans to use this secure, scalable solution for additional migration projects. This work also spurred interest in other AI applications to improve their organizational efficiency.
Allpoints Surveying
AI-Driven Automation Simplifies Order Processing
As Allpoints Surveying expanded into new markets, processing customer orders from emails, PDFs, and spreadsheets became a challenge. The industry’s inconsistent terminology, Allpoints’ multi-region operations, and errors in customer emails made manual categorization by coordinators time-consuming and error prone. AllPoints needed an adaptive solution to handle these complexities.
Resource Data implemented an AI solution powered by OpenAI’s GPT-4 for a proof of concept (POC). The AI parsed sample emails, extracting key details like builder names, lot addresses, and task requests. Advanced data-validation techniques matched this information to AllPoints’ MasterJob and Customer tables, producing structured outputs. The system also identified surveying task requests, linked them to a list of standardized task names, and assigned task IDs.
The POC achieved 94% accuracy in identifying builders and 68% accuracy in standardizing tasks. These results showed how AI could enhance efficiency and help AllPoints achieve future system integrations and operational improvements.
Epic Charter School
AI Powers Document Classification and Management
Epic Charter School, Oklahoma’s largest public virtual charter school, caters to about 30,000 students from pre-K to 12th grade. Resource Data developed an advanced AI solution for Epic to efficiently manage processing thousands of enrollment and transfer documents each year.
Our team used Azure cloud AI technologies, including GPT with Vision, to create a system that handles diverse document formats. It classifies and verifies documents and extracts essential information for updates to the student-information system.
Epic has benefited from reduced manual data entry, more efficient operations, better data reliability, cost savings, and an improved educational experience for families. We are also currently working on developing a centralized operational database for Epic with AWS Glue.
Same Sky
AI RAG chatbot speeds up technical support
Same Sky is an electronic component manufacturer with a fast-growing catalog of highly technical products. As the portfolio expanded, support engineers spent more time searching thousands of datasheets and scattered documents to answer customer questions. The information existed, but finding and verifying it slowed response times and made answers harder to keep consistent.
Resource Data partnered with Same Sky to build a retrieval-augmented generation (RAG) chatbot in Microsoft Azure. It pulls relevant content from more than 3,000 datasheets and technical resources, drafts responses, and includes citations so engineers can quickly verify sources. Engineers can rate responses to improve accuracy over time.
In early use, the chatbot provides about 90% of the content needed for complex inquiries. Engineers spend less time searching and more time validating answers and applying expertise. Customers get quicker, more consistent support that improves service from first question to resolution.
Major Food Processor
AI Product Recognition Leads to Market Insights
One of the world’s largest producers and processors of frozen potato products wanted to boost their marketing efforts. They needed a solution to identify strategic clients and competitive products, turning market data into potential sales opportunities.
Resource Data developed a .NET-based system that used an advanced web crawler to collect targeted images from websites. We leveraged AWS Rekognition, a cloud-based image analysis service, to process and categorize these images, identifying food types and storing the results in Amazon DynamoDB for integration with the client’s database for marketing use.
This AI-driven tool empowered the marketing team to analyze competitive products and identify sales opportunities efficiently, transforming market intelligence into strategic actions. By automating image recognition and analysis, the client gained deeper insights into their market landscape, enabling informed decision-making and driving business growth.
Real-World Solutions
AI with purpose.Put AI to work.
At Resource Data, we don’t chase trends. We build solutions that work.Our clients rely on us for robust data systems and tailored digital tools. Now, we’re helping them take the next step with AI from automating paperwork to analyzing satellite imagery.
Government
Reduce manual work without losing accountability
We work extensively with government agencies to digitize workflows, automate reporting, and modernize legacy systems to prepare for AI adoption. AI can extract data from applications, permits, and records, while retrieval-based chatbots can answer questions using internal policies. These tools help agencies process information faster, reduce errors, and improve service for staff and constituents.
Oil and Gas
Improve Safety and Monitoring with AI
Resource Data supports oil and gas companies with advanced analytics and geospatial modeling of Arctic terrain. Computer vision and machine learning can analyze thermal pipeline imagery, predict maintenance needs from sensor data, and track terrain changes to monitor climate impact. These tools reduce hazardous fieldwork and improve operational reliability.
Fisheries
Put more data to work for science and compliance
For fisheries agencies and research groups, we build tools for compliance, reporting, ecosystem tracking, and interagency collaboration. Machine learning can extend these systems by identifying species and classifying catch records through image recognition. Predictive models can also track migration, spawning, and environmental trends, helping teams make faster conservation decisions without disrupting regulatory or scientific workflows.
Utilities
Build intelligence into existing operations
Our utility clients rely on us to build cloud-based data infrastructure and reporting tools that support predictive maintenance, outage forecasting, and energy optimization. We also develop RAG-based virtual agents that answer billing, service, and technical questions using the utility’s own documentation and data. Modern data governance and integration help these tools reduce support loads and improve infrastructure management.
Natural Resources
Scaling Environmental Monitoring with Remote AI
From forest health to glacial retreat, natural resource organizations need reliable insight across vast, often inaccessible areas. We help managers visualize terrain changes, track environmental metrics, and centralize geospatial data. AI and computer vision can analyze satellite imagery to track land use, classify vegetation, detect erosion, and flag wildfire risk zones, reducing the need for physical surveys and supporting faster environmental planning.
Education
Reduce administrative work and find answers faster
Public schools, universities, and virtual learning platforms face document-heavy workflows and growing demand for fast, personalized support. Vision-based AI can automate form classification, while RAG chatbots can answer policy and process questions for staff and families. With security and accuracy built into these solutions, AI can reduce backlogs, speed up turnaround times, and ease administrative work while improving the student experience.
Jason Child
Service Area Lead, AI
Jason joined Resource Data and immediately championed internal AI working groups and delivered results on client AI projects. He’s excited about custom AI solutions where no off-the-shelf product exists, like a chatbot for a manufacturing client. His career spans AI, blockchain, internet of things (IoT), and cloud computing. He co-founded an industrial-IoT startup that used machine learning to predict industrial machine and systems failures. As CTO of Remarkably, he built real-estate marketing analytics with AWS ML APIs. Later, Jason launched full-stack blockchain products with Rust—learning the language via generative-AI copilots. His extensive experience with business intelligence tools, cloud solutions, AI-driven data analytics, and more has supported the growth of AI in Resource Data.
Our Data and AI Services
Engineer your advantage with data and AI.
Talk to Jason Child
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