AI Strategy & Implementation
We help businesses go from "AI sounds interesting" to "AI is working for us." Our team guides you through the entire journey—from figuring out where AI makes sense, to building and deploying models that actually work.
Whether you want to use ChatGPT-style AI in your business, build predictive models, or set up data pipelines to feed your AI—we've got you covered. We use AWS AI services like SageMaker, Bedrock, and Comprehend to build solutions that scale.
Why AI Projects Often Fail
Most companies know AI could help them, but struggle to make it work. Common problems: not knowing where to start, messy data, lack of skills, and difficulty connecting AI to existing systems.
Stuck in Pilot Mode
Many AI projects start strong but never make it to production. Without clear goals and a solid plan, you end up with demos that don't deliver real value.
Data Isn't Ready
AI is only as good as your data. Many companies discover too late that their data is scattered, inconsistent, or missing key pieces.
Security & Compliance Worries
How do you use AI without exposing sensitive data or breaking regulations? It takes expertise to get this right.
Hard to Integrate
Building an AI model is one thing. Getting it to work with your existing systems and getting people to actually use it is another challenge entirely.
Our Approach: Make AI Actually Work
We start with your real business problems, not technology. We figure out what's possible with your data, build what you need, and make sure it works in the real world. No buzzwords—just AI that delivers results.
AI Services
From strategy to deployment, we deliver end-to-end AI solutions tailored to your business needs
AI Strategy & Roadmap
We help you create a clear plan for AI that fits your business goals. No jargon—just practical steps to get from idea to working AI.
Custom ML Models
We build machine learning models tailored to your needs—whether it's predicting sales, detecting fraud, or automating decisions.
Generative AI (ChatGPT, etc.)
We help you use tools like ChatGPT and other AI assistants safely in your business with proper security and controls.
Responsible AI
We make sure your AI is fair, explainable, and compliant with regulations—so you can trust the decisions it makes.
AI Deployment (MLOps)
We set up systems to deploy, update, and manage your AI models in production—using AWS SageMaker and other cloud tools.
AI Monitoring & Tuning
We track how well your AI performs over time and fine-tune it to keep delivering accurate results.
AI Use Cases & Our Solutions
Proven AI applications that deliver business value in various industries—including our own AI-powered solutions
Predictive Maintenance
Know when equipment will fail before it happens—so you can fix it first
Customer Churn Prediction
Spot customers who might leave and take action to keep them
Demand Forecasting
Predict what products you'll need and when—reducing waste and stockouts
Fraud Detection
Catch suspicious activity instantly as it happens
Document Processing
Automatically read, understand, and extract data from documents
AI Chatbots & Assistants
Build smart assistants that answer questions and help customers 24/7
We've built production AI solutions across multiple industries, demonstrating our implementation expertise
Asset Retirement Obligation (ARO)
AI-powered compliance and financial reporting for asset retirement obligations
Learn moreUtilityIQ
Predictive analytics for gas pipeline corrosion and maintenance optimization
Smart4Cast
Advanced forecasting platform for demand prediction and inventory optimization
When Does AI Make Sense?
Not every problem requires AI. We help you identify where AI delivers genuine value versus where traditional approaches are more appropriate.
Good Fit for AI
- •Pattern recognition in large datasets
- •Repetitive decision-making at scale
- •Prediction based on historical data
- •Natural language processing tasks
- •Image or video analysis
- •Anomaly detection in real-time
Requires Careful Assessment
- •Limited or poor quality data
- •High-stakes decisions needing explainability
- •Rapidly changing business rules
- •Small-scale or infrequent tasks
- •Situations requiring human judgment
- •Regulatory or ethical constraints
Traditional Solutions Better
- •Simple rule-based logic
- •Deterministic calculations
- •Well-defined business processes
- •Insufficient data volume
- •Cost exceeds expected benefit
- •Existing solutions work well
Our Assessment Process
We begin every engagement with a structured assessment that evaluates your data readiness, technical infrastructure, organizational capabilities, and expected business impact. This ensures we recommend AI solutions only when they're the right tool for the job—and helps you avoid costly missteps.
Business Value
Quantifiable impact
Technical Feasibility
Data & infrastructure
Implementation Risk
Complexity & timeline
ROI Timeline
Time to value
Data & Infrastructure: The Foundation of AI
AI models are only as good as the data and infrastructure supporting them. We address these foundational elements before building solutions.
Data Readiness Assessment
Data Quality
Completeness, accuracy, consistency, and timeliness of your data sources
Data Volume
Sufficient historical data for training reliable models
Data Access
Ability to extract, transform, and load data from source systems
Data Governance
Policies for data privacy, security, and compliance requirements
Labeling & Annotation
For supervised learning, availability of labeled training data
Infrastructure Requirements
Compute Resources
Assessment and planning for GPU/CPU capacity needed for model training and inference
Storage Architecture
Design scalable data storage solutions for training datasets and model artifacts
MLOps Pipeline
CI/CD for model versioning, testing, and deployment automation
Monitoring Systems
Track model performance, data drift, and system health
Integration Points
APIs and interfaces to connect AI models with existing applications
Building the Foundation First
Many AI projects fail because organizations skip foundational work. We assess your current state, identify gaps, and help you build the necessary data pipelines and infrastructure before developing AI models.
This approach may take longer initially, but it significantly increases the likelihood of successful deployment and long-term model performance.
AI Implementation Journey
A systematic approach from discovery to production deployment, with continuous optimization
Discovery & Assessment
Identify high-value AI opportunities and evaluate feasibility
Proof of Concept
Validate approach with pilot project on real data
Model Development
Develop and train AI models with your data
Integration & Deployment
Deploy models to production with MLOps
Monitoring & Optimization
Continuous improvement and retraining