Data & AI Consulting
The true value of AI lies in production, not in pilots. We provide expert consulting that bridges the gap between proof-of-concept and scalable deployment. Our services cover advanced analytics, high-value use case identification, and the implementation of robust MLOps principles. We ensure continuous performance, governance, and verifiable ROI from your models, enabling true AI-enablement across your enterprise.
Benefits of Data & AI Consulting
Smarter Decision-Making
Optimized Operations
Accelerated Innovation
Data Security
AI & Data Consulting Lifecycle
1 Value Identification
Analyze your data assets and business processes to define the most high-impact AI use cases (Discovery).
2 Model Prototyping
Design and build initial machine learning models and define the secure deployment architecture.
3 MLOps Pipeline Creation
Establish automated pipelines for data versioning, model training, rigorous testing, and seamless deployment.
4 Governance & Monitoring
Integrate observability tools to track model performance, bias, and compliance in the live production environment.
5 Optimization & Iteration
Implement automated retraining loops and feedback mechanisms to ensure models adapt and maintain accuracy over time.
Maintenance
Technologies We Excel In
- Technologies
Advanced Analytics, MLOps
- Tools
Python, Power BI, Data Warehouses, ETL Tools, Generative AI ModelsÂ
Our Works
Welcome to Our Frequently Asked Questions
We’ve put together answers to some of the most common questions we receive about Bent Oak Systems’s solutions, our processes, and how we collaborate with clients.If you can’t find the information you need, please just reach out to us directly, we’re here and ready to help.
Client Testimonials
FAQ’s
Welcome to Our Frequently Asked Questions
We’ve put together answers to some of the most common questions we receive about Bent Oak Systems’s solutions, our processes, and how we collaborate with clients.If you can’t find the information you need, please just reach out to us directly, we’re here and ready to help.
What is MLOps, and why is it essential?
MLOps automates and manages model deployment and monitoring in production. It ensures your AI remains accurate, secure, and continuously delivers business value.
How do you identify the right AI use cases for my business?
We start with business value and data availability. We prioritize use cases that show the highest projected ROI and directly impact your defined KPIs.
How long does it take to deploy an AI model into production?
With MLOps, we drastically reduce deployment time. Once the model is validated, the pipeline moves it to production in minutes, not months.
What if our internal team has limited AI expertise?
Our service includes AI-enablement. We transfer our knowledge to your internal teams, empowering them to manage and maintain the MLOps pipelines.
How do you handle data governance and model bias?
We integrate governance and fairness tools into the MLOps pipeline. This ensures continuous monitoring for bias, drift, and compliance with industry standards.
Can you work with the data warehouse we already use?
Yes. Our consultants are experts in integrating with all major data platforms, including Snowflake, BigQuery, and custom data lakes, to feed the MLOps pipelines.
Do you focus on Generative AI (LLMs) or traditional ML?
We are proficient in both. Our approach is solution-agnostic; we recommend the technology (LLM, computer vision, traditional ML) that best guarantees the defined KPI.
How do you ensure the model performance doesn't degrade over time?
We implement continuous monitoring and automated retraining loops. This is a core component of MLOps to ensure the model adapts to new data and maintains accuracy.
Do we need a massive amount of data to start?
Not always. The 2-week Validation Sprint assesses data readiness: We often start with focused, high-value use cases that require smaller, cleaner data sets.
Do you provide the actual infrastructure for the AI systems?
Yes. We design and help implement the necessary cloud infrastructure (via AWS, GCP, or Azure) to host the MLOps pipelines and deploy models securely and cost-effectively.
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