Custom AI Products
Purpose-built applications that use ML, LLMs, retrieval, automation, and analytics to support real business workflows.
Custom AI, ML, and agentic systems
Custom AI products, designed and implemented for real operations
d-Analytics turns complex workflows, data, and infrastructure constraints into production-ready ML, AI, and agentic systems. We design the product, build the software, integrate it with your environment, and help you deploy it on premises, in the cloud, or across hybrid infrastructure.
Product delivery for energy, geotechnical, healthcare, and high-trust environments.
Design
Workflow, product experience, architecture, and evaluation planning.
Implementation
Applications, models, agents, integrations, retrieval, and data pipelines.
Deployment
On-prem, AWS, GCP, and hybrid delivery paths for real constraints.
Iteration
Monitoring, evaluation, feedback loops, and controlled improvement.
AI systems built around your workflow
Generic AI tools rarely match the way real organizations operate. d-Analytics designs and implements custom systems around your data, users, tools, policies, and deployment constraints.
Agentic Workflow Systems
Supervised agents that coordinate tools, data, approvals, and human review so complex processes can move faster without losing control.
Machine Learning Systems
Models, pipelines, evaluation workflows, and monitoring designed for the prediction, classification, detection, or decision-support problem in front of you.
AI Integration
Practical implementation that connects AI capabilities to existing software, data platforms, cloud services, and operational systems.
Target sectors
Agentic AI for complex, high-consequence operations
d-Analytics keeps its geotechnical and energy roots, but applies them through a modern product-delivery lens: designed, implemented, and deployed AI systems that fit the operating environment.
Oil and gas operations
Agentic systems for technical workflows, document-heavy processes, field operations, asset support, knowledge retrieval, monitoring workflows, and decision support where human review and traceability matter.
Ground, infrastructure, and risk
AI products that build on geophysical, geotechnical, mapping, monitoring, and data-processing experience without limiting the company to legacy service delivery.
Healthcare operations
Supervised agentic workflows for intake, routing, documentation support, knowledge retrieval, scheduling, referrals, revenue cycle, and operational analytics.
Custom workflow products
Cross-sector AI systems for teams with specialized data, sensitive environments, legacy software, complex approvals, or work that off-the-shelf tools cannot handle.
Sector focus
Equal depth for technical operations and healthcare operations
Both markets need the same delivery discipline: clear workflow design, controlled agents, integration with existing systems, secure deployment, evaluation, and iteration after launch.
Energy and geotechnical AI
Agentic systems for field, asset, and technical workflows
d-Analytics can design and implement agentic systems for oil and gas, geotechnical, infrastructure, mining, monitoring, and other technical operations where data, documents, field context, and engineering judgment need to come together.
- Technical knowledge retrieval
- Field and asset workflow support
- Monitoring and anomaly review
- Reporting and decision support
Healthcare AI
Agentic systems for healthcare operations
d-Analytics can design and implement supervised agentic workflows for healthcare operations, administrative processes, care coordination support, documentation support, knowledge retrieval, scheduling, referrals, claims, revenue cycle, and operational analytics.
- Human-in-the-loop review
- Audit-ready workflow design
- Controlled tool access
- Privacy-aware deployment
From idea to deployed product
The work is structured around delivery. Define the problem, design the product, build the system, deploy it into the right environment, and keep improving it.
Discover
Map the workflow, users, data, systems, risks, and measurable outcome.
Design
Define product experience, architecture, agent boundaries, and evaluation.
Build
Implement the application, models, agents, pipelines, retrieval, and permissions.
Deploy
Ship into on-prem, cloud, or hybrid environments with monitoring and rollback paths.
Improve
Measure behavior, evaluate outputs, collect feedback, and iterate.
Built for the environment you actually operate
Some AI systems belong in the cloud. Others need to run on premises because of data sensitivity, latency, governance, cost, or integration constraints. d-Analytics designs around the environment instead of forcing every solution into the same pattern.
- On-prem deployment For sensitive data, controlled networks, local inference, or strict governance.
- Cloud-native delivery Builds that use AWS or GCP services where managed infrastructure is the right fit.
- Hybrid architecture Systems that connect internal data and tools with cloud AI services.
- Evaluation and monitoring Measurement, logging, review, and iteration designed into the product from day one.
Why d-Analytics
Data-heavy roots, product-delivery focus
d-Analytics grew from complex data, modeling, and field-proven problem solving. That background still matters, but the focus has shifted: today, d-Analytics helps organizations design and implement working AI products that connect models, software, infrastructure, and real operational workflows.
Have an AI workflow that needs to become a product?
Bring the problem, the workflow, or the rough idea. d-Analytics can help shape it into a designed, implemented, deployable AI system.
Discuss an AI build