Capabilities
What we deliver.
Custom AI Solutions
Tailored implementations designed around your actual workflows, systems, users, and business constraints.
Document & Knowledge Automation
Extract, classify, summarize, route, and search business information with human review where accuracy matters.
Conversational AI
Customer or employee assistants grounded in approved business data, workflows, and escalation rules.
Decision Support
Use business data to surface patterns, priorities, exceptions, and recommendations for people to act on.
Process Automation
Connect repetitive steps across systems to reduce manual handoffs, duplicate entry, and preventable delays.
AI Strategy & Readiness
Assess feasibility, data readiness, risks, integration options, success measures, and the business case before building.
Start With the Workflow, Not the Model
The most useful AI projects begin with a business process that is slow, repetitive, inconsistent, difficult to scale, or hard to measure. We map the people, systems, data, decisions, and exceptions involved before deciding whether AI is even the right tool.
Sometimes the best solution is an AI assistant. Sometimes it is workflow automation, better data access, a custom application, or a combination of technologies. Delpuma is a custom solutions company, so the architecture follows the problem rather than forcing every client into the same product.
Before implementation, we define what success means. That can be less manual work, faster response time, fewer handoffs, improved customer service, better conversion, or another measurable outcome. The baseline matters because it gives the client something real to compare against after launch.
How AI Integration Works in Practice
Real integration connects AI to approved business data, APIs, databases, forms, CRM records, documents, communication tools, or internal workflows. The AI should operate inside clear permissions and business rules rather than becoming an uncontrolled black box.
We design for failure states and human review. High-impact actions can require approval, uncertain answers can escalate to a person, and sensitive data can be limited to the users and systems that should have access. Good AI architecture includes what happens when the model is wrong, unavailable, or missing context.
The final system should make work easier for the people using it. That means practical interfaces, useful notifications, clear status, auditability where needed, and training that helps the team understand when to trust automation and when to use judgment.
Common Business Use Cases
Customer-facing use cases can include intelligent intake, appointment or lead qualification, FAQ support grounded in business content, proposal assistance, follow-up workflows, and personalized experiences tied to known customer information.
Internal use cases can include document processing, knowledge search, summarization, CRM updates, task routing, reporting assistance, anomaly detection, content workflows, and decision support. The right use cases depend on where the business spends time and where better information changes the outcome.
We do not publish universal ROI, accuracy, or savings percentages for these use cases. Each implementation has different data, adoption, complexity, and economics, so performance is measured against the client-specific baseline and connected data sources.
Our Process
How we integrate AI into your business.
Readiness & Workflow Assessment
Map the process, systems, users, data, risks, and measurable business outcome before deciding what to build.
Architecture & Data Plan
Define integrations, permissions, data flows, human-review points, security requirements, and the experience users need.
Build & Integrate
Develop the AI and automation components and connect them to the approved business systems in testable increments.
Validate With Real Workflows
Test expected cases, exceptions, permissions, accuracy, failure behavior, and user experience against the agreed requirements.
Launch & Train
Roll out the solution with documentation, user training, monitoring, and clear escalation paths for issues or uncertain outputs.
Measure & Improve
Compare verified outcomes with the baseline, review adoption and failure patterns, and prioritize the next improvements based on evidence.
Comparison
Delpuma AI Integration vs. Generic AI Add-ons
| Feature | Delpuma | Traditional Agencies |
|---|---|---|
| Fit | Designed around your actual workflow | Generic feature set or one-size-fits-all assistant |
| Integration | Connected to approved business systems and data | Often isolated in another tab or tool |
| Measurement | Baseline and client-specific KPIs defined before launch | Usage metrics or broad AI claims |
| Human review | Approval and escalation rules designed into the workflow | May rely on users to notice bad outputs |
| Permissions | Access designed around roles, data, and business risk | Generic account-level access |
| Delivery | Discovery, build, integration, testing, training, and iteration | Software access plus documentation |
| Ownership | Terms, code, data, and support are defined in the client agreement | Typically limited to subscription access |
| Improvement | Changes prioritized from verified usage and business results | Vendor roadmap or generic feature updates |
FAQ
Common questions.
Click a question to reveal the answer.
AI integration connects AI capabilities to the systems and workflows your business already uses. The goal is not to add another chatbot or dashboard—it is to improve a specific process, customer experience, or decision using the right combination of software, automation, data, and AI.