We build custom AI solutions that automate processes, turn data into insights, and drive smarter decisions from AI agents and generative AI to predictive analytics and intelligent automation.
Explore real product examples to see how we approach an AI problem, choose the right method, connect the required data, and turn the result into a working product feature. Each example focuses on the actual workflow instead of using the same AI diagram for every project.
Wizly turns a raw business question into either an instant AI-organized insight or a booked call with a matched industry expert, whichever the question actually needs.
Modeled on wizly.app Q&A and expert-matching flow.
A tour agent answers a few guided questions, and the AI plots stops on the map, sources photos, and writes narration, ready to walk offline in minutes.
Modeled on walkntours.com 350+ GPS-enabled audio tours.
Snap a label, and the AI checks every ingredient against a 500+ animal-derived compound database and five diet profiles at once.
Modeled on foodcheckai.com real 3-step scan flow.
The AI merges 360° Leadership Growth Profile data with session notes into one coach-ready brief, scored across the three pillars TripleGoal actually measures.
Modeled on triplegoal.com Leadership Growth Profile and Triple Goal framework.
CaMI generates a compliant commissioning checklist through a back-and-forth AI chat, then keeps it audit-ready with a full record of every change.
Modeled on cami-app.com checklist, maintenance queue, and audit compliance features.
One destination and a few preferences unlock the same seven planning tiles families see in the real app, generated together instead of researched one by one.
Modeled on kiddometer.com trip-planning categories.
AI and machine learning are related, but they are not the same thing.
Artificial intelligence helps software handle tasks that normally need human judgment.
That could mean answering a customer question, reviewing a document, spotting an issue in an image, or deciding what action to take next.
Machine learning is one of the main ways we build AI.
Instead of creating a rule for every possible situation, we train a model with examples. The model learns patterns in the data and uses those patterns to make predictions or classifications.
For example, machine learning can help forecast demand, detect fraud, recommend products, or identify unusual activity.
Two disciplines working together so your AI feature is both smart and dependable in production.
Build AI features into your website, application, platform, or internal workflow.
Use generative AI to create, summarize, transform, and work with business content.
Build AI agents that can understand a goal, use tools, follow steps, and complete tasks.
Create chat and voice experiences that understand user intent and provide useful responses.
Use AI to understand images, scans, and documents.
Build models that help forecast what may happen next.
Use neural networks for problems that need advanced pattern recognition.
Help users find the products, content, or actions most relevant to them.
Keep machine learning systems reliable after they go live.
Turn large datasets into information your team can use.
AI features that fit your existing product and help your team do useful work faster.
A few common ways our clients put AI to work with solutions shaped around their data and goals.
Let AI plan, execute, and manage complex multi-step tasks from start to finish with minimal human intervention.
Forecast sales, predict demand, identify trends, and spot potential risks before they become problems.
Recommend products, articles, content, or playlists based on user behavior, preferences, and past interactions.
Analyze contracts, invoices, reports, and other documents to summarize content, classify information, and extract key details.
Identify and classify objects in images for security, quality control, inspections, and review workflows.
Turn simple text prompts into product images, marketing visuals, and creative concepts.
Tell us what you want to improve and where your process slows down. We’ll help you find the right AI solution and next steps.
AI works best when it matches the way your business operates. We design solutions around your workflows, data, customers, and industry requirements instead of treating every business problem the same way.
Personalize shopping experiences, forecast inventory, detect fraud, and understand customer behavior.
Support fraud detection, risk scoring, document review, financial checks, and customer service workflows.
Use AI for property analysis, market research, lead qualification, valuation support, and customer questions.
Improve fleet operations, route planning, vehicle monitoring, and predictive maintenance.
Support patient intake, document processing, workflow automation, and risk review with privacy and security in mind.
Create adaptive learning experiences, content recommendations, student support tools, and automated workflows.
Improve demand forecasting, route planning, inventory decisions, and supply chain monitoring.
Support content creation, recommendation systems, image and video analysis, and audience insights.
Connect AI with Salesforce, CRM platforms, internal applications, and enterprise workflows to bring intelligence into the tools your teams already use.
Every AI project is different, but the core steps stay clear. We start with the business problem and build toward a solution that can work in production.
We understand your business goal, users, current workflow, and expected outcome. We define the problem, scope, timeline, and success measures.
We review the data needed for the solution. This may include documents, customer data, application data, images, or other business information.
We test the right technical approach before moving into a full build. Depending on the project, this may involve an existing AI model, machine learning, RAG, computer vision, or a custom model.
We test the system against real examples and edge cases. We improve accuracy, speed, reliability, and cost based on the results.
We connect the AI solution to your application, Salesforce environment, CRM, APIs, databases, or other business systems.
AI systems need care after launch. We monitor performance, review results, update models or prompts when needed, and help keep the system reliable as your data and business change.
A successful AI demo is not the same as a successful AI product. Before launch, we think about what happens when the AI is wrong, the data changes, a user asks an unexpected question, or a connected service becomes unavailable.
Test AI responses against real business examples.
Add approval steps where decisions need human oversight.
Limit what information the system can access and use.
Define what the system should do when AI cannot give a reliable answer.
Track quality, usage, errors, latency, and cost.
Protect business data throughout the application workflow.
Improve prompts, models, workflows, and data as requirements change.
AI is only useful when it works with the rest of your product.
Differenz System brings software development, Salesforce, enterprise application, and AI expertise together to build solutions that can fit into real business systems.
What you can expect
Straight answers to what most clients ask before starting an AI project.