Bespoke AI Systems & Product Intelligence
Custom AI Development for
Problems Generic Tools Cannot Solve
Built around your data, decisions, and products—not someone else's template.
SensViz designs and builds custom AI solutions around your data, decisions, users, and existing software. From private knowledge systems and multimodal AI to recommendations, forecasting, and computer vision, we turn a well-defined business problem into reliable software your team can use, measure, and improve.
AI products we've designed and built
Custom-built around your business
Where generic AI stops, custom development starts
Off-the-shelf AI tools are useful when the problem is common and the workflow can adapt to the product. Custom AI development is different: the system is designed around your data, business rules, users, accuracy requirements, and the software you already rely on.
SensViz helps define where AI can create practical value, then builds the models, retrieval layer, APIs, interfaces, and integrations required to make it usable. The result may be a recommendation engine, a forecasting model, a computer vision system, a document intelligence tool, or an AI capability embedded inside an existing product.
We do not start by assuming AI is the answer. If a simpler rule, analytics workflow, or conventional software feature will solve the problem more reliably, we will recommend that instead.
- Your data and business rules shape the solution
- Requirements are defined before model or vendor selection
- Accuracy, latency, cost, privacy, and oversight are designed together
- AI is integrated into a usable product or operational workflow
What we build
Custom AI capabilities built for a clear business outcome
Each engagement begins with the decision, prediction, interaction, or insight the system must improve. We then select the smallest reliable technical approach that can deliver that outcome.
Match users with relevant products, content, funding, services, or next steps using behavioral data, business rules, similarity, ranking, and feedback. The system is designed around the context and signals that matter to your business.
Use historical and real-time data to forecast demand, risk, quality, performance, or operational conditions. Outputs can feed dashboards, alerts, planning tools, or human review rather than making unchecked decisions.
Build useful experiences across text, images, audio, and structured data. We combine model capabilities with your product rules, context, and interfaces so multimodal AI performs a defined task instead of producing disconnected content.
Give users or teams reliable access to approved company knowledge through retrieval-augmented generation. We design ingestion, chunking, retrieval, permissions, citations, evaluation, and fallback behavior as one system.
Interpret images or video for recognition, classification, tracking, inspection, measurement, visualization, or guided user experiences. We design the model and application together so the output is useful in context.
Add a focused AI capability to software you already use, or adapt an existing model through prompting, retrieval, fine-tuning, or domain-specific evaluation. We choose the least complex approach that meets the requirement.
Technology approach
A Modern AI Stack, Selected by Fit
We work across leading commercial models, open-source models, retrieval systems, machine learning frameworks, and managed cloud platforms. The stack is selected against your quality, privacy, latency, cost, and ownership requirements; no project needs every tool listed below.
Foundation and Multimodal Models
OpenAI, Anthropic Claude, Google Gemini, and open-source models available through Hugging Face. We compare models on the client's real task instead of choosing by benchmark reputation alone.
Retrieval and Knowledge Systems
LlamaIndex or focused retrieval pipelines with Pinecone, Weaviate, pgvector, PostgreSQL, and approved document or object storage. The design depends on scale, permissions, retrieval quality, and operational cost.
Machine Learning and Computer Vision
PyTorch, scikit-learn, XGBoost, OpenCV, and task-specific open-source models where they provide a better fit than a general foundation model.
Cloud AI and Deployment
AWS Bedrock, Google Vertex AI, Microsoft Foundry, Hugging Face Inference Endpoints, containers, and standard cloud infrastructure selected around the client's environment and governance needs.
Evaluation and Observability
Task-specific evaluation datasets, human review, automated quality checks, tracing, latency and cost monitoring, and tools such as MLflow, LangSmith, or Arize Phoenix when they fit the deployed system.
Where custom AI creates value
Use AI where better judgment, prediction, or interpretation matters
The strongest custom AI use cases have a defined user, a repeatable decision or task, relevant data, and a measurable standard for success. Common examples include:
A good first AI use case usually has:
- A costly or slow decision, interpretation, or matching task
- Enough relevant data or a realistic way to obtain it
- A user who can act on the output
- A measurable definition of useful, accurate, and safe behavior
- A path to integrate the result into an existing product or process
Reliable by design
A custom model is only useful when people can rely on the system around it
Production AI depends on more than model accuracy. Data quality, evaluation, permissions, latency, cost, fallback behavior, monitoring, and user experience all affect whether the system earns trust and remains useful after launch.
Data Readiness
We assess data quality, coverage, access, labeling, privacy, and known limitations before committing to an approach.
Measurable Evaluation
The system is tested against representative cases and business-defined acceptance criteria, not only a polished demonstration.
Human Oversight
Higher-impact outputs can be reviewed, approved, corrected, or rejected before they influence a consequential action.
Security and Privacy
Access, storage, model providers, logging, and data flows are selected around the sensitivity of the use case.
Fallback Behavior
Low-confidence, missing-data, and exception cases are handled explicitly rather than hidden behind a confident response.
Monitoring and Improvement
Quality, errors, drift, latency, usage, and cost can be monitored so the system can be maintained and improved.
Technology principle: Model selection is an engineering decision. We compare candidates against representative data and requirements for quality, safety, latency, cost, privacy, and operational fit before standardizing the production stack.
How we deliver
From a defined problem to monitored AI software
Every project moves through the same decision-led process. This keeps the technical work tied to a practical outcome and gives stakeholders clear review points before the scope expands.
Business and Use-Case Discovery
We define the user, current process, business constraint, desired outcome, available data, and the decision or experience the AI must improve.
Business and Use-Case Discovery
We define the user, current process, business constraint, desired outcome, available data, and the decision or experience the AI must improve.
Feasibility and Data Assessment
We review data access and quality, technical constraints, integration requirements, risk, privacy, and whether AI is the right approach.
Feasibility and Data Assessment
We review data access and quality, technical constraints, integration requirements, risk, privacy, and whether AI is the right approach.
Solution Design and Success Criteria
We design the model or retrieval approach, application flow, APIs, interfaces, oversight, and measurable acceptance criteria.
Solution Design and Success Criteria
We design the model or retrieval approach, application flow, APIs, interfaces, oversight, and measurable acceptance criteria.
Prototype and Evaluation
We test the highest-risk assumptions with representative data and realistic cases before committing to a larger production build.
Prototype and Evaluation
We test the highest-risk assumptions with representative data and realistic cases before committing to a larger production build.
Product Integration and Deployment
We build the supporting software, connect approved systems, deploy the AI capability, document it, and prepare users for launch.
Product Integration and Deployment
We build the supporting software, connect approved systems, deploy the AI capability, document it, and prepare users for launch.
Monitoring, Support, and Improvement
We track real-world behavior, address failure cases, refine quality and cost, and expand the system when the evidence supports it.
Monitoring, Support, and Improvement
We track real-world behavior, address failure cases, refine quality and cost, and expand the system when the evidence supports it.
Commercial principle: Discovery should produce a clear recommendation, not a commitment to the largest possible build. A phased project reduces technical risk and gives the client useful evidence before expanding scope.
Selected work
Different AI problems, built into usable software
SensViz has worked on AI-powered products across recommendations, language, forecasting, computer vision, and interactive user experiences.
AI grant recommendation platformA platform using large language models, recommendation logic, and vector search to help users discover grant opportunities relevant to their profile and needs.
Relevant capability: recommendation systems, retrieval, language AI, contextual matching
AI-assisted cover letter platformA writing experience that uses language models and natural language processing to help users create more relevant cover letters from their background and target role.
Relevant capability: language intelligence, structured generation, user context
Computer vision piano learningAn interactive learning product using computer vision to interpret hand movement and support real-time visual guidance during piano practice.
Relevant capability: computer vision, real-time interaction, education technology
Water quality forecastingAn AI-enabled water monitoring concept using time-series forecasting to help interpret water-quality changes and support clearer monitoring insights.
Relevant capability: forecasting, sensor data, decision support
Visual flooring previewA computer vision and augmented-reality experience that helps users preview flooring options in a room image before making a selection.
Relevant capability: computer vision, visualization, interactive product experience
Why SensViz
AI engineering connected to product and business reality
Problem-First Scoping
We define the business requirement and success criteria before choosing a model, framework, or vendor.
AI and Software in One Team
Models, data, APIs, interfaces, integrations, cloud deployment, and product experience are designed as one system.
Human-Centered Decisions
The AI supports real users with clear controls, understandable outputs, and appropriate oversight.
Production-Minded Delivery
Evaluation, fallback behavior, monitoring, documentation, and maintainability are included in the engineering approach.
Technology Without Lock-In
We select tools around accuracy, privacy, cost, portability, and maintenance rather than committing every client to one vendor.
Clear Ownership and Support
Code, infrastructure, data responsibilities, model access, documentation, and post-launch support are defined in the project agreement.
Choose the right delivery path
Related AI and software services
A visitor may arrive with the right business problem but the wrong service label. These links lead to the page that best matches the work you need.
Frequently asked questions
Custom AI development is the design and engineering of an AI system around a specific business problem, dataset, product, or operational requirement. It can include models, retrieval, APIs, interfaces, integrations, deployment, evaluation, and ongoing monitoring.
An off-the-shelf tool serves a common use case with limited customization. A custom solution is designed around your data, rules, users, accuracy requirements, integrations, and ownership needs. We recommend custom development only when that additional fit creates meaningful value.
We work on recommendation and personalization systems, predictive models, multimodal AI, private knowledge and RAG systems, computer vision applications, and focused AI features for existing software.
Not always. The requirement depends on the use case and technical approach. During discovery, we assess available data, quality, access, gaps, privacy, and whether commercial models, retrieval, rules, synthetic data, or additional collection can support a reliable first version.
Yes. We can assess the current architecture and add a focused AI capability through APIs, retrieval, model services, custom components, or supporting interfaces. Integration is planned in stages to reduce disruption to the live product.
We define representative test cases and acceptance criteria around the real task. Depending on the system, evaluation may cover accuracy, relevance, false positives, consistency, latency, cost, safety, and how low-confidence or exception cases are handled.
The timeline depends on data readiness, technical risk, integrations, model requirements, user experience, and deployment scope. After discovery, we provide a phased plan that tests the highest-risk assumptions before expanding into a production build and ongoing support.
Start with the problem
Build the AI capability your business can actually use
Tell us about the decision, prediction, content, image, or data problem you want to improve. We will assess the use case, data, risks, integrations, and practical delivery options, then recommend whether custom AI, conventional software, automation, or another approach is the best fit.
Clear recommendations, even when a simpler solution is the better answer.
