SnapApp Glossary

SnapApp

A low-code / no-code application platform that supports Gov Studio and Health Studio, enabling rapid development, deployment, and orchestration of domain-specific applications.

Low-code (or No-code)

A development paradigm enabling application construction with minimal hand coding, often via visual tools, drag-and-drop components, and declarative logic.

Workflow

A predefined sequence of tasks or steps through which data or logic flows; workflows route documents, interactions, and approvals in SnapApp.

Human-in-the-Loop (HITL)

An AI or automation approach in which human oversight or intervention is included at certain stages to validate or correct outputs.

Trigger / Event

A signal or condition (e.g. form submission, data update) that causes a workflow or action in the system to run.

Action / Task

A discrete operation (e.g. data extraction, notification, routing) within a workflow triggered by an event.

Integration / Connector

A prebuilt interface module enabling SnapApp to connect with external systems (e.g. APIs, databases, EHRs).

API (Application Programming Interface)

A formal set of endpoints and protocols through which external systems interact with SnapApp (fetch, push, trigger).

Document AI

Techniques (OCR, NLP, classification) used to extract structured data and meaning from scanned or electronic documents.

Intent Recognition

In conversational or form-driven flows, the process of classifying the user’s intended goal or request based on their input.

ADA-compliant Messaging

Communications that meet accessibility standards so that all users (including those using assistive tech) can interact effectively.

Generative AI

AI models (e.g. large language models) that produce novel content (text, summaries) from learned patterns.

Dashboard / Analytics

Visual or tabular interfaces summarizing operational or performance data for monitoring and insights.

Multilingual Support

The capability for the system to support multiple languages for UI, content, validation, and input.

Scalability

The ability of the platform to handle growth in users, data, or load without performance degradation.

Real-time Data

Data that is processed or updated with minimal delay, enabling up-to-date information and actions.

AI-powered Insights

Automatically generated recommendations, alerts, or findings produced by applying AI to collected data.

Gov Studio Terms

Gov Studio

A citizen experience / government domain application built on SnapApp, leveraging AI and document automation to modernize public services.

Citizen Interaction

Any touchpoint (chatbot, form, messaging) through which a citizen engages government services via Gov Studio.

Certificate Processing

A use case in Gov Studio where scanned government certificates (e.g. birth, death, marriage) are processed via Document AI to extract fields and route them.

Form Routing

Logic or mapping that determines where completed forms should be sent (which agency, team, or step) based on content or validation.

Intent-based Interaction

A design in which the system infers what the user wants and dynamically guides the conversation or form flow.

Configuration / Rule Engine

A subsystem allowing nontechnical users to set rules (e.g. “if field X = Y, route to team Z”) without coding.

Record Preservation

Ensuring processed documents and data are stored securely, in durable formats, and remain accessible long-term.

Automation Cut Rate

A metric (e.g. “manual entry reduced by 70%”) indicating how much manual work the automation replaced.

Health Studio Terms

Health Studio

A health / clinical research application built on SnapApp tailored for clinical trials, wearable data, participant engagement, and AI-enabled insights.

Wearable Data / Sensor Data

Data collected from wearable devices (e.g. activity trackers, sleep monitors) capturing metrics like steps, heart rate, or motion.

EHR (Electronic Health Record) Integration

Connecting with clinical databases or systems so participant medical or clinical data can be accessed or combined with trial data.

eConsent

A digital process for obtaining informed consent from study participants (via web or mobile) before enrollment.

Eligibility Screening

Automated or semi-automated logic to determine whether a participant meets inclusion / exclusion criteria for a study.

Remote Patient Monitoring

Tracking participant health metrics, compliance, or behaviors outside clinic settings via sensors, apps, or self-report.

Participant Engagement

Tools (notifications, surveys, reminders, messaging) aimed at keeping trial participants active and compliant over time.

REDCap Integration

Integration with REDCap (a widely used research data capture system) so that trial data and metadata can sync or push between systems.

Hybrid Trial

A clinical trial model combining remote, in-clinic, and digital elements, enabled by Health Studio’s flexible architecture.

Multilingual Portals (Health)

Participant-facing interfaces in multiple languages to support diverse populations in trials.

Research-grade Data

Data that meets standards of accuracy, completeness, validation, and traceability suitable for regulatory or publication use.

BlueVector AI Terms

BlueVector AI

A Google Cloud–focused consultancy delivering AI-powered, low-code solutions tailored for government and healthcare clients.

Reusable Components

Prebuilt modules, templates, or libraries developed by BlueVector.ai to accelerate domain application development.

Google Cloud Platform (GCP)

The cloud infrastructure on which BlueVector.ai builds, hosts, and deploys AI and application services.

AI Solution Accelerator

A packaged set of tools, frameworks, or modules that speed or standardize AI-enabled app development.

Domain Template

Predefined blueprint for a solution (e.g. health trial, government service) that can be customized rather than built from scratch.

AI Governance

Policies, oversight, ethical frameworks, or audit mechanisms ensuring AI systems are safe, fair, and compliant.

Observability / Monitoring

The ability to trace, log, and audit state, metrics, and decisions of AI systems to detect drift or bias.

Model Lifecycle / MLOps

The practices and pipeline to develop, deploy, monitor, retrain, and maintain machine learning models in production.

Deployment Pipeline

The sequence of automated steps (testing, validation, deployment) to move code and models from dev to production.

Domain-Aware AI

AI models or modules that incorporate domain-specific constraints, ontologies or business rules to improve relevance and correctness.

Component Library

A curated collection of UI, integration, AI, or workflow modules maintained by BlueVector.ai for reuse across projects.

Solution Catalog

A portfolio or listing of predesigned solutions, templates, and modules available to clients to accelerate delivery.

AI / Data / Machine Learning Terms

Artificial Intelligence (AI)

The field of computer science that builds systems capable of tasks normally requiring human intelligence (e.g. language, reasoning).

Machine Learning (ML)

A subset of AI where models learn from data rather than fixed code to make predictions or decisions.

Deep Learning

A subset of ML using neural networks with many layers to capture hierarchical features (e.g. in text, images).

Neural Network

A computational model of interconnected nodes (neurons) that transforms input layer through hidden layers to output.

Prompt / Prompt Engineering

In generative models: crafting input prompts to elicit desired behavior or output from the model.

Fine-tuning

The process of adapting a pretrained model on domain-specific data to improve performance for a given task.

Inference

Using a trained model to make predictions or generate output from new, unseen inputs.

Training / Model Training

The process by which a machine learning model adjusts internal parameters based on labeled data to reduce error.

Overfitting

When a model learns noise or idiosyncrasies in training data and performs poorly on new data.

Bias / Algorithmic Bias

Systematic error or skew in model outputs due to unrepresentative training data or flawed assumptions.

Feature / Feature Engineering

The process of selecting, transforming, or creating input variables (features) for model learning.

Embedding / Vector Embedding

Representing items (words, documents, users) as numerical vectors so similarity or distance captures meaning.

Vector Database / Vector Store

A database optimized to store vector embeddings and perform efficient similarity searches (e.g. nearest neighbor queries).

Retrieval-Augmented Generation (RAG)

A method combining retrieval of relevant documents (from a vector store) with generative models to produce informed text outputs.

Transfer Learning

Reusing parts of pretrained models or learned representations on new but related tasks to save resources or improve performance.

Model Drift

The phenomenon where a model’s predictive performance degrades over time as data distributions shift.

Explainability / Interpretability

Methods or tools that help users understand how or why a model made specific predictions or decisions.

Guardrails

Constraints or safety mechanisms (validation, thresholds, filters) embedded in AI systems to prevent harmful or invalid outputs.

Ensemble

A method combining multiple models or approaches to produce predictions more robust than any individual model.

Cross-validation

A method of partitioning data into subsets to more reliably evaluate model performance (e.g. k-fold cross-validation).