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AI Software Development Glossary
Terms like "AI-native," "AI-powered," and "agentic AI" get thrown around a lot — often meaning different things depending on who's using them. This glossary defines the terms that actually matter when you're evaluating an AI development company, in plain English, without the marketing spin.
SECTION 1
AI Development Approaches & Company Types
The terms vendors use to describe how — and how much — AI shows up in their actual delivery work, as opposed to their marketing.
AI-Native Software Development
Software development built from the ground up with AI embedded in the engineering process itself, not bolted on afterward. In practice, this means AI-assisted design, code generation, and QA are part of how a project moves from spec to production — not a separate add-on step. See BlastAsia's AI-native development approach.
AI-Powered Software Development
A closely related term describing delivery that actively uses AI tools throughout the work — writing code faster, testing more thoroughly, catching issues earlier. A company can be AI-powered without being fully AI-native, if AI tools are used on top of an otherwise traditional process rather than built into it from the start.
AI Development Company
A company that builds custom software and integrates AI capabilities into a client's own product — distinct from an "AI company" that sells its own AI product or platform. BlastAsia is an AI development company: AI features get built into what you're shipping, not sold to you as BlastAsia's own tool. See BlastAsia's solutions overview.
AI App Development (Company)
The same AI development company concept, applied specifically to mobile or web applications — a company that builds AI features into your app, rather than selling you a pre-built AI app of its own.
Custom AI Development
Building AI functionality — generative AI, document automation, computer vision, and more — tailored to a specific business's product, data, and workflows, rather than a one-size-fits-all AI tool. See BlastAsia's AI development services.
Enterprise AI Development
AI development built to the compliance, security, and scale requirements of larger organizations — HIPAA, PCI-DSS, GDPR, and similar standards — rather than a lightweight, consumer-grade build. Worth asking any vendor about directly if your organization is regulated.
AI Integration
Connecting AI capabilities into an organization's existing systems and workflows, as opposed to building a brand-new AI-first product from scratch. Most enterprise AI work is integration work — making AI useful inside tools people already use — not a standalone AI product launch.
AI Integration Company
The company-level version of AI integration above — a development partner whose core service is connecting AI models and tools into a client's existing software, rather than building and selling AI products of its own.
Private AI Development
AI development that runs on infrastructure a business controls — its own cloud environment or on-premises — instead of a shared third-party AI service. Typically chosen by organizations with strict data-privacy or regulatory requirements that rule out sending data to an external AI provider. See BlastAsia's Private AI Development.
Generative AI Development
Building software that uses generative AI models to create content, automate document work, or power conversational features — applied inside a client's own product, not sold as a standalone AI chatbot or writing tool.
AI Transformation
The broader, organization-wide process of adopting AI across a company's products, operations, and workflows over time — as opposed to a single AI feature or one-off project. Usually starts with a strategy phase (see AI Transformation Strategy Consulting, below) before any development work begins.
Responsible AI / AI Governance
The policies and practices an organization puts in place to make sure its AI systems are used safely, fairly, and in compliance with relevant regulations — increasingly a requirement for enterprise AI deployments, not just a nice-to-have.
AI Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated — a key risk to manage in any AI feature that generates content or answers, especially in regulated industries. Well-designed AI systems build in guardrails, such as retrieval-augmented generation (below) or deterministic logic for critical steps, specifically to reduce this risk.
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Delivery Models & Engagement Types
How the actual working relationship with a development partner gets structured, once you've chosen one.
Custom Software Development
Software built specifically for one organization's needs, as opposed to off-the-shelf or pre-built software. The umbrella category that AI-native, AI-powered, xDD, and Turnkey (below) all sit under. See BlastAsia's solutions overview.
xDD (Continuous Delivery)
BlastAsia's continuous delivery model — an AI-native team that ships working software every 2-week sprint, with a first working Version 1 in 30 days, powered by the Xamun Software Factory. Best fit for ongoing product development rather than a single, fixed-scope build. See xDD Services.
Turnkey Software Development
A fixed-price, fixed-scope delivery model for projects with a clearly defined scope, typically completed in 2-4 weeks — as opposed to xDD's continuous, sprint-based model for ongoing work. See Turnkey Software Development.
Time-and-Materials vs. Fixed-Price
Two common ways an engagement gets billed. Time-and-materials pays for actual hours worked, useful when scope may change; fixed-price (like BlastAsia's Turnkey model, above) sets one price for a clearly defined scope, useful when requirements are already locked in.
Dedicated Developer Team (Staff Augmentation)
An outsourced engineering team embedded directly in a client's own project and processes, functioning as an extension of the in-house team rather than a separate delivery unit. A more traditional engagement model, still useful for organizations that want direct day-to-day control. See Dedicated Developer Teams.
Outsourced Software Development
Contracting an external company to handle some or all of a software project, rather than building and staffing an internal team. AI development outsourcing (below) is the AI-specific version of this broader practice.
Nearshore Software Development
Outsourcing software development to a country with a similar or overlapping time zone, as distinct from offshore development, which typically prioritizes cost savings over geographic proximity.
AI Development Outsourcing
Contracting an external partner to handle AI-embedded software development — often combining dedicated engineering capacity with AI-native delivery practices — rather than hiring and building an in-house AI team from scratch.
AI Transformation Strategy Consulting
An advisory engagement to help an organization identify where and how AI can improve its existing software, operations, or product roadmap, before — or alongside — any actual development work begins. See AI Transformation Strategy Consulting.
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Core AI Concepts, in Plain English
The underlying technology terms, explained without the jargon — useful context whether or not you're the technical person in the room.
Large Language Model (LLM)
The type of AI model — like the ones behind ChatGPT, Claude, and similar tools — trained on massive amounts of text and capable of understanding and generating human language. The technology underlying most generative AI features today.
Natural Language Processing (NLP)
The branch of AI focused on understanding and working with human language — the technology behind chatbots, sentiment analysis, and document summarization. Large language models (above) are the most advanced current form of NLP.
Generative AI
AI that creates new content — text, images, code, or structured data — rather than simply classifying or analyzing existing content. The foundation for AI features like document drafting, chatbots, and automated summarization.
AI Agent / Agentic AI
An AI system that can take multi-step actions toward a goal — planning, using tools, and completing a task with limited human intervention at each step — rather than just answering a single question and stopping.
Retrieval-Augmented Generation (RAG)
A technique that lets an AI model pull in relevant information from a company's own documents or databases before generating a response, so answers are grounded in real, current data instead of only the model's original training data.
Machine Learning
A broader category of AI where a system learns patterns from data rather than being explicitly programmed with rules — the technology behind predictive analytics, recommendation systems, and fraud detection, among others.
Computer Vision
AI that interprets and extracts information from images or video — used for tasks like document scanning and OCR, quality inspection, and object detection.
Optical Character Recognition (OCR)
AI technology that reads text from scanned documents or images and converts it into usable digital data — commonly used in document automation, freeing staff from manual data entry. A specific application of computer vision, above.
AI Model Fine-Tuning
Adjusting a pre-trained AI model with additional, more specific data so it performs better on a particular task or domain — as opposed to building a model completely from scratch, which is far more resource-intensive and rarely necessary for most business applications.
Human-in-the-Loop
An AI system design where a person reviews or approves the AI's output before it takes effect, rather than letting the AI act fully autonomously. A common and often necessary safeguard for AI features used in regulated or high-stakes workflows.
Prompt Engineering
The practice of writing and structuring the instructions given to an AI model to get more accurate, consistent, and useful output — an increasingly important skill in building reliable AI-native features.
SECTION 4
Software Delivery Concepts
Terms that apply to software delivery generally, not just the AI parts of it — still worth knowing when comparing how vendors describe their process.
MVP / Version 1
The first working version of a product, built with enough real functionality to be genuinely usable — not just a prototype or proof of concept. BlastAsia's xDD model is built around delivering an actual Version 1 in 30 days.
Proof of Concept (POC)
A small-scale build used to test whether an idea or technical approach actually works, before committing to full development. Narrower in scope than an MVP, above — a POC proves feasibility, an MVP proves the product is usable by real users.
Legacy System Modernization
Updating or rebuilding an older software system — often one that's become hard to maintain or extend — to run on current technology and support ongoing development, without necessarily starting over from scratch. See App Modernization.
Technical Debt
The accumulated cost of past shortcuts, outdated architecture, or deferred fixes in a codebase. The more technical debt a system carries, the slower and riskier future changes become — a key reason modernization work gets prioritized.
DevOps
The practice of combining software development and IT operations — automating builds, testing, and deployment — so new code reaches production reliably and frequently, rather than through slow, manual releases. See DevOps.
CI/CD (Continuous Integration / Continuous Delivery)
The automated process of merging code changes frequently (continuous integration) and getting them safely into production (continuous delivery), rather than shipping in large, infrequent releases. A core practice behind BlastAsia's DevOps services and the 2-week sprint cadence used in xDD, above.
Data Governance & Compliance
The policies and controls that determine how an organization's data is collected, stored, secured, and used — including regulatory requirements like HIPAA, PCI-DSS, and GDPR. A key consideration in any enterprise AI development or private AI development project, since AI systems often touch sensitive data directly.
Software Product Engineering
The end-to-end discipline of designing, building, testing, and maintaining a software product, as distinct from one-off custom scripting or narrow feature work. BlastAsia's 25 years of experience is rooted in this discipline. See About BlastAsia.
