Glossary

The engineering glossary.

Short, plain-English definitions of the terms that come up when you plan an AI, SaaS or cloud build, and how RapidStackLab applies each one in practice.

AI & agents

AI agent#: An AI agent is software that uses a large language model to decide what to do next, calls tools (APIs, databases, other services) to do it, and checks the result before moving on. Unlike a chatbot, it completes a task end to end.; How we use it: We build agents with deterministic tool use, typed outputs and a human approval step wherever a wrong action would be costly. AI agent development
Multi-agent system#: A multi-agent system splits one job across several specialised agents, for example a supervisor that routes work, a retrieval agent that finds facts and a tool agent that takes actions. Splitting the job makes each step easier to test, monitor and replace.; How we use it: Our default pattern is a supervisor graph: supervisor → retrieval worker or human gate → tool worker, with state checkpointed to PostgreSQL. RapidCore AI architecture
Retrieval-augmented generation (RAG)#: Retrieval-augmented generation (RAG) means the model answers from documents retrieved at query time, such as policies, contracts or product data, instead of from memory alone. It keeps answers current and lets every answer cite its source.; How we use it: We build enterprise RAG with hybrid search and pgvector, and measure retrieval quality before tuning prompts. AI application development
LangGraph#: LangGraph is an open-source framework for building stateful agents as graphs: nodes are steps (model calls, tools, people) and edges decide what runs next. It supports checkpoints, retries and pauses for human review.; How we use it: LangGraph is our default runtime for multi-agent workflows that must survive failures and pause for approvals. AI agent development
Google ADK (Agent Development Kit)#: Google ADK is Google's open-source framework for building, evaluating and deploying AI agents, available in Python, TypeScript, Go, Java and Kotlin. ADK 2.0 (generally available from May 2026) runs agents, tools and functions as nodes in a workflow graph that can route, run steps in parallel and pause for human input.; How we use it: We use ADK for agents that deploy to Google Cloud, with function tools over pgvector, guardrail callbacks and adk eval suites in CI. How to build a RAG agent with Google ADK
Model Context Protocol (MCP)#: The Model Context Protocol (MCP) is an open standard for connecting AI agents to tools and data. An MCP server exposes tools, resources and prompts; an agent connects as a client, discovers them and calls them, so one integration works across agent frameworks.; How we use it: We expose internal systems as MCP servers with a narrow tool list per agent, so the same tools serve ADK, LangGraph and Claude agents. ADK multi-agent workflows
Agent2Agent protocol (A2A)#: Agent2Agent (A2A) is an open protocol that lets agents built with different frameworks discover each other through an agent card and exchange tasks over HTTP. It is for agent-to-agent calls, where MCP is for agent-to-tool calls.; How we use it: We use A2A when a client's agents live in separate services or frameworks and need to hand work to each other. ADK multi-agent workflows
Human-in-the-loop#: Human-in-the-loop is a design where an agent pauses and asks a person to approve, edit or reject an action before it continues. It is how teams use automation for refunds, contracts or customer messages without giving up control.; How we use it: Approval gates are part of every agent we ship that can move money, change records or contact customers.
Hallucination guardrails#: Hallucination guardrails are checks that stop a model from presenting invented facts: grounding answers in retrieved sources, validating structured outputs, refusing when evidence is missing and evaluating answers against test sets before release.; How we use it: We ship evaluation suites with every RAG system, so accuracy is measured rather than assumed.
Vector database (pgvector)#: A vector database stores embeddings, numeric fingerprints of text or images, so a system can find the most similar documents to a question. pgvector adds this to PostgreSQL, so vectors live next to the rest of the application data.; How we use it: We default to PostgreSQL with pgvector, which keeps one database to secure, back up and monitor.
Workflow automation#: Workflow automation connects the tools a team already uses, such as CRM, email, payments and spreadsheets, so that routine steps run on their own when a trigger fires. Tools like n8n and Make handle the wiring; AI steps handle reading and deciding.; How we use it: We use n8n or Make for integrations your team can edit, and Temporal when a workflow must be durable. Automation & workflow engineering

Product engineering

Production MVP#: A production MVP is the smallest version of a product that real customers can use safely: authentication, a database, payments or core workflows, monitoring and a deployment pipeline. It is built to be extended, not thrown away.; How we use it: Our MVP Launch engagements deliver a production MVP in 6–10 weeks, from $12,000, with 100% code ownership. Pricing
Multi-tenant SaaS#: Multi-tenant SaaS serves many customer organisations from one application and database while keeping each customer's data isolated. Done well, it lowers hosting cost per customer and lets every customer get new features at once.; How we use it: We design tenancy, role-based access and billing into the data model from the first sprint. SaaS platform development
Code and IP ownership#: Code and IP ownership means the client, not the agency, legally owns the source code, designs and documentation, and controls the repository and cloud accounts. It decides whether you can change vendors or raise funding without friction.; How we use it: Every RapidStackLab engagement includes 100% code ownership: full IP transfer and your own GitHub repository from the first commit.

Cloud & reliability

Managed SRE#: Managed site reliability engineering (SRE) is an ongoing service where an outside team keeps your production systems healthy: monitoring, incident response, scaling, patching, cost control and recovery drills, measured against an uptime target.; How we use it: Our Managed SRE & CloudOps retainers run from $4,000 to $10,000 a month with 24/7 incident response. Cloud architecture
Uptime SLA#: An uptime SLA (service level agreement) is a written promise of how available a system will be. 99.9% allows about 8.8 hours of downtime a year; 99.99% allows about 53 minutes.; How we use it: Managed SRE retainers carry a 99.99% platform uptime SLA.
CI/CD pipeline#: A CI/CD pipeline automatically builds, tests, security-scans and deploys every code change. Continuous integration catches defects within minutes; continuous delivery makes releases small, frequent and reversible.; How we use it: Every build we run has CI/CD from day one, with working software demonstrated every week. DevOps & CI/CD pipelines
Infrastructure as code#: Infrastructure as code (IaC) describes servers, networks and databases in versioned files, typically Terraform, instead of console clicks. Environments become reproducible, reviewable and quick to rebuild after a failure.; How we use it: We manage AWS, GCP and Azure environments with Terraform and Kubernetes, reviewed like application code.

Engagement models

Global Capability Center (GCC)#: A Global Capability Center (GCC) is an offshore team owned or controlled by the parent company, usually in India, that builds and runs technology for the business. Companies set one up for cost efficiency, talent depth and round-the-clock delivery.; How we use it: We set up AI-native capability pods in India for US, UK and UAE companies, at 40–50% of the cost of a US agency. India capability pods
Build-Operate-Transfer (BOT)#: Build-Operate-Transfer (BOT) is a way to set up an offshore team: a partner builds the team, operates it for an agreed period, then transfers the people, processes and assets to the client's own entity.; How we use it: Our GCC engagements follow BOT: build in months 1–3, operate in months 3–18, transfer in months 18–24. India capability pods
Dedicated engineering pod#: A dedicated engineering pod is a small, stable team of senior engineers assigned to one client full time. It works inside the client's tools and rituals and builds up product knowledge over months, unlike project staffing that rotates people.; How we use it: Our Dedicated Pod engagements run $12,000–$25,000 a month for 2–5 senior engineers, with 4 hours of daily time-zone overlap. Pricing
Fixed-scope engagement#: A fixed-scope engagement agrees the deliverables, timeline and price before work starts, with payments tied to milestones. It suits launches with a clear specification; changes go through a documented change request.; How we use it: Our fixed-scope programs run 6–20 weeks with milestone-based delivery and acceptance criteria.
Time & material engagement#: A time & material engagement bills for the engineering time actually used, sprint by sprint. It suits products whose requirements are still changing, because scope can move each sprint without renegotiating a contract.; How we use it: We run time & material work in two-week sprints with weekly progress reports and per-sprint cost tracking.

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