Perplexity AI: What is it and why is it important?

What is Perplexity AI?

Perplexity AI has rapidly emerged as a challenger to traditional search engine dominance, attracting significant investment from high-profile figures such as Jeff Bezos and NVIDIA. Positioned not just as an evolution of search but as a reimagining of it, Perplexity delivers synthesized, conversational answers directly to user queries. By April 2026, the company reported more than 100 million monthly active users and processes over one billion queries per month, with annualised recurring revenue crossing roughly $450 million and a valuation near $20 billion.

The company’s strategic ambition is to redefine how individuals access and process online information. Unlike conventional search engines, which serve as information pointers, Perplexity aims to provide naturally worded responses that are immediately verifiable through transparent, integrated citations. This approach directly addresses the growing demand for efficiency and summarised knowledge. A critical differentiation point is the platform’s core value proposition:

“Providing a tool for reducing the risk of misinformation.”In the current environment, Large Language Models (LLMs) are notorious for “hallucinations”, generating confident yet fictitious information. Perplexity’s mandatory inclusion of real-time citations tries to position the tool as the anti-hallucination mechanism for professional users. Perplexity is essentially trying to sell trust and factuality. In this article we explain all about Perplexity AI: what it is, which models power it, what it can do, what it costs, and where it is heading in 2026.

Retrieval-Augmented Generation (RAG)

The foundational technology enabling Perplexity’s promise of conversational accuracy is Retrieval-Augmented Generation (RAG). RAG is a crucial technique that allows LLMs to access, retrieve, and incorporate new information from external, specified documents or web sources before generating a response. This process ensures that the LLM does not rely solely on its static, pre-existing training data, allowing it to leverage domain-specific, proprietary, or highly updated information in real time. The function of RAG, as described by experts, is to blend the traditional LLM generative process with a web search or document look-up, thereby helping the model “stick to the facts”.

Perplexity’s RAG-driven process

Perplexity executes its search synthesis through a sophisticated multi-step sequence, where RAG plays the pivotal role:

1. Processing a question: The system initiates the process by using natural language processing (NLP) to break the user query down into individual tokens (adjectives, nouns, verbs). It then applies semantic rules to detect more complex concepts, entities, and the user’s overall intent. This deep semantic understanding is necessary to avoid misinterpretations and ensure the search addresses the true meaning behind the question.
2. Searching the web: After understanding the query, Perplexity conducts a semantic search across the internet, seeking relevant articles, websites, and research papers. Crucially, this search goes beyond simple keyword matching. The system assesses each potential source based on traits such as credibility and quality to determine which sources are suitable for generating the final response.
3. Source assessment and response generation: This is where the RAG loop closes. The LLM generates a naturally worded, synthesized response, explicitly citing the verified and curated sources identified in the previous step. By forcing the model to generate responses only from a specified, high-quality set of retrieved documents, the architecture drastically reduces the risk of factual errors, such as describing nonexistent policies or recommending non-existent legal cases.
4. Anticipating follow-up questions: To support continuous interaction, Perplexity stores inputs and responses to form a conversation history, equipping it with contextual memory. This allows users to ask subsequent questions without having to reiterate the original query, leading to more personalised and fluid interactions.

Strategic implications of RAG

The implementation of RAG provides Perplexity with profound strategic advantages. Beyond enhancing accuracy by mitigating hallucinations, RAG offers a substantial operational cost benefit. LLM retraining is notoriously expensive and computation-heavy. By enabling the model to stay current by pulling relevant, real-time text from databases and web sources, Perplexity conserves significant computational and financial resources. This ability to maintain factual currency through efficient RAG retrieval, rather than continuous massive foundation model updates, creates a business model that is potentially more capital-efficient than those of rivals reliant purely on enormous, frequently retrained models.

Furthermore, the explicit practice of assessing sources for “credibility and quality” before synthesis means Perplexity operates as a knowledge curator, not just an indexer. Traditional search often relies heavily on domain authority and traffic as ranking signals. Perplexity’s RAG layer adds a critical quality control step, establishing the platform as a verified knowledge broker for the professional market. The ability to include verifiable sources in responses is paramount, providing greater transparency and allowing users to cross-check retrieved content for accuracy and relevance.

Which AI models power Perplexity AI?

Perplexity is not a single-model product. It is a multi-model orchestration layer in the AI value chain, sitting between foundation model providers and the end user. The platform combines its own in-house Sonar family with a rotating roster of frontier models from OpenAI, Anthropic, Google, xAI, and Moonshot.

Sonar: Perplexity’s in-house default model

Sonar is Perplexity’s proprietary model family and the default engine for every quick search on the Free tier. It is built on top of Llama 3.3 70B and has been further post-trained specifically to optimise answer factuality and readability for Perplexity’s search mode. Running on Cerebras inference infrastructure, Sonar achieves a generation speed of roughly 1,200 tokens per second, which makes responses feel near-instant. Sonar comes in two variants: a base model for lightweight grounded search and Sonar Pro for deeper multi-source retrieval with follow-up queries. The Sonar family also powers the public Perplexity API.

Frontier models inside Pro and Max

On the Pro tier ($20 per month), users can manually choose between Sonar and a selection of leading frontier models. As of May 2026, the rotating Pro line-up typically includes:

• GPT-5.1 (OpenAI), strong for complex reasoning and structured outputs
• Claude Sonnet 4.5 and Claude Opus 4.5 (Anthropic), strong for long-context analysis, legal, and academic writing
• Gemini 3 Pro (Google), strong for multimodal tasks and integration with Google data
• Grok 4.1 (xAI), strong for current-event reasoning
• Kimi K2 Thinking (Moonshot), strong for long-context reasoning

The Max tier ($200 per month) unlocks the newest premium variants such as GPT-5.2, Claude Opus 4.6, and reasoning-optimised versions like o3-pro and Opus 4.5 Thinking, along with priority access to whatever frontier model lands next.

Model Council: querying multiple models at once

Launched on 5 February 2026 as a Max-exclusive feature, Model Council dispatches a single user query simultaneously to three frontier models (typically Claude Opus 4.6, GPT-5.2, and Gemini 3 Pro) and surfaces all three answers side by side. The feature exists because different frontier models still disagree meaningfully on technical, creative, and analytical questions, and seeing three independent answers is a fast way to spot where confidence is justified and where it is not.

R1 1776: Perplexity’s open-source reasoning model

In February 2025, Perplexity released R1 1776, an open-source 671B-parameter reasoning model post-trained from DeepSeek-R1 to remove CCP-aligned censorship while preserving the base model’s math and coding capabilities. The team used a multilingual dataset of around 40,000 prompts covering roughly 300 sensitive topics, fine-tuned with NVIDIA NeMo 2.0, and released the weights under MIT license on Hugging Face. R1 1776 is accessible in the Perplexity product, through the Sonar API, and directly via Hugging Face for self-hosting. It is the clearest signal that Perplexity sees itself as both a consumer of foundation models and a contributor of specialised ones.

Perplexity’s role in the AI value chain

Stepping back, Perplexity occupies the application and orchestration layer of the AI value chain. It does not own a foundation model at frontier scale, but it owns the retrieval system, the citation layer, the user interface, the browser surface (Comet), and the agent layer (Computer). This positioning lets it route every query to the most appropriate model rather than betting the company on any single LLM provider, and it converts model improvements from upstream labs into immediate product value without retraining costs.

Perplexity AI tiers comparison

Perplexity restructured its plans in 2025 with the launch of the Max tier and the addition of Education Pro. The current 2026 line-up is as follows:

Feature	Free	Pro	Max	Enterprise Pro	Enterprise Max
Cost	Free	$20/mo or $200/yr	$200/mo or $2,000/yr	$40/seat/mo or $400/seat/yr	$325/seat/mo or $3,250/seat/yr
Default model	Sonar	Sonar	Sonar	Sonar	Sonar
Advanced model access	Limited	GPT-5.1, Claude Opus 4.5, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4.1, Kimi K2 Thinking	All Pro models plus GPT-5.2, Opus 4.6, o3-pro, Opus 4.5 Thinking, Model Council	Pro model set with admin controls	All Max models plus GPT-5 Thinking, Opus 4.6, video generation
Daily Pro searches	~5 per day	300+ per day (effectively unlimited)	No fixed daily limit	Unlimited	4,000+ Pro searches per week, 500 Deep Research per month
File & document handling	Basic	Unlimited uploads, 50 files per Space	All Pro features plus image and video generation	15,000 file repository, SSO/SCIM, audit logs	15,000 files per Space plus 10K shared repository
Agent features	Comet browser (free)	Comet browser, Deep Research, basic Computer access	Full Perplexity Computer (Max-exclusive), Model Council	Comet for Enterprise, Comet Assistant	Full agentic suite with admin controls
Core value	Exploration, occasional answers	Daily research, file analysis, multi-model choice	Maximised deep research, unlimited usage, frontier model access	Team security, SSO, compliance, audit logging	Enterprise-grade autonomy with SCIM, audit logs, video generation

Best practices for Perplexity AI

To maximize the efficiency of the platform, users must employ effective prompting strategies.

The system works best when:

• The user starts with a clear goal,
• Utilizes straightforward language,
• Provides sufficient background context for the task to be fully understood.

Users should avoid common pitfalls such as:

• Being overly vague,
• Asking for too much information in a single query,
• Neglecting to provide necessary context.

Furthermore, the contextual memory feature allows for iterative refinement, where users can test and tweak prompts and ask follow-up questions without having to repeat their original query each time. Saving recurring workflows as Spaces or as Comet Shortcuts (see below) compounds these gains across teams.

Comet: The Perplexity AI browser

The introduction of Comet represents Perplexity’s most aggressive strategic maneuver, signalling an intention to transition from a focused search interface into a full-fledged AI operating environment designed for workflow automation and delegation. Comet launched as an invite-only Max-tier benefit in July 2025, expanded to a free worldwide release in October 2025, added Android later in 2025, and reached iOS in March 2026, completing the cross-platform rollout.

Comet functionality and workflow integration

Comet is engineered as a Chromium-based AI browser specifically optimised for research, drafting, and accelerated organisation. It offers a suite of core tools aimed at professional productivity, including the ability to organise search results into collections, save threads, and pin important tabs. For content generation, Comet streamlines the process of composing, generating outlines, summaries (via the /tldr shortcut), and drafts directly sourced from the web.

Comet moves beyond simple assistance by integrating intelligence into natural work processes. It provides daily workflow support by managing complex, mundane tasks, such as summarising recently watched videos, organising research tabs, checking schedules, and listing action items for the day. Voice Mode, refreshed in early 2026 with OpenAI’s GPT Realtime 1.5, raises the reliability of hands-free interaction by roughly 25% over the previous voice stack.

Advanced automation via shortcuts

The backbone of Comet’s efficiency is its highly configurable “Shortcuts,” which function as reusable mini AI agents capable of executing complex, multi-step workflows with minimal input.

These Shortcuts are typically triggered using the / command in the search bar or sidecar. Examples of these agents include /cite for generating citations in MLA, APA, or Chicago formats, /tldr for summarising the current page, and specialised professional tools like /job-fit, which analyses a job description against a user’s LinkedIn profile. These commands transform complex operations, such as fact-checking a document against credible sources (/fact-check), or preparing a personalised demo briefing (/demoprep tomorrow’s meetings), into single, rapid prompts. Shortcuts can also be combined and targeted at specific data sources, such as files or currently open tabs, allowing for layered, complex workflows within a unified interface.

Enterprise security and system integration

For institutional users, Comet for Enterprise integrates sophisticated security and administrative standards. Organisations subscribing to Enterprise Pro seats can customise data retention schedules that apply universally to both Perplexity and Comet data, ensuring compliance and internal security management. Privacy features include built-in Adblock functionality that automatically blocks intrusive ads and trackers, and an Incognito mode that limits data sharing with external AI services, though Comet Assistant functions are limited in this mode.

Since March 2026, Comet for Enterprise is silently deployable across macOS and Windows via MDM, with hundreds of configurable browser policies, granular control over which actions the AI agent may take, and security tooling developed in partnership with CrowdStrike. All activity inherits existing Enterprise settings for data retention, audit logs, and permissions, and no data is used to train Perplexity’s models. Comet also allows enterprise users to establish local MCP servers as Custom Connectors, enabling the system to perform actions, search for information, and edit files across an organisation’s everyday applications directly through the Comet interface.

Perplexity Computer: the agentic layer

Launched in early 2026 and currently exclusive to the Max tier, Perplexity Computer is the platform’s autonomous agent system. It runs in an isolated cloud sandbox, breaks goals into tasks and sub-tasks, and orchestrates roughly 19 different frontier models, picking the best one for each sub-step. Tasks run asynchronously in the background, pause when credits run out, resume when topped up, and support scheduled or condition-based triggers. Real-world use cases reported by Perplexity include reviewing documents, planning marketing campaigns, adjusting ad spend, generating tax filings, and booking travel. The company estimates that around 57% of all agent activity focuses on cognitive work rather than simple browsing tasks.

For organisations evaluating where agentic AI fits, Computer is the clearest signal of where Perplexity is heading: from answer engine, to browser, to a full agent runtime that can complete multi-step work without a human at every step.

The strategic business agent and market conflict

The AI agent within the Comet browser is designed to act as an autonomous assistant capable of executing transactions, such as making purchases and comparisons on a user’s behalf. Importantly, Perplexity asserts that user credentials associated with these actions remain stored locally on the user’s device and are never saved on Perplexity’s servers.

This move toward transactional autonomy placed Perplexity in direct conflict with entrenched e-commerce platforms. Amazon issued a legal challenge in November 2025, demanding that Perplexity block the Comet agent from performing shopping actions on its platform. On 9 March 2026, a federal judge in the Northern District of California granted Amazon a preliminary injunction barring Comet from accessing password-protected sections of Amazon and ordering Perplexity to destroy collected Amazon data. The court found that Comet accessed Amazon accounts “with the Amazon user’s permission, but without authorisation by Amazon,” a distinction likely to define agentic-commerce case law for years. Perplexity was granted a stay on 30 March 2026 and filed its appeal on 1 April 2026. The case is still working its way through the Ninth Circuit.

The legal and commercial conflict confirms that Comet represents a foundational shift: the platform is competing not just on information retrieval but on user commerce and productivity efficiency. Traditional search engines monetise the moment of discovery (through advertisements next to links), while Comet aims to monetise the moment of action (facilitating the purchase or automating the workflow).

The verification paradox: Citation vs. Accuracy

Perplexity’s greatest competitive asset, citation transparency, is also its most significant challenge. Independent evaluations confirm that Perplexity performs better than many rivals in maintaining citation accuracy, showing the lowest rate of incorrect citations among tested AI search engines.

However, this relative superiority does not equate to absolute reliability. A comprehensive study indicated that Perplexity still answered incorrectly or misattributed claims in approximately 37% of cases. This error rate is considered unacceptable for uncritical reliance in high-stakes scholarly or professional work. The fundamental problem is compounded by the authoritative, conversational tone of the responses, which generates a potentially dangerous “illusion of reliability”. The mere presence of citations can mislead users into believing the information is verified when errors or misattributions still occur roughly one time in three.

This paradox reveals that Perplexity remains a “half-step” solution. While it vastly accelerates the discovery phase of research, the inherent error rate means professional users must maintain strict internal protocols requiring mandatory manual verification of every cited source. The time saved in synthesis is partially offset by the required time spent on auditing and validation. This dependence on continuous human verification means professional users must be re-skilled to adopt a critical, skeptical mindset toward AI-generated answers, counteracting the platform’s promise of full, seamless automation.

An additional concern relates to ethical data retrieval. Reports indicate that Perplexity Pro, in particular, demonstrated aggressive data retrieval tactics, correctly identifying excerpts from publishers who had intentionally blocked its crawlers. Perplexity has responded in part with the Comet Plus revenue-share program (a $42.5M publisher pool launched in 2025), which gives content creators a direct stake in AI-driven traffic, though tension with publisher data governance and intellectual property rights is unlikely to fully disappear.

Perplexity AI benefits and constraints

Area	Key benefits (pros)	Known constraints (cons)
Accuracy claim	Lowest rate of incorrect citations among tested rivals	Answers incorrectly or misattributes claims in approx. 37% of cases
Business impact	Operational efficiency, cost reduction via RAG, multi-model orchestration	High initial setup costs (for Enterprise), integration complexity
Strategic edge	Model agnosticism, full workflow automation (Comet, Computer, Model Council)	Security concerns, limited human oversight in autonomous decision-making
User risk	Transparency accelerates discovery	Illusion of reliability necessitates mandatory manual verification
Regulatory	Active publisher revenue-share program softens content-rights tension	Open agentic-commerce questions following the Amazon preliminary injunction

Competitive mapping: Perplexity AI

Perplexity AI operates within a hyper-competitive field dominated by technology behemoths, forcing the company to execute a differentiated and hybrid competitive strategy. Its core rivals include Google’s Search Generative Experience (SGE) / AI Overviews, Microsoft Copilot, OpenAI’s ChatGPT (with the Atlas browser), Anthropic’s Claude, and Google Gemini.

Structured comparison with key rivals

The fundamental distinction among competitors lies in their technical architecture, their primary revenue model, and their ultimate workflow focus.

• Google AI Overviews / SGE: Google’s decades of dominance are built upon its colossal Search index, which organises hundreds of billions of webpages. AI Overviews is Google’s response, augmenting this indexing power with its Gemini 3 LLM to synthesise information. Google’s strength is its near-universal data depth and reach. Its weakness, relative to Perplexity, is a historic reliance on ad revenue and a system that sometimes lacks the immediate, granular source transparency critical for verification.
• Microsoft Copilot: Microsoft positions Copilot as a daily AI assistant designed to enhance both professional and personal life. Leveraging GPT-5 family models and deep integration with the Microsoft 365 ecosystem, Copilot’s competitive advantage is its ability to seamlessly optimise existing business workflows across tools like Outlook, Word, Teams, and Excel.
• OpenAI ChatGPT and Atlas: ChatGPT excels at generative and reasoning tasks, and the Atlas browser (launched October 2025) is OpenAI’s direct response to Comet. ChatGPT’s strength is raw model quality and ecosystem depth; its relative weakness is that real-time retrieval and citation transparency are bolted on rather than core to the product.
• Anthropic Claude: Claude is the strongest pure-LLM competitor on long-context analysis and writing. Perplexity strategically capitalises on this by offering Claude Sonnet 4.5 and Claude Opus 4.5/4.6 inside its own Pro and Max tiers, turning a competitor into a feature.

Perplexity’s differentiated competitive edge

Perplexity executes a sophisticated hybrid strategy, allowing it to carve out a highly defensible niche:

1. Transparent RAG specialisation: Perplexity’s dedicated design around Retrieval-Augmented Generation ensures concurrent citation and ranking of data sources.
2. Focus on the AI research operating system: Perplexity is competing with Google on search quality (factuality), with Microsoft and OpenAI on workflow integration (Comet), and with everyone on the agent runtime (Computer). The move into autonomous workflow allows Perplexity to compete across the entire user workflow rather than just the search box.
3. Model-agnostic strategy: By allowing Pro and Max users to choose among multiple advanced models, and by surfacing three answers at once via Model Council, Perplexity positions itself as a model-agnostic platform. This insulates the company and its enterprise clients from the performance volatility or vendor lock-in associated with relying on any single foundation model developer.

Comparative analysis: Perplexity AI vs. Key rivals

Feature	Perplexity AI	Google AI Overviews	Microsoft Copilot	ChatGPT (with Atlas)
Core function	Conversational search, research OS, agent runtime	AI-augmented web indexing	Daily AI assistant and productivity integrator	General assistant with agentic browser layer
Technical moat	RAG plus Sonar plus Comet plus Computer	Vast search index depth (Gemini 3)	Deep integration with Microsoft 365	GPT-5 family quality, broad ecosystem
Citation transparency	High (citations integral to every response)	Medium (integration varies)	Medium (citations integrated into workflow)	Medium (citations available, less granular)
Model strategy	Multi-model, model-agnostic, Model Council on Max	Single-vendor (Gemini)	Primarily OpenAI	Primarily OpenAI in-house
Workflow focus	Deep research, summarisation, autonomous action (Comet, Computer)	General discovery and retrieval	Drafting, communication, M365 optimisation	Generative tasks plus agentic browsing
Strategic model	Subscription, enterprise knowledge broker, agent runtime	Advertising-driven search dominance	Enterprise software integration, subscription	Subscription, API, enterprise

Future outlook

Perplexity AI’s trajectory in 2026 is defined by its success in capturing the high-value research market, its aggressive move into the workflow automation space with Comet and Computer, and its expansion into infrastructure-scale commitments.

Infrastructure and scale

In January 2026, Perplexity signed a three-year, $750 million commitment with Microsoft Azure to secure GPU capacity for high query volumes and enterprise customers. With more than 100 million monthly active users, over a billion monthly queries, and a $20 billion valuation, the company is no longer a niche challenger; it is an established player in the AI value chain whose primary risk is execution rather than relevance.

The “Adult” AI brand and ethical stance

Perplexity is deliberately cultivating a brand identity as the “serious” or “adult” AI platform. This is reinforced by its public commitment to transparency, exemplified by the CEO’s focus on implementing features that track the stock market holdings of public figures, such as members of the US Congress, with plans to expand this transparency to other financial markets. This explicit prioritisation of objective, verifiable public data strongly appeals to financial, legal, and research users.

Furthermore, CEO Aravind Srinivas has taken an unambiguous ethical stance, warning against the growing trend of AI companions and “AI girlfriends,” labelling them as dangerous due to their potential for deep psychological manipulation and detachment from real-world relationships. By drawing this moral and ethical distinction, contrasting objective reality and transparency with psychological engagement, Perplexity reinforces its image as a fact-focused research tool, aligning its brand with the core compliance and objectivity requirements of enterprise and institutional clients.

Dependence on accuracy and future challenges

The future success of Perplexity is inextricably linked to continuous, measurable improvements in factuality. The platform’s entire revenue model, particularly the Pro, Max and Enterprise tiers, is based on selling accuracy and efficiency through subscriptions. Therefore, future iterations must prioritise rigorous, auditable verification chains to achieve near-perfect citation accuracy.

The scaling of the Comet and Computer platforms presents significant challenges, particularly in navigating regulatory and platform conflict. The Amazon preliminary injunction confirms that Perplexity’s autonomous agent strategy directly threatens the established business models of entrenched platforms, and the appeal outcome at the Ninth Circuit will set the tone for the wider agentic-commerce industry. The company’s success will hinge on its ability to navigate these legal headwinds concerning user agency, data rights, and the scope of AI agents in digital commerce. With the EU AI Act’s General-Purpose AI obligations enforcement window closing on 2 August 2026, European procurement decisions for Perplexity should also be planned with that regulatory volatility in mind. Finally, achieving widespread enterprise adoption will depend on demonstrating clear Return on Investment (ROI) and facilitating seamless, secure integration with the complex proprietary IT infrastructures utilised by large corporations.

Conclusion and recommendations

Perplexity AI stands as a technological disrupter whose core advantage is transparency derived from its RAG architecture and its multi-model orchestration via Sonar plus frontier LLMs. It is moving aggressively to command the enterprise and professional research market by shifting the AI utility from simple Q&A to autonomous workflow management via the Comet browser and the Computer agent runtime.

For organizations leveraging Perplexity AI, the following strategic recommendations are provided:

1. Mandate verification protocol: Despite Perplexity’s superior citation performance, its current 37% error rate dictates that it must be treated as a powerful research accelerator, not a final answer generator. Organizations must maintain strict internal protocols requiring mandatory human verification (clicking and reviewing the source) of all cited primary documentation before incorporating findings into high-stakes reports, legal briefs, or operational decisions.
2. Strategic deployment of Comet: Enterprises should prioritize the adoption of Comet Shortcuts and automation capabilities to address recurring, multi-step tasks in departments such as competitive intelligence, marketing asset repurposing, and internal knowledge management. This enables organizations to shift human capital away from routine data gathering and towards strategic, analytical work.
3. Capitalize on model agnosticism: Utilize the Pro/Enterprise flexibility to access and compare outputs from various LLMs through a unified Perplexity interface. This strategy mitigates single-vendor lock-in risks and ensures the utilization of the best available AI capability for any given task.
4. Acknowledge implementation cost: Recognizing that the highest ROI is found in high-leverage tasks (e.g., fraud detection, real-time logistics), organizations must accurately budget for the complexity and cost associated with securely integrating Perplexity Enterprise Pro with proprietary internal data systems to unlock the full potential of automation and predictive analytics.

If you are looking to get started with Perplexity AI and want to inspire your team you can have a look at the Perplexity Live Demo. If your team is already looking forward to getting started, but doesn’t know how to do so you can check out the Perplexity AI Workshop, where we teach you how to use it optimally. If you are even further in the process and decided to implement Perplexity in your business, we can also assist with that through the Perplexity consultancy service, where we guide you to the best way to start off right.

Frequently asked questions (FAQ)