Questions we get asked every week.

RAG stands for Retrieval-Augmented Generation. It's an architecture that lets a language model answer questions using your own documents, data, or knowledge base — rather than relying on what it was trained on. You need one when your users need accurate, up-to-date answers grounded in your company's information: customer support bots, internal knowledge search, document Q&A, or anything where hallucination is a business risk. If you're just using ChatGPT as-is for general tasks, you don't need RAG. If you need an AI system that knows your product catalog, your contracts, or your support history — you do.

A scoped AI agent — one that handles a specific, well-defined workflow — typically runs $8,000–$25,000 for design, build, and deployment, depending on complexity. Simple agents that call an LLM with a fixed context sit at the lower end. Agents that manage multi-step retrieval, tool use, memory, and production monitoring sit higher. Ongoing cost includes LLM API usage (typically $50–$500/month at moderate scale) and maintenance. We scope every engagement before agreeing a price, so you know the number before we start.

Using ChatGPT means typing prompts and reading responses — it's a UI for a model. AI engineering means designing and building systems: retrieval pipelines, evaluation frameworks, latency budgets, fallback behavior, cost monitoring, and production observability. An AI engineer can tell you why a system is getting worse, how to fix it without retraining, what it will cost at 100K requests per day, and how to detect when the model's outputs are drifting. That's the difference between a tool user and an engineer.

A focused AI integration — adding an AI-powered feature to an existing product — takes 4–10 weeks. A standalone AI system built from scratch (RAG pipeline, agent workflow, MLOps infrastructure) typically takes 8–16 weeks to production. The main variable is how well-defined the problem is before we start. The clearer the inputs, outputs, and success criteria, the faster we move. Vague problems take longer to scope than to build.

Data scientists analyse data, build models, and run experiments — usually in notebooks, for internal stakeholders. AI engineers build the systems that put those models into production and keep them running: APIs, retrieval layers, prompt engineering at scale, output validation, monitoring, and cost optimization. Most companies need both but confuse them during hiring. If you're asking 'why is our AI feature slow and expensive in production?' — that's an AI engineering problem, not a data science problem.

For most business applications, you do not need to train a custom model. Fine-tuning or training from scratch makes sense only when you have very large proprietary datasets, extremely specific output formats that prompt engineering can't achieve, or latency/cost requirements that commercial APIs can't meet. The vast majority of valuable AI products — internal tools, customer-facing chatbots, document processing, recommendation systems — are built on top of existing models with good retrieval, evaluation, and prompt engineering. We'll tell you honestly if custom training is the right call for your situation.

Define success in human hours, conversion rate, error rate, or revenue before you build — not after. Good metrics to track: time saved per user per week (for internal tools), support ticket deflection rate (for chatbots), error rate vs. human baseline (for document processing), and latency + cost per query (for system health). If you can't define what 'better' looks like before the project starts, you will not be able to measure it after. We always agree on success metrics before writing a line of code.

A marketing site or landing page: 2–4 weeks. A full product website with blog, CMS, contact forms, and SEO: 4–8 weeks. A SaaS product or complex web application: 10–20 weeks. The timeline is driven by scope clarity, not just complexity. If you have your copy, brand, and requirements defined before we start, we move significantly faster. Projects that start vague take longer regardless of technical complexity.

A professional marketing site with Next.js: $8,000–$18,000. A full product website with CMS, blog, and SEO infrastructure: $15,000–$35,000. A complex SaaS product: $40,000+. WordPress can be cheaper upfront, but it typically costs more over 3 years when you factor in maintenance, security, plugin conflicts, and performance work. We build on Next.js because it performs better, ranks better, and requires less ongoing maintenance.

Lighthouse is Google's automated tool for measuring web performance, accessibility, SEO, and best practices. A score of 90+ means your site loads fast, is accessible, and follows modern standards. A score below 60 directly harms your Google rankings — Google's Core Web Vitals are a ranking factor, and a slow site loses organic traffic and conversions simultaneously. We build to 98+ Lighthouse on every project. If your current site scores below 70, it's costing you both rankings and conversion rate.

Rebuild when: the site is on WordPress with no CMS flexibility, loads in more than 3 seconds on mobile, or scores below 60 on Lighthouse. Rebuild when: the platform can't support the content or features you need. Redesign when: the underlying technology is sound but the visual design or copy is outdated. In practice, most startups that 'just want a redesign' find that the real problem is platform limitations — you can't design your way out of a slow, poorly indexed site.

Next.js is a React framework built for performance, developer experience, and modern deployment. It renders pages server-side or statically, loads in milliseconds, and scores well on Core Web Vitals by default. WordPress is a PHP CMS that powers most of the web but requires significant optimization to perform at the same level. Next.js sites typically outperform WordPress sites on SEO metrics because of speed and structural advantages — which is why we default to it for every new project.

Yes. We build cross-platform React Native apps for iOS and Android alongside web projects. Because React Native shares TypeScript types, design tokens, and business logic with a Next.js web app, the two stay consistent without maintaining separate codebases. For startups, this means one team handles both surfaces — no coordination cost between a web agency and a separate mobile shop.

For a new domain with no history: 4–6 months to start ranking for long-tail terms, 9–12 months for meaningful organic traffic. For an existing domain with a solid foundation: improvements from technical fixes can show up in 6–8 weeks, content improvements in 3–4 months. SEO compounds — the sites that rank well in year two started a year ago. The worst thing you can do is start, stop after 3 months because you don't see results, and repeat that cycle.

GEO is the practice of optimizing your content to be cited by AI systems — ChatGPT, Perplexity, Google AI Overviews, Claude — rather than just ranking on traditional search. When someone asks an AI 'who builds AI agents in Singapore?' or 'what does a RAG pipeline cost?', the AI cites sources it finds authoritative. GEO means creating content that AI systems trust and cite: specific answers to specific questions, original data, strong opinions with evidence, and structured information that AI can extract and surface accurately.

AI systems cite content that is specific, authoritative, and directly answers questions. Practically: write content that answers the exact questions your customers ask, use structured data (FAQ schema, Article schema), build FAQ pages with direct Q&A format, publish case studies with real numbers, and create content with a clear point of view rather than balanced-but-vague summaries. Generic content that says 'it depends' gets ignored. Specific content that says 'it costs $X and takes Y weeks for Z reason' gets cited.

The fundamentals haven't changed: relevance, authority, and technical performance. What has changed is the bar for quality. Google's helpful content system penalizes thin, templated, and AI-generated-without-original-insight content. What works now: content that demonstrates first-hand experience, specific data or case studies, clear answers to questions your audience is actually searching, and pages that load fast on mobile. The technical SEO floor is higher than it used to be — if your Core Web Vitals are poor, no amount of content quality will fully overcome it.

Enough to get statistically meaningful data, not enough to bet the company. For most B2B startups: $3,000–$8,000 per month to run real experiments across Google and LinkedIn. Below $2,000/month, data volume is too thin to optimize from. Above $15,000/month before you've validated your messaging and landing pages, you're spending against an unproven funnel. Paid media only scales profitably once organic conversion benchmarks are established — if your landing page converts at 0.5%, paid traffic won't fix that.

Technical SEO is the foundation: site speed, crawlability, indexing, structured data, Core Web Vitals, canonical tags, and mobile usability. If technical SEO is broken, content won't rank regardless of quality. Content SEO is the superstructure: keyword targeting, topical authority, content depth, and internal linking. Most sites that 'can't rank' have a technical problem — slow loading, thin pages, poor site structure — that content alone won't solve. We fix the foundation before building content.

Senior engineers do the work. This is the main reason we exist. The standard agency model sells a senior engineer in the pitch and hands the project to a junior team. At go-via-us, the engineers who scope the project are the engineers who build it. We are a small team by design — no bench of juniors, no account managers between you and the people writing code.

We prefer fixed-scope, fixed-price engagements: you know the cost before we start, and there are no surprises. For ongoing work — SEO, content, social media, paid media — we use monthly retainers with clear deliverables agreed upfront. We don't do time-and-materials for project work because it misaligns our incentives with yours. If we scope it right, we both win. If we scope it wrong, we fix it.

For most engagements: within one to two weeks of agreeing scope. We do a short discovery session first — usually 30–45 minutes — to make sure we understand the problem before proposing anything. If your need is urgent, tell us and we will tell you honestly whether we can accommodate it.

Our team operates across US (EST/PST), UK/Ireland (GMT/BST), Singapore (SGT), and Australia (AEST). Most clients receive daily async updates and at least one live call per week at a time that works for their location. We have never had a project stall because of a time zone mismatch.

Yes. We can operate as a standalone delivery team or alongside your in-house engineers. Many clients bring us in to add senior AI or full-stack capability they don't have full-time — we integrate with your Git workflow, attend your standups, and leave behind documented, maintainable code that your team can own after the engagement.

Both, but with honest expectations. Early-stage startups get the most value from us when they have a clear problem to solve and a realistic budget for solving it. We have worked with pre-revenue founders building their first product and with Series B companies adding AI capabilities. What we don't do: speculative work on equity, large scopes with undefined requirements, or projects where the decision-maker hasn't yet committed to the direction.