In the first post in this series we described the open-book exam: instead of letting an AI answer your visitors’ questions from memory, you hand it the relevant pages of your own website and ask it to answer from those. That post ended with a promise to explain the next piece of the puzzle — how do you know the AI actually read the book? The answer, when it works, is the little line at the bottom of a reply that says Sources.

This week that line has been on our workbench, because we caught it doing something subtly wrong. So this post is about what a citation actually is, why it matters more than it looks, and two real ways a Sources line can mislead — both of which we found in our own product, in our own chat logs.

What a citation actually is

When a visitor asks a public-facing AI assistant a question, three things happen in quick succession. A search runs over the site’s own documents and fetches a couple of dozen excerpts that look relevant. The AI writes an answer, drawing — if all is well — on a handful of those excerpts. And then, underneath the answer, the assistant lists the documents it drew on, each one a link to a real page.

That list is the citation. And a citation is best understood as a promise: this answer stands on that page — go and look.

Which is why citations matter more than their modest appearance suggests. A chatbot without sources asks to be taken on trust. A chatbot with sources can be checked — by the visitor, who can click through and read the page for themselves, and just as importantly by you, the site owner, reading your chat logs over coffee the next morning. Our working rule for the whole product is never take the AI’s word for anything you can check, and a Sources line is what makes an answer checkable at all.

But a promise is only worth something if it’s kept. Here are two ways we’ve watched it break.

The greeting that arrived with sources

Early in development, a visitor to our demo shop typed one word: “Hello.”

The assistant replied, perfectly reasonably, “Hello! How can I help you today?” And underneath that greeting, it printed: Sources: Hello world!, Clan Name Relationships.

It had cited the default WordPress post — the “Hello world!” page every new site starts with — as a source for saying hello.

The explanation is almost funny once you see it. The document search runs before every message, because at that point nothing knows whether the message is a question or a pleasantry. Asked to find pages resembling “Hello”, it dutifully returned the closest matches it had, and the AI just as dutifully listed them. Every one of those documents was real. The search genuinely fetched them. And the Sources line was still false — because the promise a citation makes is this answer is based on these pages, and the answer was based on nothing at all.

That’s the first lesson about citations: a Sources line can be built entirely from real documents and still be a false statement. The fix, once we understood the problem, was plain: small-talk replies now cite nothing, and our quality scoring stopped awarding marks for citations an answer never used. An empty Sources line under “Hello! How can I help?” is the honest one.

Twelve sources for one clan

The second failure is this week’s, and it’s the opposite of the first: not sources that were never used, but too many of them.

Part of every release for us is firing a battery of test questions at the live demo shop and reading the results back through the chat logs. One of this week’s questions was about Clan Cameron — crest, septs, the usual. The answer was good: grounded in the site’s own pages, correct, every link real and working.

But the Sources line listed twelve documents. And on the follow-up question it listed nine — nine clan-map pages, not one of them about Cameron.

Nothing was invented. Every page existed; every link loaded. What the assistant had done was list a large slice of everything the search fetched, rather than the two or three pages the answer actually stood on. And a citation list like that quietly breaks the promise from the other direction: a visitor who wants to check the answer now has to wade through a dozen links to find the one that matters. Cite everything and you may as well cite nothing — the reader can no longer tell which source is load-bearing.

So we did what we always do with a finding like this: logged the evidence, filed the issue, and designed the fix before touching code. The Sources line should list what the answer used, not what the search returned, and it should be capped at a sensible handful. In the interests of honesty — because honest engineering stories are rather the point of this series — that fix is filed and designed but not yet shipped. If you’re watching our demo, you should see the Sources lines get shorter in a coming release.

And the links must exist

There’s a third way a citation can fail, the bluntest of the three: the AI can simply invent a source — a plausible-looking link to a page that has never existed. We told that story in our guardrails post, where our assistant confidently invented two products, complete with convincing web addresses, on our own domain.

That one we treat as a hard stop. Before any answer reaches a visitor, every link in it is checked against the site’s real pages, and an invented link fails the whole answer rather than being quietly served. In this week’s release checks — umpteen scenario conversations fired at the live site — that check had a quiet day: not one invented link, and every single cited page loaded when we clicked it. Quiet days are what good guardrails sound like.

Three questions worth asking

If you’re evaluating an AI assistant for the front of your own website — ours or anyone’s — the citations behaviour is worth probing, and three questions get to the heart of it:

Does the Sources line show what the answer used, or what the search returned? (Asked of AnswerRails today, the honest answer is “the search, for now” — which is exactly why it’s on our fix list.)

Are cited links checked to actually exist before the visitor sees them? An AI that can print an invented link under the word “Sources” has turned the trust signal into a liability.

Can you, the owner, see the difference in your logs? Our chat logs record what the search offered and what the reply cited, separately — so when a Sources line misbehaves, there’s evidence to read rather than a mystery to shrug at.

A citation is a promise you can check. The engineering job — never quite finished — is making sure it’s a promise that’s kept: no sources the answer never used, no more than the answer needs, and never, ever a link to a page that doesn’t exist.

Next in this series: why your AI should tell you when it’s having a bad day — the story of our assistant’s health dashboard, and the afternoon a provider slowdown taught us what an assistant should say about itself.

AnswerRails is a public-facing AI assistant for WordPress, in development at Celtic3d — built compliance-first, with guardrails as the product rather than an afterthought. Follow its progress at answerrails.com.

The post How Do AI Citations Work? (And Two Ways a Sources Line Can Mislead) appeared first on Celtic3d.

Every AI product page you’ll read this year says it has “guardrails”. Almost none of them tell you what that means. Is it a list of banned words? A legal disclaimer? A vibe?

This week we shipped a release of AnswerRails that was almost entirely guardrail work, and it started with an embarrassing discovery in our own chat logs. So rather than define the term abstractly, let us show you what guardrails actually are — using a real failure, on our own website, and what it took to fix it properly.

The crests that never existed

Our demo shop, Celtic3d, sells 3D-printed Scottish clan crests, and the AnswerRails assistant on the site answers questions from a knowledge base of real product and clan information. In late July, a visitor asked it a perfectly reasonable question:

“What’s your most Irish clan crest?”

The assistant — running on the small, economical model our free tier uses — answered confidently. It recommended the Clan O’Neill crest and the Clan O’Brien crest, and it helpfully included links to both product pages on our site.

Neither product exists. It invented them — names, descriptions and links — because O’Neill and O’Brien sound like exactly what an Irish-crest product would be called. The links looked completely legitimate, because they pointed at our own domain in the same format as our real product pages. A visitor clicking through would have landed on a “page not found”.

And here’s the part that stung: our own quality checking gave that answer a high score. We already verified links in answers — but only links to other websites. A link to our own site was waved through on trust, no questions asked. The assistant’s most damaging inventions turned out to be the most plausible-looking ones: pages on the very domain it lives on.

A guardrail is a check, not a hope

That incident is a perfect illustration of what “guardrails” means to us, because the fix wasn’t a better model or a sternly worded instruction (though we tightened the instructions too). The fix was a rule the software enforces, whether the AI cooperates or not:

Never take the AI’s word for anything you can check.

In our first post we described the open-book exam: the assistant answers from your documents, not its imagination. Guardrails are everything wrapped around that exam to catch the cases where it goes wrong anyway. In AnswerRails, a visitor’s question now passes through checks at three points:

Before the AI ever sees the message. The incoming message is screened first — and the person this layer protects most is you, the site owner. AnswerRails runs on your own AI provider account, so everything a visitor types into your chat box is processed under your name — and providers have usage policies. A visitor who decides to fill your chat with abuse is doing it on your account. The screen catches that traffic before the answering AI ever sees it: abusive messages get a polite deflection, and only clear, repeated abuse costs a visitor their chat access. One category is deliberately treated differently. Someone whose message suggests they’re in real distress has done nothing wrong, and is never handled like a rule-breaker — no strikes, no telling-off. For those messages the assistant skips the AI entirely and immediately provides signposting to real human support. Some things should never be improvised on, and because this check runs before any AI is involved, it can’t be talked around.

While the answer is being written. This is the open-book machinery: the question is searched against your knowledge base, and the AI writes from the excerpts it’s handed, citing its sources.

After the answer is written — and before your visitor sees it. This is the layer our incident exposed. The software now inspects the finished answer, and every link the AI wants to include on your own site is verified to actually exist — resolved against your real pages, products and posts. If the AI has invented a link, that’s treated as evidence the whole answer has gone off the rails: the answer is withheld entirely, not tidied up and served. The visitor gets an honest message instead, and the invented answer is quarantined in your logs — so you can see exactly what your assistant almost said.

That last design choice surprises people. Why throw away the whole answer over one bad link? Because an invented link is rarely alone. An answer that fabricates a product page has usually fabricated the product too. Stripping the link and serving the rest would mean confidently describing merchandise that doesn’t exist — with the evidence removed.

Staying in its lane

The same release closed a subtler gap. During testing, we asked the shop assistant to explain photosynthesis. The old behaviour: the knowledge base search comes back empty (we sell clan crests, not biology lessons), and the model cheerfully answers from its general knowledge — a small essay on chloroplasts, served on a shop’s website with the shop’s name over it.

Nothing about that answer is dangerous. But it’s not grounded — nothing connects it to your business, and you have no way to vouch for it. And the moment your assistant will answer anything, it will eventually answer something you’d never have signed off: an opinion on a contested political topic, medical advice, a hot take on a competitor. All delivered in your shop’s voice.

So AnswerRails now enforces scope. Friendly conversation — greetings, “what can you do?” — flows normally. But when a substantive question can’t be answered from your documents, the assistant declines rather than freelancing. Which brings us to the most counterintuitive idea in the whole subject.

Why a deflection can be the right answer

Here’s what a visitor sees when one of these checks fires:

“I don’t have reliable information to answer that, so I’d rather not guess. If you have a question about our products or services, I’m happy to help.”

It’s tempting to read that as the assistant failing. We’d argue it’s the assistant doing its most important work. A wrong answer from your website isn’t like a wrong answer from a search engine — it carries your authority. A fabricated returns policy, a made-up product, an invented delivery date: each is a small liability with your name on it. An honest “I’d rather not guess”, followed where appropriate by a handoff to your team, costs you almost nothing — and quietly builds the thing an AI assistant is actually for, which is trust.

The economics only work one way. One invented product page can cost more goodwill than a hundred honest declines.

Guardrails you can audit

One more principle from this release, because it separates real guardrails from marketing: everything the assistant declines is recorded before it’s declined. The withheld answer, the invented links, which check fired and why — all of it lands in your chat logs, scored honestly (an invented answer now scores zero, not 87). Guardrails that operate silently are just another black box. Guardrails you can audit are a system you can supervise.

That cuts both ways, incidentally. The same release changed how moderation events are recorded: a borderline automated flag on a visitor’s message is logged as what the classifier reported, with its confidence score — never as a fact we assert about the person. Guardrails protect your visitors from over-zealous software, too.

What to ask any vendor

If you’re evaluating a public-facing AI assistant for your website, “does it have guardrails?” will always get a yes. Better questions: What happens when the AI invents a link on my own site — is it checked, or trusted? When my documents don’t contain the answer, does it decline, or improvise? And can I see, in my logs, everything it declined to say?

We didn’t have good answers to the first question three weeks ago. We do now, because we watched our own assistant invent two clan crests and decided that should be structurally impossible rather than merely unlikely.

Next in this series: why your AI assistant should tell you when it’s having a bad day — the story of our health dashboard, and a live incident where the diagnosis mattered.

AnswerRails is an AI customer-service assistant for WordPress, built around grounded answers, real citations, and honest handoffs. Follow along as we build it at answerrails.com.

The post What Do AI “Guardrails” Actually Do? (A Story About Two Products That Never Existed) appeared first on Celtic3d.

If you’ve experimented with AI chatbots for your business, you’ve probably run into the problem that keeps most of them off serious websites: they make things up. Ask a generic chatbot for your opening hours and it will cheerfully invent some. Ask about your returns policy and it will describe a policy you don’t have, in a confident tone your customers have no reason to doubt.

This isn’t a bug that better AI models will simply grow out of. It’s a consequence of what these models are: they’ve read a huge slice of the internet, but they have never read your business. The fix isn’t a smarter model. It’s giving the model your actual information at the moment it answers — and that’s where vector stores come in.

The open-book exam

Here’s the mental model we use constantly while building AnswerRails: a closed-book exam versus an open-book exam.

A generic chatbot sits a closed-book exam about your business. It has general knowledge and a lot of confidence, so it writes something — and something is often wrong.

A grounded chatbot sits an open-book exam. Before it answers, it’s handed the relevant pages from your own documents — your FAQ, your product notes, your policies — and instructed to answer from those pages. If the pages don’t contain the answer, it should say so and offer to connect the customer with a human, rather than guessing.

We see this daily on our own demo site, Celtic3d, which sells 3D-printed Scottish clan crests. Ask the assistant “Which clan is the surname Bain associated with?” and it answers from a clan-name reference document we loaded into its knowledge base — Clan MacKay, as it happens — and cites the document it used. No guessing, and the customer can see where the answer came from.

But this raises the practical question the whole approach depends on: out of everything in your knowledge base, how does the system find the right pages to hand over?

Finding the right page: why ordinary search isn’t enough

The obvious answer is keyword search — the kind of matching that powers a basic site search box. It works, and (as you’ll see below) we use it too. But it has a well-known weakness: people don’t use your words.

Your FAQ says “opening hours”. Your customer types “when are you open?” or “what time do you shut?” or “are you around on Saturdays?” — three questions that share almost no words with the document that answers them. Keyword search matches letters, not meaning, and meaning is what customers actually type.

So what is a vector store?

A vector store is a database built to search by meaning instead of by matching words.

The idea sounds stranger than it is. Modern AI systems can convert a piece of text into a long list of numbers — called a vector, or an embedding — that acts like a set of coordinates. Not coordinates on a geographic map, but on a kind of map of meaning. Passages that mean similar things end up close together on that map, even when they use completely different words. “Our opening hours are 9 to 5” and “what time are you open?” land near each other. “Our opening hours” and “opening a jammed sporran clasp” do not, despite sharing a word.

A vector store holds those coordinates for every passage in your knowledge base. When a customer asks a question, the question is converted to coordinates the same way, and the store finds the passages sitting nearest to it on the meaning map. Those passages — usually just a handful of short excerpts — are what gets handed to the AI as its open book for that one answer.

That’s the whole trick. No magic, just geometry: represent meaning as position, then “which documents are relevant?” becomes “which points are nearby?” — a question computers are extremely good at.

How AnswerRails puts this to work

When you add documents to the AnswerRails knowledge base — FAQs, product information, policies, even existing pages from your site — the content is broken into digestible passages and indexed for search. Then, on every single question a visitor asks:

1. The question is searched against your knowledge base.
2. The most relevant excerpts are retrieved — only the passages that relate to this specific question.
3. The AI writes its answer from those excerpts, and the reply shows its sources, so both you and your customer can see which document the answer came from.
4. If the search comes back empty-handed — the question is about something your knowledge base doesn’t cover — guardrails step in and the assistant offers to hand the conversation to your team instead of improvising.

That last step matters as much as the first three. An honest “let me connect you with someone” is a good customer experience. A fabricated returns policy is a liability.

A lesson from the workshop: don’t bet everything on one search engine

Here’s a story from our own development log. Earlier this summer, the cloud vector-search service we relied on had an intermittent outage. Our chat kept working — but with knowledge search failing, answerable questions were being handed off to a human unnecessarily. The assistant was being honest, which is exactly what guardrails are for, but it was honest more often than it needed to be.

Our response shaped the product. AnswerRails now keeps a second, independent search index of your knowledge base — a keyword index stored in your own WordPress database. If the cloud vector search ever fails, the assistant automatically falls back to searching locally, and answers stay grounded in your content instead of everything routing to your inbox. The dashboard tells you honestly when this is happening.

And in our latest release we went a step further: that local index is now a fully supported mode in its own right. On our free tier you don’t need a vector-store account at all — your knowledge base is indexed and searched entirely within your own WordPress database. Keyword search is less clever than vector search (it matches words, not meaning), which is why vector search remains the recommended upgrade — but both modes are grounded: every answer is built from your documents, with sources, or honestly handed off.

Where does my data live?

A fair question, and one we think deserves a plain answer. Your knowledge base is stored and searched in your own WordPress database — and if you enable vector search, also in a vector store on your own account with your AI provider, not ours. When a customer asks a question, only the excerpts relevant to that question are shared with your chosen AI provider so it can write the answer. You bring your own API key, so the relationship — and the bill — is directly between you and your provider.

What to take away

If you’re evaluating an AI assistant for your website, the technology behind the buzzwords boils down to three questions worth asking of any product:

Does it answer from your documents rather than its general knowledge? Does it show its sources, so answers can be checked? And when it doesn’t know, does it say so and hand over to a human?

Vector stores are the machinery that makes the first of those work well — search that understands “are you around on Saturdays?” is the same question as “opening hours”. The rest is engineering discipline: grounding, citations, guardrails, and a fallback for the days the cloud has a bad morning.

That’s what we’re building with AnswerRails. In future posts we’ll dig into other pieces of the machinery — how citations work, what “guardrails” actually means in practice, and why we think an AI assistant should tell you when it’s having a bad day.

AnswerRails is an AI customer-service assistant for WordPress, built around grounded answers, real citations, and honest handoffs. Follow along as we build it at answerrails.com.

Celtic3d Chat with us

How can we help you today?

Celtic3d

−

This chat gives informal guidance only, it does not generate binding commitments. For a binding quote, please contact us directly at sales@celtic3d.com. By continuing, you consent to us logging the conversation for 180 days.

I agree

Edit consent

This site is protected by reCAPTCHA and the Google Privacy Policy (opens in new tab) and Terms of Service (opens in new tab) apply.

Powered by AnswerRails

The post What’s a Vector Store? (And Why Your Website’s Chatbot Needs One) appeared first on Celtic3d.

Here’s what it does now, and why we think it’s the assistant your WordPress site has been missing.

The heart of AnswerRails hasn’t changed: every answer is built from your own pages, posts, product data and uploaded documents, with real source citations underneath. If the answer isn’t in your content, it doesn’t improvise — it says so, and offers to connect the visitor with your team instead. A structured escalation email means nothing gets lost on the way.

Grounded answers, not guesswork

You’ve seen AI chatbots confidently invent opening hours, prices and return policies. AnswerRails was built so yours never does.

A widget your visitors will actually enjoy

The front end has just had a complete overhaul:

• Your brand, automatically. Set your accent colour and the widget themes itself — including a proper dark mode, with text contrast checked so it stays readable on every background.
• At home on every screen. On desktop, visitors can move and resize the chat window like an app. On phones, it opens into a clean full-screen conversation that plays nicely with the keyboard.
• Small touches that matter. A one-tap “new conversation” button, a considerate consent flow, and a launcher that stays out of the way until it’s needed.

Protected by default

A public chat endpoint is an invitation to bots — so AnswerRails ships with rate limiting built in, plus optional Google reCAPTCHA v3 protection with a tunable strictness threshold. It’s invisible to real visitors (no puzzles, no traffic lights), and privacy-conscious too: Google’s script only loads for visitors who actually open the chat.

Your choice of AI — including a genuinely free one

AnswerRails runs on OpenAI, Google Gemini, or Anthropic Claude, switchable in one click, on your own API key. New installs default to Gemini’s free tier, so you can go from installation to grounded answers without paying any AI vendor a penny. And if a provider announces it’s retiring your chosen model, AnswerRails now warns you in the dashboard well before anything breaks.

It listens as well as answers

Every conversation is a signal. AnswerRails Pro turns those signals into business intelligence:

• Sales-lead detection spots buying intent mid-conversation, asks for contact details, and emails your team the transcript.
• Idea capture files feature requests and suggestions your visitors volunteer.
• Knowledge-gap analytics show you the real questions your content doesn’t answer well yet — a to-do list for your next content session.

Compliance is the foundation, not a feature

Configurable conversation logging (off, anonymised, or full), retention periods, consent prompts and disclaimer text are all built in — because an assistant that talks to your customers should meet the same standards you do.

Free and Pro, from day one

At launch there will be two editions, just as in beta: a free core — the widget, grounded answers with citations, and the anti-fabrication safety rails — and Pro, which adds unlimited knowledge, analytics, lead and idea capture, enforced citations, human-handoff escalations, and advanced privacy controls. Full details live at answerrails.com.

When can you get it?

The public launch date will be announced soon — here and at answerrails.com. Until then, the private beta is open: if you’d like AnswerRails answering questions on your site ahead of the launch, email sales@celtic3d.com and we’ll get you set up. Beta sites get hands-on help and a direct line to shape what ships.

You’ve already written the answers. Soon, every WordPress site will be able to make sure customers find them — reliably, responsibly, and without extra overhead.

AnswerRails is built and operated by Celtic3d Ltd in Aberdeen, and runs live on this very site — the chat in the corner is it. Ask it something.

Celtic3d Chat with us

How can we help you today?

Celtic3d

−

This chat gives informal guidance only, it does not generate binding commitments. For a binding quote, please contact us directly at sales@celtic3d.com. By continuing, you consent to us logging the conversation for 180 days.

I agree

Edit consent

This site is protected by reCAPTCHA and the Google Privacy Policy (opens in new tab) and Terms of Service (opens in new tab) apply.

Powered by AnswerRails

The post Your Website’s AI Assistant Is Growing Up: AnswerRails Heads for Public Launch appeared first on Celtic3d.

Small-business websites are often full of useful information — product details, policies, FAQs — yet customers still struggle to find what they need. They send messages or open tickets asking questions you’ve already answered somewhere on your site.

AnswerRails™ fixes that for WordPress users.
Our WordPress plug-in helps your website understand what people ask, match it with your existing content, and reply confidently — using only information you control. An attractive, modern chat interface on the front-end. Robust knowledge base and compliance controls in the back.

The problem for small businesses

If you run a small business, every support request costs time.
A customer types “returning my order” while your page is titled “Refunds and Exchanges.”
Another asks “warranty claim” but your content says “Product Guarantee.”

Humans see the match instantly — computers don’t.
AnswerRails bridges that gap between how people talk and how your website is organised.

How AnswerRails works

It combines two layers of intelligence designed for WordPress:

1. Local understanding
   The plugin scans your pages and builds simple language variations — recognising plurals, prefixes, and synonyms. This means your site can suggest the right page or insert smart links, even without an AI service connected.
2. AI-powered chat (optional PRO feature)
   The PRO version connects to OpenAI’s models and a private “vector store” of your own documents. The assistant replies in natural language, grounding every answer in your verified business content — not random web data.

Accuracy, quality, and compliance built in

We know small businesses can’t afford AI going off-script, so AnswerRails includes safety and accountability by design:

• Grounded in your content – Every response is validated against your pages or uploaded files. Any AI-generated links are checked and stripped if invalid.
• Structured answers – Replies must meet a minimum quality score before display, ensuring clarity and consistency.
• Full audit trail – Each interaction is logged so you can see what was asked, what was answered, and which sources were used.
• Privacy-aware – Chats and logs stay in your WordPress database, with options for anonymised sessions and consent prompts and data retention policies to stay GDPR-compliant.

Free and PRO editions

When AnswerRails™ launches in January 2026, there will be:

• 🟢 Free Edition – Core smart-linking, AI chat, local knowledge search, and privacy-compliant logging.
• 🔵 PRO Edition – Vector-store knowledge, analytics dashboard, and advanced compliance controls for teams that want deeper automation.

Both are designed for small businesses that already use WordPress and want their websites to help customers instead of hiding answers.

Why this matters

• ✅ Reduce repetitive support requests
• 🔍 Link customers to verified content
• 🧾 Keep full visibility and control
• 🛡️ Stay compliant and privacy-safe

Whether you run a shop, service business, or local organisation, AnswerRails™ helps your WordPress site speak clearly, safely, and in your own voice.

You’ve already written the answers. AnswerRails™ makes sure people can find them — reliably, responsibly, and without extra overhead.

(AnswerRails™ is in closed beta.  Check back soon for more details)

Celtic3d Chat with us

How can we help you today?

Celtic3d

−

This chat gives informal guidance only, it does not generate binding commitments. For a binding quote, please contact us directly at sales@celtic3d.com. By continuing, you consent to us logging the conversation for 180 days.

I agree

Edit consent

This site is protected by reCAPTCHA and the Google Privacy Policy (opens in new tab) and Terms of Service (opens in new tab) apply.

Powered by AnswerRails

The post Making Your Website Answer Back — Safely and Reliably appeared first on Celtic3d.

At Celtic3d, we make 3D printing simple. Most of our production is handled through our trusted manufacturing partner, giving us access to industrial-scale capacity, a wide choice of technologies and materials, and consistently high quality. Better still, when you need multiple identical copies of a part, you’ll benefit from generous quantity discounts.

There is one trade-off: because of courier transit times, set-up costs per file and a minimum order value, this service is not the best option for once-off small parts. For larger projects, prototypes or batches of parts, however, it’s an excellent fit.

Our core 3D printing technologies

SLS (Selective Laser Sintering) – PA12 Nylon

Our go-to process. SLS fuses fine PA12 powder layer by layer into strong, detailed parts.

• No support structures needed → allows intricate, interlocking or organic shapes.

• A versatile, durable material with a slightly textured finish.

• Great choice for functional parts, prototypes and assemblies.

SLA (Stereolithography) – Resin

The next most popular option. SLA uses a light-cured liquid resin for superb detail.

• Smooth surface finish, excellent for visual models.

• Can mimic some production plastics, ideal for product design prototypes.

• Wide range of resin properties to choose from.

FDM (Fused Deposition Modelling) – Filament

The odd one out. FDM prints by extruding melted plastic (e.g. PLA).

• Best suited for larger, simpler shapes.

• Limited detail compared to SLS or SLA.

• We don’t use this much, but if it’s needed for a project, we will seek a solution through our manufacturing partners.

To contact us: email sales@celtic3d.com or phone +44 (0)7714 790161 (During business hours – GMT+0)

The post 3d Printing Services appeared first on Celtic3d.