AI Integration Services
HelloDevs builds AI into the software you already run — search, support, content and workflow. Grounded in your own data, with a human still in control.
- Six years building software
- We ship our own AI product
- Fixed quotes, not hourly guesses
Most AI projects end as a chatbot nobody uses. HelloDevs builds the ones that change a number you already report on.
Where AI actually helps
Where AI actually helps
AI is mostly sold as magic. Here is where it earns its cost, and where something simpler does the job. Tell us the number you want to move before you tell us the feature — HelloDevs turns down AI work regularly, because a lot of it has a simpler answer.
| Your problem | Does AI help? |
|---|---|
| People search your site or app and get nothing useful | Yes — semantic search understands intent, not keywords. |
| A large catalogue or library nobody can navigate | Yes — guided discovery and recommendations. |
| Your team answers the same twenty questions all day | Yes — but only where the answer is settled and written down. |
| Documents, tickets or forms get sorted by hand | Yes — AI automation handles classification and routing well. |
| Writing product copy or first drafts takes days | Yes — as drafts a human approves, never published raw. |
| Your conversion rate is low | Usually no — fix speed, pricing and the page first. |
| Traffic is falling | No — that is a search and content problem. |
| You want AI because competitors announced some | No. |
Six kinds of AI integration services
These are the six that consistently pay for themselves. HelloDevs builds each one into your product rather than bolting on another subscription.
What these things are actually called
What these things are actually called
Vendors use a lot of vocabulary here. Here is what the terms mean, so you can tell whether a quote is describing real work. If a proposal is heavy on fine-tuning and light on evaluation, ask why.
- RAG (retrieval-augmented generation)The model looks up your real content before answering instead of relying on training data. This is what stops invented answers, and most of our RAG development work is in the retrieval half, not the model.
- Embeddings and vector searchHow a system finds content by meaning rather than exact words. The reason semantic search works.
- LLM integrationConnecting a model to your systems through an API. The easy part — the hard part is everything around it.
- EvaluationA test set of real questions with known good answers, run on every change. Without it, "the AI got better" is an opinion.
- GuardrailsRules on what the system may say and do, and what it must refuse. Where AI agent development succeeds or fails.
- Fine-tuningRetraining a model on your data. Rarely the right first step, and often sold when RAG would have done the job better.
Getting found inside AI assistants
Getting found inside AI assistants
If you sell online, this is the part most agencies have not caught up with. Shoppers now ask ChatGPT and Google AI Mode what to buy. This area moves fast and parts of it will change again — HelloDevs tracks it because our own products depend on it.
- What Shopify has enabledShopify distributes product data to AI platforms through Shopify Catalog, and merchants control it from the agentic section of the Shopify admin. As of mid-2026 that covers ChatGPT, Microsoft Copilot, and AI Mode in Google Search and the Gemini app, with availability varying by channel and market. Across every channel you stay the merchant of record — orders land in your admin with the AI channel attributed, like any other sales channel.
- Where the industry actually landedIn-chat checkout was announced loudly and then pulled back. The model that stuck is simpler: discover in AI, buy on your own site. For ChatGPT, the shopper finds your product in conversation and completes the purchase on your storefront through an in-app browser. What that means for you: the work is not a checkout integration — it is making sure your product data, content and site are good enough to be chosen and then convert.
- What we do about itWe audit your AI channel settings and catalogue distribution, clean and structure product data so AI systems can read it — titles, attributes, variants, availability — add the structured data AI systems and search engines rely on, fix the content gaps that stop your products being recommended, and make sure the landing experience converts someone arriving from an AI assistant who has already compared you against three competitors. If the storefront itself is what is slow, that is Shopify Theme Development territory, not this page.
Where AI goes wrong
- A model asked about your refund policy will invent one if it has not been given yours. Every answer must be grounded in your real content, with a hand-off when it is not.
- Generated copy that claims a material, a size or a certification that does not exist. Human approval before publish, always.
- Customer records, addresses and support history sent to a third-party model without an agreement in place is a legal problem, not a technical one.
- An AI widget that adds two seconds to a page costs more than it earns.
- Anything AI-generated needs a human override and an audit trail.
- Per-query pricing looks fine in testing and painful on your busiest day. We model it before you commit.
If a vendor will not tell you their failure modes,
they have not hit them yet. Every AI feature HelloDevs ships is designed around the list above before any code is written.
What you get
What every HelloDevs AI quote covers
Every HelloDevs quote covers all of this. If an agency leaves any of it out, the cost reappears later as a change request.
- Scoping documentThe problem, the metric it should move, and what is out of scope.
- The buildIntegrated into your product, not a widget dropped on top.
- Grounding setupThe AI answers from your content, data and policies.
- Human controlsApproval, override and off switch on anything customer-facing.
- Data handlingWhat is sent where, written down, with the agreements in place.
- Evaluation setReal questions with known good answers, so quality is measured rather than claimed.
- Regulatory checkGDPR, and where the EU AI Act applies to your use case, what it requires of you.
- Cost modellingWhat this costs per month at your real volumes, before you commit.
- Speed checkMeasured impact on load time, not assumed.
- DocumentationHow it works and how your team changes it.
- 30 days of post-launch fixesIncluded, not billed.
How an AI project runs
Five steps, and a metric we track after launch
We do not declare success at handover. Thirty days of fixes are included, and we keep watching the number we agreed to move.
- Problem callYou tell us the number you want to move. We tell you whether AI is the right tool for it, which it often is not.
- Fixed quoteA written scope, a timeline, and a price — including the monthly running cost, not just the build.
- Build and groundWe connect it to your real data first, because an ungrounded model is a liability rather than a feature.
- Test against realityReal questions, real edge cases, real volumes.
- Launch and watchThirty days of fixes included, and we track the metric we agreed on rather than declaring success at handover.
Our stack
Pure-play Shopify developers
We develop only for Shopify. No bloated frameworks, no visual page builders that degrade site performance, and no subcontracting. Just clean, hand-crafted code.
The AI Stack
Included because technical buyers ask, and because a vague answer here usually means the agency is reselling someone else's widget. Current frontier models via API, RAG pipelines over your own content, embeddings and vector search for semantic matching, evaluation sets so quality is measured rather than assumed, guardrails on what the system may say and do, and caching to keep per-query cost predictable.
- RAG
- Embeddings
- Vector Search
- Evaluation Sets
- Guardrails
- Next.js
- PostgreSQL
- lib/rag/retrieve.ts01
- lib/embeddings/index.ts02
- lib/guardrails/policy.ts03
- app/api/agent/route.ts04
Full Product Stack
Node.js, TypeScript, React, Next.js, PostgreSQL and MongoDB, deployed where your data rules require. On Shopify: Admin GraphQL API, Storefront API, metafields and metaobjects, and Shopify Catalog for AI channel distribution.
const chunks = await retrieve(
query,
{ topK: 6 }
);No Subscription Lock-In
We build it into your product. Not another monthly subscription you cannot switch off without losing the feature.
ai-widget-addon.zip
No Subcontracting
We do not subcontract. The people on the scope call are the people writing the code.
GroundShip
AI integration services questions
- Sometimes, and only for specific problems. Search, guided discovery and workflow automation reliably pay off where there is a large catalogue, a repetitive manual process, or a support queue full of settled questions. If the real problem is speed, pricing or product quality, AI will not fix it, and HelloDevs will say so before quoting.
- A focused proof of value — one narrow use case, tested against real data — takes four to eight weeks. A production system with evaluation, guardrails, security review and integration into your existing tools takes three to six months. HelloDevs will not quote the second until the first has told us whether the idea works.
- No. HelloDevs builds AI into custom web applications, SaaS products and internal tools as well as ecommerce stores. Shopify is where a lot of our work happens, not a requirement.
- By grounding every answer in your actual content and refusing to answer outside it. A model with no source for your refund policy will invent one, so the system hands off to a human instead of guessing, and every customer-facing answer has a human override.
- Only where you agree, and it is written into the scope before we build. Customer records, addresses and support history going to a third-party model without an agreement in place is a legal exposure, not a technical detail.
- That depends on volume and how many queries reach the model, which is why HelloDevs models the monthly cost before you commit rather than after launch. Per-query pricing that looks fine in testing gets painful at peak.
- If you sell on Shopify, yes — product data is distributed to AI platforms through Shopify Catalog and controlled from the agentic section of your admin. Availability differs by channel and market, and you remain the merchant of record. What decides whether you get recommended is the quality of your product data and content.
- Mostly not, and that changed during 2026. In-chat checkout was scaled back and the model that stuck is discover in AI, buy on your own site. This is good news: the work is in your product data and your site, not a checkout integration.
- Both are part of the scope, not an afterthought. The EU AI Act classifies AI systems by risk, and most commercial uses — search, recommendations, support — sit in the low-risk band with transparency obligations rather than heavy compliance. HelloDevs will tell you where your use case falls before building, and we are not lawyers, so anything borderline goes to yours.
- Usually not, and certainly not first. Fine-tuning changes how a model writes; RAG changes what it knows, and "it does not know our products" is the actual problem in almost every project. Fine-tuning is expensive, has to be redone as models change, and is often quoted when retrieval would have worked better.
- Usually yes. Most of this work happens through APIs and extensions rather than rewrites, though we will tell you if the existing system is the thing standing in the way.
Tell us the number you want to move
Send the problem, not the feature. A developer at HelloDevs replies within one business day.
Our talk
Tell us the number you want to move
Send the problem, not the feature. A developer at HelloDevs replies within one business day.
