All Case Studies
AI work we already run, for clients and for us
Before we build AI into your product, we have shipped it elsewhere: skin analysis in a client's shopping app, a ChatGPT connector to our CMS and embedding search in our own operations.
AI skin analysis in production
Natually app, Google Play For Natually, the second brand of CuraSkin Solutions, we built a Flutter shopping app with AI skin analysis, voice search and photo search. It has been on Google Play since October 2026 and shares one catalogue with the website.
Model choice decided by tests
No favourite provider We run the same set of your real examples through OpenAI, Anthropic Claude, Google Gemini or an open model. The one that answers well at a cost per request you can accept gets the job, whichever company makes it.
Embedding search we use daily
Semantic search, in-house Our own task system matches each piece of work to the right specialist using sentence embeddings, so a request finds the right person even when the words differ. We build the same kind of search over your catalogue or documents.
Customer data kept private
Keys stay server-side API keys live on your server, never inside the app. We remove personal details a model does not need and log what is sent. On our Amazon Profit Calculator, connected account tokens are stored encrypted with AES-256-GCM.
Senior engineer approves releases
Human review, every change AI tools help us research and draft code faster, but a senior engineer reviews each change before it ships. The person who scopes your feature also joins the weekly call and writes the plain monthly recap.
Ratings you can check
4.8 from 127 reviews Our work with brands goes back to 2016, across 500+ projects and campaigns. Google shows a 4.8 rating from 127 reviews, GoodFirms and Clutch hold more client reviews, and the Shopify Partner directory lists 17 reviews at 5.0.
HOW WE WORK
See how Dcrayon plans, builds and reviews client work
Our general film about client work: one senior owner per account, a written plan, a weekly call and a short written summary each month.
What is AI Development?
AI development is building features into software that use AI models to see, read, search or suggest, such as analysing a photo, finding products by meaning or pulling fields out of a document.
Step 1: A feature, not a chatbot
Definition Most AI development is a feature inside a screen people already use: a camera button that reads skin type, a search box that understands 'cotton kurta for summer', or an upload that turns a PDF invoice into rows. Users may never see the word AI.
Step 2: Wired into one product step
Differentiation A chatbot holds a conversation. AI development places a model inside one step of your product and checks its output against rules before anyone sees it. Conversation-first products sit on our AI Chatbot Development page; this page covers the features.
Step 3: Scored on your examples
Measurement Before launch we collect real examples from your business, each with the right answer written down. Every model is scored on that set for correct answers, cost per request and response time, and the same set runs again after every change.
AI features we design, build and run
Each one tested on your data, with a cost cap and a human owner.
We start with one feature tied to one number you care about, such as search-to-cart rate or hours spent on data entry. Then we build it, test it, ship it and watch it in production.
Image and photo analysis
Image checks inside Flutter or web apps.
For Natually we built AI skin analysis and photo search into the shopping app, linked to the same product catalogue as the website.
Voice search for apps
Shoppers speak a query and the app turns speech into a search.
In the Natually app, voice search sits beside text and photo search, and all three return products from one catalogue.
Semantic search with embeddings
We turn products, articles or help pages into embeddings so search matches meaning, not only words.
Embeddings can sit in PostgreSQL with the pgvector extension, next to your existing data.
Recommendations from shopper behaviour
Suggestions based on what a shopper viewed, bought or searched.
Simple rules come first and AI ranking is added where it helps, tested against your current related-products block.
Document reading and extraction
Invoices, purchase orders, KYC forms or price lists read by a model and returned as clean fields.
Each field gets a format check, and unclear ones go to a person instead of the database.
Cost and quality monitoring
A view of cost per request, error rate and response time for each feature.
An alert fires when spend nears the monthly cap you set, and the test set runs again before any model update.
Three steps, from AI idea to working feature
Every AI feature starts with a free review, moves into a written 90-day plan, and is built with our in-house Dcrayon AI tools under senior sign-off.
Growth Intelligence (AI Development review)
Our free 150-point review checks your data, your current software, where AI could save time or lift sales, your privacy rules and the likely cost per request.
Dcrayon Growth Engine (AI Development plan)
A written 90-day plan for one AI feature, aimed at one number you name, like photo search use or minutes saved per order, with test targets and a cost cap.
Dcrayon AI (AI Development toolkit)
In-house tools for test sets, model comparison, prompt versions and cost logs. They speed up the engineering, and a senior engineer approves every release.
How Dcrayon builds an AI feature
Four steps, each with a written check before the next one starts.
Step 1: Scope
We pick one feature, name the number it should move, and write down what a good answer looks like using real cases from your business. We also list what the feature must never do.
Step 2: Test models
The same cases run through OpenAI, Anthropic Claude, Google Gemini, and an open model where data rules need one. We compare accuracy, speed and cost per request, and share the scores with you.
Step 3: Build
We wire the chosen model into your Python, Laravel or Node.js backend, or your Flutter app, with output checks, retries, logging and a fallback for when the model is slow or unsure.
Step 4: Watch
After launch we track answers, cost and complaints, run the test set again before any model or prompt change, and report on the weekly call and in the monthly recap.
Get my free AI development review
Tell us the feature you have in mind. We check your data and software, then send a written view on cost, risk and fit.
+187%
Median sales lift, first year
Other Artificial Intelligence services
Need a chat assistant, an agent that takes actions, AI connected to your CRM, or advice before you build? Each service below has its own page.
Numbers from client work across Dcrayon
AI features sit inside wider projects. These figures come from the full client engagements we run.
What clients say about building with Dcrayon
Reviews from owners who worked with our engineers, taken from Google, GoodFirms and Clutch.
“On time delivery, supportive team. [Dcrayon's] on-time delivery was commendable.”
“The process was smooth and professional. The project achieved strong measurable results, including a 35% increase in website traffic after launching the Shopify website.”
“They ensure all campaigns go live as scheduled without delays. Since [Dcrayon] took over our digital marketing, we consistently achieved our sales targets, especially during festive sales.”
“Dcrayon made everything right. We commissioned a website design + development build from scratch. it shipped on schedule with responsive adjustments through the review cycles and met the Google feature compatibility we needed.”
“Their attention to detail and compliance-focused approach helps build a stronger and more sustainable business. Initially they ask for documentation many sellers find difficult to provide. that is exactly what sets them apart. They now also offer USA seller account management. Loved the service. Bestseller in 3 category.”
“Dcrayon took our Amazon account from steady but flat to explosive growth: 180 percent more revenue, from Rs 1.82 crore to Rs 5.10 crore.”
Keratine Professional
Salon-grade Hair Care on Amazon
WHY DCRAYON
AI built by people who ship it and run it
Proof you can open and check
See the Natually app case study for AI skin analysis and photo search, the Amazon Profit Calculator for how we protect connected accounts, and Dcrayon AI for the tools we use on every build.
Page owner: Rahul Yadav, founder of Dcrayon. Last updated 10 November 2026.
No favourite AI company
We are not tied to one provider. The model that wins on your test set gets the job, and we can switch later if prices or quality change.
Tests before launch
No feature goes live until it passes a written test set built from your real cases. You see the scores before you approve the release.
Cost per request in writing
You get an estimate of what each request costs before the build and a monthly cap after it, so a busy month never brings a surprise bill.
Minimum data sent out
We send a model only the fields it needs, keep keys on the server and choose provider settings that match your privacy rules.
One full-stack team
The same team builds the backend in Python, Laravel or Node.js, the app in Flutter and the database in PostgreSQL, so AI is part of the product from day one.
Honest when AI loses
If the tests show AI is not good enough for a task, we say so and suggest a simpler rule or a manual step instead of shipping a weak feature.
Get a free AI feature review and a written 90-day build plan
We look at your data, your software and the feature you want, then write a plan tied to one number. The review costs nothing, and Dcrayon has worked with 485+ clients.
150-point review of your data, software and privacy rules
Model shortlist with an estimated cost per request
A 90-day build plan with one target number you pick
A senior engineer on the weekly call, not a sales rep
Question & Answer
Frequently asked questions
Straight answers on AI development: cost, timing, data, contracts and how models are chosen.
An AI development company builds features that use AI models inside your own app, website or software. For example, we built AI skin analysis, voice search and photo search into the Natually shopping app on Google Play. Other common features are meaning-based search, product suggestions and reading documents into clean data fields.
Each build gets its own fixed quote, given after the free review, before any work starts. The quote covers design, build and testing. Model usage is billed separately by the AI provider for each request, so we estimate that running cost during testing and set a monthly cap that you approve.
It depends on scope and on how ready your data is, so we do not promise a launch date before the review. The 90-day plan sets milestones. The first one is usually a small test on your real examples, so you can judge actual answers and costs early, before the full build.
We need real examples of the task with the right answers, access to the software the feature will live in, and one person who can approve results. We also ask for your data rules, such as which customer details may leave your servers, and a list of things the feature must never do.
We run AI systems in our own business before we sell them, including a ChatGPT connector to our CMS and embedding search in our task system. Every client feature is tested against real cases, models are chosen on scores rather than habit, and a senior engineer signs off each release.
No. The first engagement is one 90-day cycle, with one written plan and one target number you choose. After it, the work continues on monthly terms, and you are free to end any agreement. You hear from us on a weekly call and read a plain recap every month, so progress is never a guess.
No single model is best for every task, so we test before choosing. We run the same set of your real examples through OpenAI, Anthropic Claude, Google Gemini and, where data must stay in-house, an open model on your own server. Then we compare correct answers, response time and cost per request, and share the scores.
Tell us the AI feature you want
Share the task and the software it lives in. A senior engineer replies with questions, not a sales script.
Start with one AI feature that earns its place
Pick the job that eats the most staff hours, or the step where shoppers leave. We test whether AI can do it well, and at what cost per request.
If the tests say AI is the wrong tool, we tell you plainly. You still keep the written review and the scores.