Macronimous serves as an AI development company in India that builds AI apps and integrations from written specs. Bring a finished spec, rough requirements, or only an idea. Senior engineers turn it into a build-ready specification, then build it with AI inside your product and AI coding tools behind the build. We’ve been serving the US, UK, and Australia since 2002.
Most AI development you can buy right now is a demo, or a page of promises that reads the same on every agency website. We work the other way. We already build and maintain the WordPress, Magento, and React stacks AI features live in, so the feature ships to production and survives the next platform upgrade. If you are weighing an offshore AI partner, here is why an India-based team is a sound choice when the track record is real, not a leap of faith.
What We Build
Five lines, all tied to the web and commerce work we already do. We lead with what we can actually ship, not a menu of everything AI can theoretically do.
1. AI Integration Services for Your Existing Web and Commerce Products
If you already have a live store or site and want AI inside it, this is the most common build we staff. The feature is engineered to survive a Magento or WordPress upgrade, not bolted on as a widget that breaks at the next release.
- Semantic and AI-assisted search that understands intent, not just keyword matches
- Product recommendations driven by behavior and catalog relationships
- AI-generated product descriptions at catalog scale for Magento, Shopify, WooCommerce, and PrestaShop, with human review built into the workflow
- AI content tools inside WordPress for drafting, summarizing, and tagging at scale
- Classification, extraction, and personalization wired into your existing data and templates
These sit on top of the commerce engineering we do every day. See our eCommerce development and React and Magento storefront work for the stack underneath.
2. Custom Chatbots and RAG Assistants
A chatbot is only as good as the data behind it. We build retrieval-augmented assistants grounded on your own content, so answers come from your documents, catalog, and tickets, not a generic model guessing.
- Customer support assistants grounded in your product docs, policies, and order data
- Internal knowledge assistants that let your team query scattered documentation in plain language
- Retrieval pipelines with vector search so the assistant cites real sources instead of inventing answers
- Guardrails and human handoff for the questions a model should not answer alone
- Model-agnostic builds across OpenAI, Anthropic, Gemini, and open models, so you are not locked to one vendor
3. AI App Development Services for Web and Mobile
When the build is a new product rather than a feature on an existing site, we develop the whole application with AI at its core. The same engineering teams that ship our web and mobile work build these, from the same kind of written spec.
- AI web applications in React, Next.js, and custom PHP, from MVP to production scale
- AI mobile apps in React Native, with on-device or API-backed model use
- WordPress-based AI products when the content and publishing layer is the foundation
- Model integration and orchestration so the AI logic is a clean, testable layer, not scattered through the codebase
- A proof of concept where the risk is real, run on your own data, so the riskiest part of the spec is tested before the full build
For the underlying delivery, see our WordPress development and React Native mobile app services.
4. Custom AI Extensions and Plugins for Magento, WordPress, and PrestaShop
Often the highest-value AI is not a new app. It is a small extension that removes repetitive work inside a tool your team already uses every day. We start by finding where that work sits: the manual steps, the copy-paste, the report someone rebuilds by hand each week. Then we build the extension that handles it, inside the platform you already run.
- Magento and Adobe Commerce modules for AI product copy, catalog tagging, order triage, and support macros
- WordPress plugins for AI content workflows, editorial tagging, internal search, and moderation
- PrestaShop modules for AI merchandising and category and attribute automation
- Custom PHP and React admin tools that wire AI helpers into the dashboards your team already works in
- A productivity audit first, so we build the extension that pays back, not the one that looks impressive in a demo
This is the same extension and module engineering we have shipped for years, now with AI inside it. See our Magento development work for the platform depth underneath.
5. White-Label AI Development for Agencies
Your clients are asking for AI, and you do not have the engineers to build it. We do the work under your brand. Same model we have run for offshore web delivery since 2002, applied to a new demand.
- Your brand, your repo, your client relationship. We do not contact your client
- NDA before the first commit and IP transfer written into the contract
- Fits your delivery process across Slack, Jira, Basecamp, and your sprint cadence
- Senior engineers who can defend the architecture on a client call if you need them to
This is how we have always worked. Read more about the team and how we deliver.
How We Build: Spec-Driven Development, Then AI-Assisted Coding
Spec-driven development means we write or review a specification before any code exists, lock the acceptance tests that define “done,” and only then build with AI tools against that document. It’s how we keep AI-written code accountable to something a person signed off on.
You bring what you have.
A finished spec, a requirements document, a few pages of notes, or an idea you can explain on a call. Any of these is a fine starting point.
We turn it into a build-ready spec.
We define features, the data model, integrations, how the AI in your product should behave, and the acceptance tests. If you sent a spec, we review it and fill the gaps. Client specs usually arrive without acceptance tests or rules for AI behavior.
Senior engineers build against the spec.
They write code with AI coding tools, including Claude Code, Cursor, and OpenAI Codex. The spec decides what gets built. The tool doesn’t.
Every AI-written change gets reviewed.
An engineer reads it and runs it against the acceptance tests before it goes in. Nothing ships because a model said it works.
The spec stays with you.
It lives in your repository as a working document. When you add a phase or change teams, the next people start from facts instead of guesswork.
Prompting a model until something seems to work, the habit people call vibe coding, is fine for a prototype. A product you’ll pay to run needs a written spec someone can be held accountable for.
The spec also changes how you pay. Once it’s locked, we send a detailed fixed-price proposal for the build, priced line by line against the spec, so the scope and the price live in the same document.
We use two kinds of AI in our work, and most projects use both. AI inside the product is the feature your users touch: search, a chatbot, a report that writes its own summary. AI behind the build is how our engineers write the code. The spec governs both. It defines what the AI feature must get right, and it’s the document we check the AI-written code against.
Paid Services Before the Build
Some clients need the thinking before they need the code. Both services below stand on their own. You can take the result to us, to another team, or in-house.
Specification Building
You bring an idea or rough requirements. We turn them into a build-ready spec: features, user flows, data model, integrations, AI behavior, and acceptance tests. You own the spec outright, and any competent team can quote and build from it.
Tech Stack and API Consulting
We recommend the stack, the AI models, and the APIs for your build, with the trade-offs written down: monthly running cost, vendor lock-in, data handling, and what breaks when a provider changes its API. Useful when you have a spec and want a second opinion before you commit.
When You Do Not Need AI, and We Will Tell You
A rules engine, a well-built search index, or a cached lookup often beats a language model on cost, speed, and reliability. We scope the cheapest solution that actually works, and we are explicit about what client data and source code we ever send to a third-party model. If your problem is better solved without AI, we’ll tell you that on the first call, before you spend anything. That is the opposite of how most AI shops are paid to think, and it’s why agencies keep coming back.
How We Price AI Work
Three engagement models. Pick the one that matches your scope.
Fixed-Price Build from a Spec
Best for: A defined AI app, feature, or extension.
Rate: A detailed fixed-price proposal once the spec is locked.
Commitment: Per project.
Hourly
Best for: Discovery, productivity audits, proofs of concept, and iterative work.
Rate: USD 25 to 30/hr.
Commitment: No minimum.
Dedicated AI Engineer
Best for: Ongoing AI work alongside your product roadmap.
Rate: Contact us for monthly rates.
Commitment: Minimum 3 months, 1-month notice thereafter.
Model usage costs, meaning what OpenAI, Anthropic, or Google charge per request, are listed separately from our fees so you see both numbers. Change requests after the spec is locked are handled the way our scope-change policy describes.
What You’re Committing To
Clear terms up front, so nothing is a surprise later.
Your Repository, Your Cloud Accounts
Mutual NDA before the first commit. Code, prompts, and the spec live in your repository. Hosting and AI provider accounts are in your name. No retained IP.
An AI Evaluation Plan in Every Spec
The spec defines how the AI feature is measured before launch: accuracy, hallucination (the model stating false things as fact), response time, and model cost per request.
A Security Check Before Launch
An engineer checks your database access rules, meaning who can read and write which records, and verifies payment webhook signatures, the check that proves a payment notice really came from your payment provider.
Support When AI Providers Change
Model providers retire models and change APIs on their own schedule. Post-launch support covers those changes, so a provider update doesn’t quietly break your feature.
We Document What Leaves Your Servers
Before any build, you get a plain-language map of which data, if any, is sent to which model provider, and where it is processed.
Model-Agnostic, No Lock-In
We build so you can switch between OpenAI, Anthropic, Gemini, or an open model without a rewrite. The provider is a setting, not a dependency.
Direct Developer Access
You work directly with the engineer in your Slack, Jira, or Teams. A project manager coordinates, but does not gatekeep daily communication.
No-Questions Replacement
If the fit is wrong on a dedicated engagement, we replace the developer. No reason required, no billing gap on your side.
White-Label by Default
For agencies, we work under your brand and stay out of your client relationship unless you ask us in.
Have a spec, or only an idea?
Send what you have. We’ll tell you what’s missing from it, whether AI is the right tool, and how we’d price the build. Same-day response during UK/EU hours.
Frequently Asked Questions: AI Development
What does Macronimous do as an AI development company?
We build and integrate AI into web and commerce products. That means AI features inside your existing WordPress, Magento, or React application; custom AI extensions and plugins; chatbots and RAG assistants grounded in your data; new AI web and mobile apps; and white-label AI development for agencies. Every build starts from a written spec with acceptance tests. We do not train foundation models. We build production AI that works inside the stack you run, and we have built software for US, UK, and Australian clients since 2002.
What is spec-driven development?
Spec-driven development is a way of building software where a written specification comes first and the code is built against it. The spec covers the features, the data model, the integrations, how any AI in the product should behave, and the acceptance tests that define “done.” With AI coding tools, the spec matters even more, because it’s the document every AI-written change is checked against. At Macronimous, an engineer reviews each change and runs it against those tests before it goes in, and the spec stays with you after the project ends.
We want a team that builds with AI, but not blindly. How do you keep AI-written code under control?
Nothing gets built until there’s a spec. If you already have one, we review it, fill the gaps, and build from it. If you don’t, we write it with you. Either way, the spec lists the features, the data model, the integrations, how the AI inside your product should behave, and the acceptance tests that define “done.” Our engineers then build against that document with AI coding tools, Claude Code among them. An engineer reviews every AI-written change, and the change has to pass the acceptance tests before it goes in. Prompting a model until something seems to work is fine for a prototype. A product you’ll pay to run needs a written spec that someone is accountable for, and that spec stays with you when the project ends.
I already have a spec. Can you build from it?
Yes. We read it first and send back a short gap list: missing acceptance tests, unclear AI behavior, integrations with no error handling, data questions nobody answered. Once you approve the fixes, the spec is locked and we send a detailed fixed-price proposal for the build. You don’t have to rewrite your document in our format. We work from yours and mark the changes so you can see exactly what we added.
I only have an idea. Can you write the spec?
Yes. That’s our Specification Building service. We start with a call about the problem, the users, and the systems the product has to talk to. Then we write a build-ready spec covering features, user flows, data model, integrations, AI behavior, and acceptance tests. You own the spec outright. You can have us build it with a fixed-price proposal, hand it to another team, or build it in-house. If you’re unsure which stack, models, or APIs to use, our Tech Stack and API Consulting service answers that before the build starts.
Which AI tools do your engineers use?
For writing code, our engineers use AI coding tools such as Claude Code and OpenAI Codex. They’re fast, and they’re only as good as the instructions they get, which is why every build runs against a written spec and every AI-written change is reviewed by an engineer. The AI inside your product is a separate decision. We choose between OpenAI, Anthropic, Gemini, or an open model in the spec, based on accuracy, cost, and data handling, and we build so you can switch providers later.
How do you test AI features before launch?
Against the evaluation plan written into the spec. We build a test set of real inputs with expected answers, then measure accuracy, hallucination (the model stating false things as fact), response time, and model cost per request. The feature also has to pass the spec’s acceptance tests. Before launch, an engineer runs a security check on database access rules and payment webhook signatures. After launch, support covers the changes AI providers make to their models and APIs.
Who owns the code and the spec?
You do, 100%. A mutual NDA is signed before the first commit, and the contract includes a written IP transfer clause covering source code, custom modules, prompts, retrieval logic, and the spec itself. Work happens in your repository, and hosting and AI provider accounts are in your name. There is no retained IP and no licensing held back. If you change teams, you leave with the code and the document that explains it.
Can you add AI to my existing site, or build a new AI app?
Both. Adding AI to a live Magento, WordPress, or React application is our most common build, because we already engineer that layer and can integrate into your real codebase and data. When the project is a new product instead, we develop the whole application with AI at its core: AI web apps in React, Next.js, and custom PHP, and AI mobile apps in React Native. Either way we work in your repository, under your review, and start from a written spec.
Will our data or source code be sent to third-party AI models?
Only what is strictly required, and you approve it first. Before any build, you receive a plain-language map of which data leaves your servers, which provider receives it, and where it is processed. For sensitive workloads we can use zero-retention providers or run open models in your own environment. You decide the boundary, and it is written into the engagement.
Do you offer white-label AI development for agencies?
Yes, and it is a core offering. We build under your brand, in your repository, and stay out of your client relationship unless you ask us into a call. NDA before the first commit, IP transferred to you, and delivery that fits your existing sprint process and tools. This is the same offshore model we have run for web development since 2002, applied to AI.
How much does AI development cost?
Spec-based builds get a detailed fixed-price proposal once the spec is locked, priced line by line against it. Discovery, productivity audits, proofs of concept, and iterative work are billed hourly at USD 25 to 30 per hour with no minimum. For a full-time dedicated AI engineer, contact us for monthly rates. Model usage costs, meaning what OpenAI, Anthropic, or Google charge per request, are listed separately from our fees so you see both numbers.
Related Questions
Ready to scope an AI build?
Send your spec, your notes, or the problem you want solved. You get an honest read on whether AI fits, a list of what your spec is missing, and a fixed-price proposal once the spec is locked.
