AI Development Services That Ship to Production
Most AI projects fail in the same place: a demo works on ten hand-picked examples, then falls apart on real user input, real data volume, and a real invoice at the end of the month. We build the unglamorous parts that decide whether an AI feature survives contact with production: retrieval that returns the right context, evaluation you can run before every deploy, guardrails on what the model is allowed to do, and cost tracking per request so the bill is a number you chose rather than one you discover.
- 56+ projects shipped
- 5 countries
- 24-hour response
Why teams choose us for AI Development
We are engineers who use these tools daily, not a consultancy that added AI to a slide. That shapes what we recommend. Roughly half the AI briefs that reach us do not need a model at all: they need a database query, a rule, or a better form, and we will say so before you spend anything. When a model genuinely is the right answer, we build it as a normal production system with logging, retries, fallbacks and tests, because an LLM call is an unreliable network dependency that occasionally returns confident nonsense, and it has to be engineered like one.
When AI Development is the right choice
AI is the right tool when the input is genuinely unstructured and the output is genuinely open-ended: reading messy documents, answering questions across a large knowledge base, drafting text a human will edit, classifying free-text at a volume nobody wants to read. It is the wrong tool when the answer is deterministic, when a wrong answer is expensive and cannot be checked, or when you need the same output every time. If that is your case, we will build you the boring system that actually solves it.
Our AI Development Process
We start by asking what happens if the answer is wrong. If a wrong answer is expensive and unverifiable, we will usually talk you out of the AI approach and into a deterministic one. This conversation is free and often ends the project early, which is the point.
Before any prompt work, we collect real examples with known-good answers. Without this you cannot tell whether a change improved anything, and every later decision becomes a matter of opinion.
Most quality problems are retrieval problems, not prompt problems. We get the right context in front of the model first: chunking, embeddings, ranking and filtering, measured against the evaluation set.
Timeouts, retries with backoff, fallbacks when the provider is down, structured output validation, and logging of every request and its cost. An LLM is a flaky network call that sometimes lies, and the system around it has to assume that.
Per-request cost tracking and budget ceilings from day one, plus a dashboard showing spend by feature. You should never learn what an AI feature costs from the invoice.
What we deliver
- Retrieval-augmented generation over your own documents and databases
- AI agents with tool access, scoped so they can only do what you allow
- Model Context Protocol (MCP) servers that connect assistants to your systems
- LLM integration into existing products: OpenAI, Anthropic, and open models
- Document extraction and classification for invoices, contracts and forms
- Evaluation suites so a prompt change cannot quietly break output quality
- Cost and token controls with per-request tracking and hard budget limits
- Chatbots and support assistants grounded in your own content, not the open web
Transparent pricing
One well-scoped feature in an existing product: a grounded chatbot, document extraction, or classification, with evaluation and cost tracking included.
Retrieval over your own data, or an agent with tool access to your systems. Includes ingestion pipeline, evaluation suite, guardrails, observability and cost controls.
Multi-feature AI built into a product: several models, MCP servers connecting internal systems, per-tenant budgets, and the evaluation and monitoring to run it safely.
Tech stack we use
Frequently asked questions
Do we actually need AI for this?
Often not, and we will tell you before you spend anything. Roughly half the briefs that reach us are better solved by a database query, a rule, or a clearer form. AI earns its place when the input is genuinely unstructured and the output is genuinely open-ended. If your problem is deterministic, a model just makes it slower, costlier and less predictable.
How do you stop it making things up?
Grounding and checking. The model answers only from context we retrieved from your data, we validate the structure of what comes back, and we make it cite the source so a human can verify it. For anything where a wrong answer is expensive, we design a human approval step rather than pretending the problem is solved. No prompt makes a model incapable of being wrong.
What will it cost to run, not just to build?
That depends on model choice, context size and volume, and it is one of the first things we size rather than one of the last. We build per-request cost tracking and hard budget ceilings in from the start, and we route cheap requests to cheap models. A typical grounded support assistant runs a few thousand rupees a month at moderate volume; a heavy document pipeline can run considerably more, which is exactly why it gets measured before launch.
Whose data does the model see, and where does it go?
We use enterprise API tiers where prompts are not used for training, and we scope precisely what leaves your systems. Where data cannot leave at all, we build on open models you host yourself. This gets settled in scoping, not discovered during a security review.
What is MCP and do we need it?
Model Context Protocol is a standard way to give an assistant controlled access to real tools and data, so it can query your systems instead of guessing. It is useful when you want an assistant to actually do things rather than only talk about them. If you just need a grounded chatbot over a set of documents, you do not need it.
Can you add AI to our existing product without a rewrite?
Usually yes. Most AI features attach at the edges: a new endpoint, a background job, a panel in the admin. We would rather add a narrow feature to what you have and measure it than propose rebuilding a working product around a model.
Have an AI feature you need built properly?
Free 30-min consultation. No pitch deck, no hard sell, just an honest scoping call.
Other services
We build and operate EV charging software, not just write it. buildbyravirai runs PlugEV, a production OCPP 1.6-J charge point management system (CSMS) live on 40+ chargers across two Indian cities since early 2025. We build the full stack an EV charging operator needs: the OCPP gateway that talks to the hardware, RFID and UPI billing with automatic GST invoices, the operator dashboard, and the driver-facing app.
We build rental and tracking software: live GPS and IoT tracking, geofencing, booking, KYC, and billing for businesses that rent out vehicles, bikes, or equipment. This sits on real work. We have built telemetry pipelines and firmware bridges for connected hardware, including an EV cycle's rider app and dashboard and the telemetry behind PlugEV.
We've shipped 7+ Shopify stores for D2C brands, B2B catalogs, and subscription businesses across India, USA, Canada, UK, and UAE. Whether you're launching your first store on Basic Shopify or replatforming a 10,000-SKU catalog onto Shopify Plus, our team handles the full build, strategy, theme development, app integration, payments, shipping, and post-launch growth.