Most businesses don't need "more AI." They need a specific job done faster: invoices read automatically, customer questions answered at 2 a.m., sales numbers that actually predict next month. CodeXoro is an AI development company that builds those things. We design and ship custom AI applications, chatbots, AI agents and machine learning systems, and we connect them to the software your team already uses.
No two companies run the same way, so we don’t sell one-size-fits-all AI. We start with how your team actually works, find where AI will save real time or money, and build around that.
Have a product idea, or a process that's stuck in spreadsheets and email? We build AI-powered web and mobile software around it, from a first working version to a full platform your customers or staff use every day.
If you've got a few years of sales, usage or operations data, there's probably a pattern in it you're not using yet. We build models that forecast demand, flag unusual activity, score leads and recommend next steps.
We build tools on top of large language models that write, summarise, search and answer questions in plain language. Think a sales team that gets a draft proposal in minutes, or staff who can ask a question and get an answer pulled from your own documents.
A good chatbot answers the common questions quickly and hands the tricky ones to a person, with the full conversation attached. We build assistants for websites, WhatsApp, mobile apps and internal helpdesks.
An agent goes a step beyond a chatbot. It can look up an order, update a CRM record, draft an email or kick off a multi-step workflow. We set clear limits on what each agent may do and add approval steps for anything sensitive.
We turn the numbers you already collect into forecasts and early warnings: which customers may leave, which products will run short, which projects are likely to slip.
AI is not a fix for every problem. It works best where there's a lot of repeated work, a lot of information, or a lot of waiting. If you recognise any of the situations below, it's usually worth a conversation.
Someone on your team retypes data from PDFs, emails or forms every single day. AI can read those documents, pull out the details and enter them into your system, leaving people to check exceptions instead of doing the typing.
When the same ten questions make up most of your inbox, customers wait while your team repeats itself. An assistant handles the routine ones instantly and passes the rest to the right person.
If every new customer means hiring another person for admin, margins shrink. Automating the repeatable parts of a workflow lets the same team handle more.
Pricing lives in one spreadsheet, customer history in the CRM, policies in a shared drive nobody can search. An AI assistant connected to these sources lets people ask a question and get one answer, with a link to where it came from.
Ordering stock, planning staff or choosing which leads to chase is hard without a view of past patterns. Forecasting models give you a starting point backed by your own data, and you still make the final call.
We pick technology to suit the project, not the other way round. A simple FAQ assistant doesn't need a custom-trained model, and a demand forecast doesn't need a language model. Here's the toolkit we draw from.
Plugging an AI model into an app takes an afternoon. Making it accurate, secure and useful for months afterwards is the real work. Here's what that covers.
We start with the problem, not the model. Here's how a typical project runs.
We talk to the people who do the work today. We map the current process, find the slow or costly steps and agree on what success looks like, for example "cut invoice handling time in half."
Before committing to a full build, we check your data, your systems and the technical options. If a simpler or cheaper approach works, we'll tell you.
We plan how data flows, where the AI sits in your system and what the user will see. You review wireframes and the technical plan before we write production code.
We build in short cycles and show you working software regularly, so you can react early instead of waiting months for a big reveal.
We test against real business scenarios, track accuracy, and add guardrails and human-review steps where mistakes would be costly.
We deploy to your environment, train your team and keep watching performance. Then we refine, based on how people actually use the system.
These are common starting points. Most projects combine two or three of them.
An assistant trained on your help articles, policies and order data that resolves routine questions and escalates the rest with full context.
Software that reads invoices, contracts, forms and reports, extracts the fields you need and sends them to your accounting or CRM system.
Suggestions for products, courses, content or next actions, based on what each user has browsed, bought or asked about.
An internal search assistant that answers staff questions from your manuals, policies and past projects, and shows the source document.
Dashboards where you can ask "why did sales drop in March?" in plain English, plus automatic trend alerts and summaries.
Approvals, routing, data entry and notifications that run on their own, with AI handling the steps that need reading or judgement.
It means building software that uses AI for your specific business need, rather than using a generic tool. Examples include a support assistant trained on your documents, a model that forecasts your own sales, or a system that reads and files your invoices.
We build custom AI software, generative AI tools, machine learning models, chatbots, AI agents, predictive analytics, recommendation engines, computer vision apps and workflow automations.
Yes, and this is one of the most common requests. We connect AI features to existing websites, mobile apps, CRMs and e-commerce platforms through APIs, so you keep what already works.
Not always. Many useful applications, like document assistants and chatbots, work with existing language models plus your documents. Custom prediction models do need historical data, and we'll tell you honestly during discovery whether yours is enough.
Traditional machine learning predicts or classifies: will this customer churn, is this transaction suspicious. Generative AI creates new content such as text, summaries or images from instructions. Many projects use both.
It can be. We use access controls, encryption and role-based permissions, and we help you choose between a hosted AI provider and a privately hosted model depending on how sensitive the data is. We'll explain exactly where your data goes before you agree to anything.
A focused proof of concept typically takes a few weeks. A full production application with integrations usually takes a few months. We'll give you a realistic timeline after discovery.
It depends on the scope, the integrations, the amount of data work and the ongoing model and hosting costs. We can give you a fixed estimate for a defined first phase, which keeps the risk low. [Add a "starting from" range here if you're comfortable publishing one. Pages with a price signal tend to convert better.]
Yes. Agents can look up records, update systems, draft and send messages and run multi-step workflows. We limit what each agent can access, log what it does and require human approval for sensitive actions.
You don't need a technical spec or a big budget to start. Tell us what's slowing your team down or what you'd like to build. We'll tell you whether AI is the right tool, what a sensible first version looks like and what it would take. From custom AI software and chatbots to machine learning and automation, CodeXoro turns practical ideas into working systems.
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