XenonDev builds AI products and automates the operations around them. We develop AI agents, custom software, and SaaS platforms, then automate the business processes and workflows that support them. One team takes you from the first idea to a system running at scale.
Services
XenonDev's AI services split into two groups. Development builds new capability: applications, agents, and models that did not exist before. Automation removes cost from work your team already does manually. The engineering overlaps heavily, so one team handles both.
XenonDev reviews your operations and finds which problems AI can actually solve, ranked by return on investment. You get a detailed plan with costs you can follow, including advice to avoid AI when a simpler tool works better.
XenonDev maps how a process really works, removes unnecessary steps, and automates the rest. This includes approvals, passing tasks between departments, status updates, and the coordination work that quietly takes up the week.
Full AI products built around your data and users rather than a template. Architecture, model selection, evaluation, backend and frontend engineering, and deployment handled by the same team throughout.
Assistants trained on your own documentation that resolve routine questions and escalate the rest with full context attached. Built to say "I do not know" rather than inventing an answer, which is the difference between a useful assistant and a liability.
Products built on large language models from OpenAI, Anthropic, and Google, including drafting tools, analysis systems, and internal assistants. Where general models fall short, XenonDev fine tunes or grounds them in your proprietary data.
Voice agents for inbound calls, qualification, booking, and support, with speech recognition and response latency tuned so conversations do not feel stilted. Built with a clear handoff to a human whenever the caller asks.
Agents that complete multi step tasks independently. An agent gathers the context it needs, applies your business rules, calls the systems required, and finishes the job. Approval gates and audit logs stay in place wherever the decision carries consequences.
Forecasting, classification, scoring, and anomaly detection built and tested against your historical data. You see precision, recall, and error distribution before deployment, not after.
Search and question answering across your own documents, contracts, and internal knowledge using retrieval augmented generation. Answers stay grounded in your actual material, and updating information does not require retraining anything.
Automation only pays off when it reaches the software you already run. XenonDev integrates with Salesforce, HubSpot, SAP, NetSuite, Zendesk, Jira, and internal databases through APIs, webhooks, or custom connectors. Where no API exists, we look at database access or file exchange before calling it impossible.
Invoices, contracts, forms, and scanned paperwork read and structured automatically, with extracted fields validated against your records before anything is written to your systems. Uncertain cases go to a person rather than through a guess.
Deployment pipelines, version control for models and prompts, and monitoring for accuracy drift, latency, cost per request, and error rates. Models degrade quietly as your data shifts, so systems that are not monitored stop working without anyone noticing.
What we automate
AI can automate any process that happens often, follows a recognisable pattern even when the inputs vary, and has a recoverable cost of error. Below are the areas where XenonDev clients most often start.
Our process
The XenonDev AI development process runs in eight stages. The first four exist to prove the approach works on your data before significant money is committed to building it.
XenonDev maps how the work happens today, measures where time and errors accumulate, and identifies which processes are realistic candidates. Some assessments conclude that a rules based tool or a process change would solve the problem for far less, and we say so.
We assess what data exists, how clean it is, and what needs structuring before a model can use it. Messy data is normal and is often part of what the project fixes, so this stage rarely blocks anything.
Architecture, model selection, integration points, and the rules governing when the system escalates to a human. Choosing the right model matters less than defining what happens when it is wrong.
XenonDev builds the core capability and runs it against your real data, measuring accuracy honestly. You see whether the approach works before committing to a full build. Some prototypes show it does not, which is information worth having early.
Full development with connections into your CRM, ERP, helpdesk, or databases. Built in two week sprints so you see progress in a real environment throughout.
Systematic evaluation against a test set, edge case testing, load testing at expected volume, and security review. We deliberately look for the inputs that break it, because your users will find them otherwise.
Production deployment with monitoring for accuracy, latency, cost per request, and error rates. Alerting is configured so problems reach us before they reach your customers. Your team gets trained on what the system does and where it escalates.
Model accuracy drifts as your data changes and new edge cases appear under real volume. XenonDev reviews performance monthly, tunes what needs tuning, and identifies the next process worth automating.
Stack
XenonDev selects models and tools based on accuracy, latency, cost per task, and data residency requirements rather than on brand preference. Model choice is revisited at every project, because the performance and pricing landscape shifts every few months.
Commercial models from OpenAI, Anthropic, and Google for most production work, and open models such as Llama and Mistral deployed on your own infrastructure where data cannot leave your environment.
Vector databases for semantic search across your own documents, with retrieval augmented generation keeping answers grounded in your actual content rather than model memory.
Python with scikit learn, PyTorch, and XGBoost for forecasting, classification, scoring, and anomaly detection, evaluated against held out data before deployment.
Agent logic, tool calling, and multi step reasoning built in Python and Node, using LangChain or LangGraph where a framework helps and written directly where it does not.
For lighter workflows XenonDev uses Zapier, Make, or n8n when they solve the problem at a fraction of the build cost. Recommending the cheaper platform loses us revenue on that project and keeps the relationship.
AWS, Azure, and Google Cloud with containerised deployment, automated pipelines, and monitoring for uptime, accuracy drift, latency, and cost per request.
Case studies
Four systems running in production, with the numbers our clients measured after launch rather than the ones we hoped for during the pitch.
A legal and compliance team was spending entire days reviewing contracts by hand — hunting for risk clauses, pulling out key dates and obligations, and writing findings up for stakeholders. As contract volume grew, the review backlog itself became the business risk.
We built an ingestion pipeline that handles PDF, DOCX and scanned contracts through OCR, then an extraction layer on Claude that identifies risk clauses against a taxonomy defined with their legal team, scores them, and produces a structured summary. Anomalies are flagged for human review rather than decided automatically, and findings export to stakeholder reports in one click.
Read the full case studyA SaaS company's sales reps were each losing four or more hours a day reading uploaded proposals, contracts and intake forms, then retyping the contents into Salesforce fields. It was expensive, error-prone, and it was throttling the entire revenue cycle.
We mapped every document type to its target Salesforce fields, built an extraction pipeline combining document parsing with LLM field extraction, and wrote results straight to the correct records through the Salesforce API. A confidence score sits on every field: high-confidence extractions post automatically, low-confidence ones route to a human before anything is committed.
Read the full case studyAn HR team was burning three hours of admin per new hire: opening the signed offer letter, copying candidate details into the HRIS, and manually kicking off onboarding emails. Hiring volume was climbing and the process was already at its limit.
We mapped the full hire-to-onboard workflow, then automated it end to end. A signed offer letter triggers AI extraction of the candidate data, the HRIS record is created automatically, and a role-specific onboarding sequence fires immediately — with the whole chain orchestrated in n8n so the HR team can see and adjust it without us.
Read the full case studyA fast-growing retail group was processing hundreds of orders a day entirely manually — copy-pasting order data between systems, sending confirmation emails by hand, and reconciling inventory in spreadsheets. Errors were frequent, fulfilment was slow, and the team was burning out.
We audited the full order lifecycle, then built a pipeline connecting their storefront, warehouse system and courier APIs, with real-time inventory sync to prevent overselling. Standard orders now flow from placement to fulfilment with no human involvement, while an exception workflow routes the edge cases that genuinely need a decision.
Read the full case studyWhy XenonDev
A good AI development company will tell you when AI is the wrong tool, prove the approach on your real data before a full build, report accuracy honestly including where the system fails, and hand over full ownership. XenonDev commits to all four before a project starts.
XenonDev proves the approach on your real data during prototyping and reports the numbers honestly, including where the system performs badly. A model presented as flawless is a model that has not been tested properly.
Confidence thresholds, escalation paths, and complete audit logs ship as standard. You can always see what the system decided, why, and where a person intervened.
Plenty of problems presented to us as AI projects are better solved by a rules based script, a process change, or software that already exists. Saying so costs us one project and keeps the relationship.
You talk to the people building the system rather than an account manager relaying questions to a team you never meet. This matters most when something breaks.
Code, prompts, fine tuned models, documentation, and infrastructure configuration transfer to you. Take the system in house whenever you decide.
Monitoring for accuracy drift, latency, and cost per request comes with every deployment. Models degrade quietly as data changes, and unmonitored systems stop working without anyone noticing.
Cost
AI development cost depends on four things: how many systems need integrating, how complex the decisions are, how clean your data is, and whether the output requires human review. XenonDev issues a fixed quote after discovery so the figure does not move once work begins.
| Engagement | Typical timeline |
|---|---|
| AI assessment and roadmap | 1 to 2 weeks |
| Single process automation | 3 to 6 weeks |
| AI chatbot or assistant | 4 to 8 weeks |
| Custom AI application | 8 to 16 weeks |
| Machine learning model | 6 to 10 weeks |
| AI agent system | 6 to 12 weeks |
| Ongoing support and optimisation | Monthly |
Timelines are what comparable engagements have actually run to. The figure itself is fixed after discovery, once we know how many systems are involved and how ready your data is.
FAQ
AI development builds new capability such as a product, model, or feature that did not exist before. AI automation removes cost from work you already do manually. Same engineers, different business cases.
Cost depends on scope, data readiness, and how much of the system is genuinely new. A focused MVP is a far smaller commitment than a full platform. We quote fixed after discovery rather than giving a figure that drifts.
Pricing scales with the number of systems involved and the complexity of the decisions being automated. Most clients start with a single process build. If the payback period does not justify it, we say so.
A working MVP usually ships in 8 to 12 weeks. A single automated process typically goes live in two to six weeks. Larger programmes are delivered in stages so value arrives early rather than all at the end.
Robotic process automation follows fixed rules and suits identical, predictable steps. AI automation handles varied inputs like emails and scanned documents and copes with cases nobody wrote a rule for. Strong systems often use both together.
Yes, and for established products this is often the quickest path to a visible result. We integrate carefully so existing functionality stays stable.
You do. Code, models, prompts, workflows, and documentation all transfer to you at handover. There is no lock in and no licence to keep paying.
Confidence thresholds route uncertain cases to a person instead of forcing a decision. Every action is logged, so errors can be traced, corrected, and prevented from recurring.
In most of our projects it does not. Automation absorbs the repetitive portion of a role and leaves judgement, relationships, and exceptions to your people. Teams generally handle more volume at the same headcount.
Both. Early stage clients often suit the MVP route, where the priority is a working product in front of users quickly.
Access control, data minimisation, and encryption are set during architecture, with clear rules on what leaves your environment. Where a project carries specific regulatory requirements, we agree them in writing before the build starts and design to them.
Yes. We deliver remotely for clients across the United States, United Kingdom, United Arab Emirates, Canada, Australia and Europe, with scheduled overlap hours for your working day.
Other services
AI is one of several things we build. Clients often start with one service and expand into the others.
Full software products from discovery through launch, including MVPs, SaaS platforms, and mobile applications.
Explore our product development servicesBusiness systems, internal platforms, and bespoke tools built around how your company actually operates.
See our custom software developmentSubscription platforms built with multi-tenancy, billing, and permissions designed from the start.
View our SaaS development servicesTell us what you are trying to fix. In 30 to 45 minutes we will look at your processes, tell you which ones are realistic candidates for AI, and give you an honest view of effort, cost, and payback.
There is no charge, no obligation, and no sales script. If we are not the right partner, we will say so and point you somewhere better.
Prefer email? Write to hello@xenondev.com. We reply within one business day.