Clear answers about how our AI development company plans, builds and supports custom AI solutions. Search below or browse by topic to learn about our services, process, pricing and data security.
Talk to the engineers who would scope your project. We reply within 24 hours.
Who we are, where we work and the kind of companies we help.
DenebrixAI is an AI development company that designs, builds and deploys custom artificial intelligence solutions for startups, mid-size firms and enterprises. Our team works across generative AI, AI agents, machine learning, computer vision and full-stack software, so one partner can take your idea from strategy to production.
We have offices in London, United Kingdom, and Brooklyn, New York. Our engineers work with clients worldwide, with meetings planned around your time zone so collaboration stays easy.
Our team brings more than 10 years of collective agency experience and more than 46 specialized AI engineers. Together we have delivered over 120 AI projects, from early prototypes to production systems that process millions of data points.
We work most often with healthcare, retail, finance, manufacturing, telecom, utilities, supply chain, automotive and agritech companies. Every industry has its own data rules, so we adapt models, security controls and integrations to the regulations you operate under.
Yes. Our case studies include healthcare products such as 360Alert and the AI Care System, as well as Auto Mask AI. During a discovery call we can walk you through relevant projects in more detail, within the limits of our client NDAs.
We start with the business problem instead of the model, then prove value with a working prototype before you commit to a full build. You get one accountable team for data, AI, software and design, plus transparent sprint tracking and full ownership of what we build.
We work with both. Startups usually come to us for an MVP or a first AI feature. Larger companies bring us in to modernize legacy systems, integrate AI into existing platforms or scale models across teams.
What we build, which technologies we use and how AI fits into your product.
Our core services include generative AI development, AI agent development, AI chatbot development, AI and machine learning development and AI consulting. We also build the custom software, web platforms and mobile apps that bring those AI features to your users.
Yes. We connect AI models to your current ERP, CRM, databases and apps through secure APIs, so you do not need to replace systems that already work. Our AI integration and modernization services explain this approach in detail.
A chatbot answers questions inside a conversation. An AI agent goes further, since it can plan steps, call tools and complete tasks across your systems. It might update a CRM record or process a refund, with human approval where it matters.
We work with leading large language models from OpenAI, Anthropic, Google and Meta, plus open-source models we can host privately. For machine learning and computer vision we use PyTorch, TensorFlow and OpenCV, deployed on AWS, Microsoft Azure or Google Cloud.
Yes. Our team handles custom software development, web development, mobile app development and UI UX design. Many clients prefer one partner for the AI layer as well as the product their users actually see.
Yes. When off-the-shelf models are not accurate enough, we fine-tune or train models on your own data, with careful labeling, evaluation and bias testing. We only recommend custom training when it clearly improves accuracy, cost or privacy.
We ground answers in your approved content using retrieval-augmented generation, limit what the model is allowed to do, then test it against real questions before launch. In production we monitor accuracy, log edge cases and route uncertain answers to a person.
How projects start, how we communicate and what you can expect from our team.
Start by contacting our team with a short description of your goal. We reply within 24 hours, then schedule a free discovery call to understand your use case, data and timeline before we suggest next steps.
The first call focuses on your business problem, current systems and available data. By the end, you should know whether AI is the right fit, what a realistic first phase looks like and which questions discovery needs to answer.
Yes. We are happy to sign your NDA or provide ours before you share sensitive information about your product, data or customers.
You do. Once the project is paid for, you own the source code, trained models, documentation and design files, with no vendor lock-in or hidden license fees.
Yes. We begin with a code and model audit, then share a clear report on quality, security and risk. From there we can rescue, remodel or continue the project, depending on what makes the most business sense.
You get a dedicated project manager, weekly stand-ups and weekly progress updates. Sprint boards in Jira stay visible to your team, so you can follow tasks, priorities and blockers at any time.
Yes. Many clients start with AI consulting or a short discovery phase to define use cases, check data readiness and build a business case. That work gives you a clear roadmap, whether you build with us or not.
How we plan, build, test and launch AI solutions, and how long each stage takes.
Most projects follow four phases: discovery, prototype development, data pipelines and deployment. Discovery checks AI readiness, the prototype proves value, pipelines make the system reliable and deployment moves it into production with MLOps monitoring.
Pre-project consulting takes one day to one week, while a discovery phase usually runs two to three weeks. An MVP is often ready in four to eight weeks, and larger enterprise systems are delivered in stages over several months.
Not always. Many projects start with pretrained models, which need far less data than training from scratch. During discovery we review the data you have, then suggest ways to fill gaps, such as labeling, augmentation or synthetic data.
Yes. We work in agile sprints, so priorities can shift as you learn from real users. We agree on any change to scope, timeline or budget before the next sprint begins, which keeps surprises to a minimum.
We test model accuracy on real examples, check edge cases and bias, then run functional, performance and security tests on the full application. A user acceptance phase follows before anything goes live.
Yes. You get a demo at the end of each sprint, access to our sprint board and weekly written updates. Each update covers what shipped, what comes next and any decisions we need from you.
We need a product owner who can make decisions, access to relevant data and systems, plus time from subject matter experts during discovery and testing. Clear input early on keeps the project fast and focused.
How we estimate cost, which engagement models we offer and what return to expect.
Cost depends on the use case, data readiness, integrations, compliance needs and team size. A focused proof of concept costs far less than an enterprise platform, so we share a detailed estimate after discovery rather than a generic price list.
We offer fixed-price projects for well-defined scopes, time and material billing for evolving products and dedicated teams on a monthly plan. Every option starts with a written proposal that shows milestones and costs.
We focus on projects with a realistic budget for the outcome you want. If your budget is tight, we usually suggest a smaller first phase, such as a prototype or consulting engagement, that proves value before a larger investment.
Running costs include cloud hosting, model or API usage and maintenance. We estimate these during discovery, then design the system to keep them predictable, for example by caching results or choosing smaller models where accuracy allows.
Many clients see returns within 6 to 12 months, depending on the use case. We define measurable goals at the start, such as hours saved or conversion lift, then track them after launch so the return is easy to see.
Yes. You can add AI engineers, data scientists, developers and QA specialists who work as an extension of your team. They can join full time or for a set number of hours each month.
Yes. Your first consultation is free, with no commitment. We use it to understand your goals, then tell you honestly whether AI is the right investment for your business right now.
How we protect your data, meet compliance rules and support your solution after launch.
We use encryption in transit and at rest, role-based access, audit logs and data masking where needed. Sensitive workloads can run in your private cloud or on premises, so confidential data never leaves your environment.
No. Your data is used only for your project. When we use third-party AI providers, we choose enterprise options that do not train on customer data, then confirm those terms with you in writing.
Yes. We design solutions to support GDPR, HIPAA and other relevant regulations from the first sprint. For AI risk, our approach follows the NIST AI Risk Management Framework.
Yes. We can deploy open-source models on your own servers, in a private cloud or on edge devices. This option suits regulated industries as well as teams that want full control over data and costs.
We stay on to monitor performance, fix issues and handle updates. Support plans can include model retraining, security patches, new features and regular performance reviews.
Yes. We track accuracy, response quality, latency and cost in production. When real-world data changes and performance starts to drift, we retrain or adjust the model before it affects your users.
Existing clients can reach their project manager directly. For anything else, email hello@denebrixai.com or use our contact form, and our team will respond within 24 hours.
Tell us about your idea or the system you want to improve. Our AI engineers will answer your questions and suggest a practical first step, with no commitment.