What our AI software development service covers
We build custom AI software end to end. We take an idea for software with AI at its core and turn it into a product your customers and staff can rely on. One team does all of it, so nothing gets lost between handoffs.
You might need a customer-facing app, an internal platform, or AI features inside a product you already sell. What you usually don't have is the team to build it. We become that team, and you own everything we build: the code, the documentation and the IP.
This is custom software, not a packaged tool. We don't sell a subscription product and then bend your process to fit it. If an off-the-shelf AI tool already solves your problem well, we'll tell you, because buying is usually cheaper than building. Custom makes sense when your workflow, your data or your product is what sets you apart.
We work with growing US small and mid-sized businesses: owners and managers who have a clear business problem, some data to work with, and no in-house AI team. Each project covers discovery, architecture, model selection, build, deployment and support.
Why this matters
Your customers expect AI in the software they pay for, and the companies that get there first set the standard. A product built properly from day one saves you the painful rebuild that comes when a quick prototype meets real users, real data and real growth.
How we approach AI-driven software development
Our approach is simple: prove the risky parts early, then build in small steps you can see. Here's how a project runs.
- Discovery and scoping. We learn about your business, your users and your goals. We write down what the software must do and what it won't do, and you sign off on that scope.
- Data and architecture review. We look at the data you have, where it lives and how clean it is. Then we design how the app, the data and the AI models connect, and flag gaps before they get expensive.
- Prototype or MVP. We build the smallest version that answers the big question: does the AI do the job well enough on your real data? Sometimes that's a rough prototype. Sometimes it's a minimum viable product real users can try.
- Iterative build with you. We build in short cycles and show you working software in regular demos. You give feedback and we adjust. You never wait until the end to see what you're getting.
- Testing. We test the software the usual way, and we test the AI on its own: how accurate it is, how it handles odd inputs, and what it does when it doesn't know the answer.
- Deployment. We launch on secure cloud hosting with logging, monitoring and cost controls in place from day one.
- Post-launch support. We stay on to fix issues, watch performance and costs, and add the features your users ask for.
A note on AI-assisted software development. AI coding tools help engineers write and check routine code faster, and that's useful. They don't make the decisions that decide whether a project works: what to build, how the pieces fit, how to test the AI, and how the software fits your business. Those stay with experienced people.
What we build
Most of our work falls into a few types of software, and many projects mix more than one.
- AI-powered SaaS products: software you sell to your own customers, with AI at the core of what it does.
- Internal tools: platforms your staff use every day, such as document processing, research or reporting tools with AI built in.
- AI agents inside your software: agents that research, decide and act within your product or your tools, with a person approving the decisions that matter. See our custom AI agent development service.
- Automation layers: AI workflows that connect the systems you already run and remove manual steps. See AI automation services.
- Copilots and assistants: helpers that sit beside your staff or your customers and answer questions from your own knowledge. See custom AI assistants and AI chatbot development.
Not sure which of these you need yet? Our AI consulting services start with a written roadmap, so you can decide where AI fits before you commit to a build.
Tech stack and AI models we work with
We build on proven, widely used technology that you can hire for after us, so you're never locked in.
- Product and backend: Next.js and React for the product, Python or Node for the backend.
- LLM APIs: hosted models from providers such as OpenAI and Anthropic (Claude), including Azure OpenAI for businesses that run on Microsoft's cloud.
- Self-hosted and open-source models: for cases where data can't leave your environment, or where running your own model makes more sense at your volume.
- RAG pipelines: retrieval-augmented generation, so the AI answers from your own documents and data and can cite its sources. Frameworks such as LangChain and LangGraph handle multi-step logic.
- Fine-tuning: when prompting and retrieval aren't enough, we adapt a model to your task. Often it isn't needed, and we'll say so.
- Vector databases: to store and search your documents by meaning, such as Milvus.
- Cloud platforms: we deploy on major cloud platforms, most often Microsoft Azure.
How we compare to freelancers and large agencies
You have real options when you hire an AI development company. Freelancers can be a good fit for a small, well-defined job. Large agencies bring scale and long client lists. Both can be the right call. Here's where we fit.
- Focused on US businesses. We work with US small and mid-sized businesses, and we write scopes, demos and documentation in plain language that owners and managers can follow.
- Across business functions. We don't stick to one industry. Our work covers finance, hiring, research and customer-facing products, so ideas that work in one function carry over to another.
- Direct access to the build team. You talk directly with the people designing and building your software.
- A transparent process. You sign off on a written scope, see working software in regular demos, and get a fixed price after scoping.
If you need a very large team, a big agency is probably the better fit. If you want a small, focused team that treats your project as its main job, talk to us.
Projects we've built
A few projects that show how we work. Each case study covers the problem, what we built and the results. We also built a platform where learners train through conversations with AI avatars, described in the immersive AI learning platform case study.
- Invoice automation for a mid-sized manufacturer. Finance staff processed invoices by hand and checked them against ERP data. We built a platform that extracts and validates invoice data with Azure Document Intelligence and Azure OpenAI, plus a chat interface for asking about invoices. Read the AI invoice automation case study.
- Hiring software for an HR tech company. It pairs AI resume parsing and ranking with automated voice interviews, and gives human interviewers a copilot that suggests questions and scores answers. See the recruitment platform we built.
- A research assistant for an enterprise pharma consulting firm. Consultants ask questions in plain language and get cited answers drawn from curated datasets and documents, using agentic RAG. Read the pharma research assistant case study.
How pricing works
Every project is priced for its scope, so we don't publish a price list. Most builds move through three phases, and you decide at each step whether to go on.
- Discovery. We learn about your business and the problem, review your data and systems, and write a scope. At the end you know what we'll build, how it fits together and what it will cost.
- MVP. We build the first working version around the most important use case, test it on real data and put it in front of real users.
- Scale. We add features, users and integrations, prepare the software for more load, and keep improving the AI based on how people use it.
What drives cost: the number of features and screens, how many systems we connect to, the state of your data, which models you use, and how much testing the AI needs. Pricing and timelines are set during scoping, and you get a fixed price before we build. Read more about how we price custom AI software.
What you get
- Product scoping and a solution design you sign off
- Web app or platform with AI built into the core experience
- Integration with payments, user accounts and your existing systems
- Secure cloud hosting with monitoring and cost control
- Full source code, documentation and IP ownership
- Support, maintenance and new features after launch
Related services
Related case studies
AI-Powered Invoice Automation
Manufacturing · Avg. invoice processing time: 4 hrs → 12 sec
Read the case studyIntelligent Recruitment Platform
Human Resources · Time-to-first-interview: 5 days → same day
Read the case studyAgentic RAG Pharma Research Assistant
Pharma · Research query time: 2 hrs → 8 sec
Read the case study