Custom AI software development
When no off-the-shelf product fits, we build the system: applications, internal platforms and the integrations that connect AI to your ERP and CRM.
- Typical build
- 4-12 weeks
- Code ownership
- Yours
- Scoped from
- A written specification
- Runs on
- Cloud or your servers
What we mean by custom AI software
Custom AI software is an application built for one company's process, with language models as a component rather than as the product. In practice that means three kinds of work: internal platforms that replace a spreadsheet nobody trusts, integrations that connect AI to systems you already run, and customer-facing applications where the AI is a feature rather than the pitch.
Build or buy: the honest version
Most companies asking for a custom build should buy something instead, and the ones who genuinely should build usually know why already. Our test is three questions.
| Question | Points to buying | Points to building |
|---|---|---|
| Is the process generic? | Invoicing, support, CRM: buy | It is how you compete: build |
| What does integration cost? | Product connects out of the box | Integrating the product costs as much as building |
| Can the data leave? | Yes, with a processing agreement | No, contractually or legally |
Two out of three pointing the same way is usually decisive. If you want that answered against your real numbers rather than in the abstract, the AI readiness audit produces a build-versus-buy recommendation per use case.
What we build
Internal platforms
The system that replaces the shared spreadsheet, the mailbox used as a database and the process that lives in one person's head. Approval workflows, internal catalogs, planning and reporting tools, with AI doing the reading and drafting inside them rather than sitting in a separate chat window nobody opens.
Integrations between the systems you already run
Most of the value in an AI project is not the model, it is the plumbing. We connect through REST and SOAP APIs, direct database access, scheduled file exchange, or a scripted interface where a system offers nothing else. Legacy on-premises software is a normal condition of the job, not an exception we charge extra to tolerate.
Customer-facing applications
Web applications and platforms where AI is one feature among many: search that understands a question, forms that fill themselves from an uploaded document, configurators that explain their own output. Built as products, with the performance and accessibility standards that implies.
Data pipelines
AI is only as good as what it reads. Extraction, cleaning, deduplication and the scheduled jobs that keep it current. Unglamorous, and frequently the difference between a system people trust and one they quietly stop using.
How we work
- Written specification first. What it does, what it explicitly does not do, what "finished" means. Scope agreed before anyone opens an editor.
- Thin slice to production early. One narrow path working end to end in week two or three, in a real environment. Demos on a laptop prove nothing.
- Human in the loop by default. Anything touching money, contracts or customer commitments gets an approval step until the statistics justify removing it.
- Handover as a deliverable. Documentation, runbook and training, not a zip file and good luck.
What we will turn down
Projects with no named owner on your side, because they are abandoned within a year. Builds where the requirement is "add AI" with no process attached. And anything where an existing product plus two days of configuration would do the same job, because we would rather lose the project than deliver something you resent paying for.
The stack
Modern web stacks for applications, with model access through cloud APIs, through self-hosted open models, or through a routing layer that decides per request. Deployment on your cloud account or your hardware. We do not resell licences and we take no commission from providers, so the architecture recommendation is driven by your constraints rather than by our margin.
Questions we get asked every time.
When should a company build custom AI software instead of buying a tool?
Build when the process is the thing that makes you money and no product models it, when the integration cost of a tool approaches the build cost anyway, or when the data cannot be handed to a third party. Buy when the process is generic. Most companies should buy more than they think, and we say so.
Can you integrate AI with our existing ERP or CRM?
Yes, and that is most of the work. We connect through whatever the system exposes: REST or SOAP APIs, database access, scheduled file exchange, or a scripted interface where nothing else exists. Older on-premises software with no modern API is normal and rarely the blocker people expect it to be.
Who owns the code you write?
You do. Source in your repository, deployment on your infrastructure or an account in your name, documentation written so another team could pick it up. We would rather keep clients because the work is good than because leaving is painful.
What does a custom AI project cost and how long does it take?
The variables are the number of systems involved, the state of your data and how much of the process needs a human in the loop. A single-process build usually lands in the four-to-twelve week range. We scope from a fixed, written specification rather than an hourly guess, so the number is knowable before you commit.
What happens after the system is live?
Monitoring, an SLA and a review cadence. Language models improve, dependencies change and processes drift, so a system that is never reviewed loses accuracy quietly. If you have an internal team who would rather own it, we hand over the documentation and train them instead.
Keep reading.
Describe the process no tool on the market fits.
Bring the workflow and the reason the obvious products do not fit. Thirty minutes and we will tell you whether it is genuinely a build.
Book a free 30-minute call