Zachary D. Perl
AI software development
That ships wired to your business, not thrown over a wall
Most AI software development services hand you a demo, an invoice, and a goodbye.
dotfun builds custom AI software the other way: our senior US team ships the LLM app, retrieval system, or ML feature — owned by people who answer the phone.
The problem is not the model.
It is everyone who disappears after the build.
AI software is easy to prototype and hard to land. Landing it accurate, connected, and owned inside a real company is the actual job. That is the part most shops skip.
Where custom AI software goes wrong:
It gets thrown over the wall.
An offshore team builds to a spec, ships a repository, and moves on. Then the model drifts.
Nobody who understands the code is still on the line.
It never touches the business.
The build sits in a corner, cut off from your funnel, your CRM, and your analytics.
Technically finished, commercially useless.
Accuracy is assumed, not measured.
The demo answered three questions well, so it shipped. There is no evaluation set.
There is no plan for the moment the model is confidently wrong.
Ownership is fuzzy.
Who owns the code, the data, and the model once the invoice clears?
On too many builds, nobody wrote that down.
A model in a notebook is not software.
Software runs every day, stays accurate, and belongs to you. That gap is what we build across.
What we build when you hire dotfun for AI software development
dotfun delivers custom AI software development as a done-for-you build, not a staffing contract. You get a senior team that scopes the problem, picks the right approach, builds it, and stays. This is the software lane of our AI Solutions practice, alongside AI agent development and workflow automation.
The custom AI software we build.
LLM applications and generative AI features
We build applications on large language models: assistants, drafting and summarization, classification and extraction, and generative features inside your product (IBM has a clear primer on what generative AI is). When the generative model is the product — copilots, RAG assistants, document generation — that work has its own practice. The point is a feature your users trust. Not a party trick.
RAG and knowledge systems
Most companies sit on knowledge locked in PDFs, tickets, wikis, and spreadsheets. We build retrieval-augmented systems that answer from your actual documents instead of guessing (AWS explains the RAG pattern here). Grounded answers, fewer hallucinations, a knowledge layer your team can extend.
Machine learning features and models
Sometimes the problem needs prediction, not language. We build it: lead scoring, forecasting, recommendation, and the pipelines behind them. Right-sized to your data, not over-engineered to look impressive.
AI added to your existing software or product
You do not always need a new system. Often you need AI inside the product you already run. We build the feature into your existing software, on your codebase, with your team. It ships as part of your product. Not a bolt-on nobody trusts.
Integrations that make it usable
Software is only useful when it lives inside your stack. We connect the build to your CRM, your data, and your tools, so the AI works where your team already works. Deeper AI integration services are a separate practice, and we scope connective-heavy projects that way.
How a custom AI software build runs, start to finish
No mystery, no black box, no disappearing act. Here is the path from problem to owned software.
Scope the real problem
We start with the job, not the technology.
What task should this software improve? What does good look like? Is custom even the right call? If not, we will tell you.
Choose the model and the approach
Fit to your data, not the trend
We select the model, the pattern, and the architecture that fit your data and your accuracy bar. LLM app, RAG, classic ML, or a mix. Driven by the problem, not by what is trending.
Build it on your stack
Tight loops, no six-week reveal
We build against your real data, tools, and edge cases, in tight loops you can see. You are not waiting six weeks for a reveal. You watch it take shape.
Evaluate accuracy before it ships
Accuracy is a number, not a feeling
We build an evaluation set and measure whether the software is actually right. Then we tune until it clears the bar you set. Accuracy is a number we check, not a feeling we hope for.
Ship it wired, then stay
Launch is the start, not the finish
The software ships connected to the funnel and the interface it needs. We stay for support and iteration. The build does not end at launch. That is where ownership begins.
Why senior-led, US, done-for-you beats the alternatives
Most teams weigh three other ways to get custom AI software built. Here is where each one falls short.
Compared to an offshore AI software development company:
Offshore dev shops sell staff-augmentation. You rent capacity, manage it across time zones, and own the risk when the spec meets reality. dotfun is a senior team, not a body shop. You trade a lower hourly rate for software that lands, stays accurate, and has an owner who picks up the phone.
Compared to a DIY AI platform:
Platforms hand you the tools and wish you luck. That works if you have in-house ML talent and time. Most growth-stage teams do not. dotfun is the done-for-you alternative: we build the thing, wire it in, and prove it works.
Compared to a generalist software agency:
A general dev shop can write code. But AI software lives or dies on model selection, retrieval, and evaluation. We build AI software as the job, not a line item on a website project.
DON’T WORRY
What you do not have to worry about with a dotfun build
You own the software, the code, and the data.
What we build for you belongs to you: the code, the models, and the data that runs through it. Ownership is written down at the start, not a surprise at the end.
Your data is handled with care, and we describe exactly how.
We are direct about how your data is used, whether it grounds or trains a system, and how it is protected. We set those terms with you before we build. We do not quietly train on your data.
Accuracy is measured, not promised.
We do not ask you to take model quality on faith. We build an evaluation set, measure how the software performs on your real cases, and tune to a bar you agree to. When the model has limits, we design for them, not hide them.
Nobody throws it over the wall.
The team that builds your software stays for launch, support, and iteration. If you take it in-house, we hand off cleanly. You are not left holding code only a stranger understood.
Built by dotfun.
Your software is built by Run Good.
That is dotfun's senior build team, the same engineers who run automation for our growth clients every day. They bring 30-plus combined years in B2B tech and software builds to your product and your data.
And because that team sits next to our growth and design work, your software ships wired to the funnel, the CRM, and the analytics, so it shows up in pipeline, not just in a repository. That is the edge our AI software development services carry that a standalone dev shop cannot.
Who we build custom AI software for
dotfun fits teams with a specific AI problem their stack does not cover off-the-shelf, and who would rather hire a senior team than build one.
Founders adding AI to a product
You have a product and a roadmap, and AI belongs in it. We build the feature into your product, at a quality you can put your name on.
Ops and engineering leaders with a bespoke need
Off-the-shelf does not fit the problem. It is specific to your business, your data, or your workflow, and it needs real engineering. We scope it honestly and build it right-sized.
Teams without in-house ML talent
You do not have machine learning engineers on staff. Hiring a team to ship one system does not pencil out. We are the senior AI software development services you bring in for the build.
Frequently asked questions
LLM applications and generative features, RAG and knowledge systems, machine learning features like lead scoring and forecasting, AI added to software you already run, and the integrations that make any of it usable. It is a done-for-you build, not a staffing contract.
Buy when a proven product already solves the problem. Build when it is specific to your business, spans your own tools and data, or needs something off-the-shelf does not reach. We scope the real problem first, and if custom is not the right call, we will tell you.
We build an evaluation set from your real cases and measure whether the output is actually right, then tune until it clears the bar you set. Accuracy is a number we check, not a feeling we hope for. That same set becomes the regression check whenever a model or a data source changes.
A senior US team scopes the problem, picks the approach, builds it, and stays. You are not renting capacity across time zones and owning the risk when the spec meets reality. The people who understand the code are still on the line after launch.
Often that is the better move. You do not always need a new system; you need AI inside the product you already run. We build the feature into your existing codebase, with your team, so it ships as part of your product rather than a bolt-on nobody trusts.
You do, and it gets written down before the build starts rather than after the invoice clears. The software also ships connected to the funnel, the CRM, and the analytics it needs, so it runs every day inside your business instead of sitting in a notebook.
