AI software development for products and tools
Put AI inside your product so it helps customers without surprising them.
AI software development builds language, search, summarizing and recommendation features into the products and internal tools you run. We choose and test the model, manage prompts like code, plan for failures and track cost per use, so the feature behaves well when real people use it.
One in-house team since 2015 · Free audit, no obligation
AI software problems that appear after the demo, and our fixes
An AI feature that impresses in a demo can disappoint in daily use. These are the worries behind that gap, how we build against each and what improves.
- 01
“Every prompt tweak fixes one thing and quietly breaks another.”
WHAT WE DOPrompts live in version control like code. We build an evaluation set of real examples with expected outcomes and run it on every change, so a regression shows up before release instead of in front of customers.WHAT CHANGESChanges to the AI are tested like any other code change, and rolled back if they make results worse. - 02
“What do we do when the AI service is down or gives nonsense?”
WHAT WE DOWe design the fallback first. If the model times out, errors or returns something that fails validation, the feature retries, switches to a backup model or falls back to a plain rule-based path, and tells the user honestly.WHAT CHANGESAn outage or odd answer becomes a graceful fallback, not a broken screen. - 03
“I worry the usage bill will grow with every customer who clicks it.”
WHAT WE DOWe measure the cost of each AI call, set budgets per user and per feature, cache repeat answers and use smaller models for simple steps. Your dashboard shows spend alongside usage, so pricing decisions rest on facts.WHAT CHANGESYou know what each AI feature costs per use, so you can price and plan with confidence. - 04
“Our customers' data goes to an outside model, and I'm uneasy about it.”
WHAT WE DOWe map which data each feature sends, strip or mask what the model does not need, choose providers and settings that fit your data rules, and keep prompts and outputs out of logs unless you need them.WHAT CHANGESYou can explain exactly what leaves your system, why, and where it goes.
AI earns its place as a feature, built like software
Adding AI to a product is easy to demonstrate and hard to do well. The model is the small part. Around it sit the pieces that decide whether people can rely on the feature: how prompts are changed and tested, what happens when the model is slow or wrong, what each request costs and where customer data travels.
We treat AI as one component in an ordinary software project. It gets a specification, a test set built from your real examples, monitoring and a fallback path. The rest of the product stays traditional software, where rules must be exact. That split keeps the AI feature useful and the product dependable.
- A prototype impressed people but cannot go live
- You want AI features without handing over customer data
- Nobody can say what the AI feature costs per use
What AI software development adds to your product
What changes for your business once it’s done properly.
- 01
The right model for each job
We compare models on your own examples for quality, speed and price, and can change providers later without rebuilding the product.
- 02
Quality you can measure
A test set built from your real cases turns the question of whether it works into a score you can compare from one release to the next.
- 03
Graceful behaviour under failure
Timeouts, errors and doubtful answers are planned for, so a model problem shows up as a sensible fallback rather than a broken product.
- 04
Costs and data under control
Per-feature budgets, usage reporting and clear data rules keep spending visible and private information where it belongs.
Inside our AI software development service
- 01AI feature scoping and design
- 02Model selection and testing
- 03Prompts managed as code
- 04Evaluation sets and quality checks
- 05Fallbacks when the AI fails
- 06Usage cost tracking
- 07Data privacy and retention rules
- 08Release, monitoring and updates
Tools we build with.
- OpenAI API
- Claude
- Python
- Next.js
- pgvector
- AWS
- 01Fixed-scope projectA defined scope, timeline and price. Best for MVPs, websites and well-defined builds.
- 02Dedicated teamEngineers, designers and QA working as part of your team on a monthly basis.
- 03Support retainerOngoing maintenance, upgrades and improvements with agreed response times.
How our AI software process works
- 01
Discover
Workshops to understand your goals, users, process and constraints, ending in a clear scope and estimate.
- 02
Design
User flows, wireframes and clickable prototypes tested before development starts.
- 03
Build & test
Two-week sprints with a working demo each cycle, code reviews and automated tests.
- 04
Launch & support
Staged release, monitoring, training and ongoing support as you grow.
What clients say
“Our Google Ads campaigns were burning through budget with little to show for it. All In One Marketing Pro stepped in and completely revamped our strategy, targeting the right audience and optimizing our ads for conversions and our website also google map. Within three months, our cost per lead dropped by 65%, and our ROI improved significantly. They solved a problem we thought was unsolvable! Thank you so much Lovi and all in one marketing team”
Alissa Mandzuk-PowerClient review “We had no real strategy for social media before, but after working with All In One Marketing Pro, our engagement skyrocketed! Our Instagram and Facebook pages grew by over 150%, and we’re now getting consistent leads directly from social media. Their content is top-notch, and they truly understand how to turn followers into customers.”
N KaurClient review “All in One Marketing has played a crucial role in the growth of my cardiology practice. Their targeted strategies have greatly enhanced patient engagement through social media, and by the second month of running Google Ads, we began seeing a steady influx of new patients. They are highly professional and results-driven! Thank you”
Emma NClient review
Reviews are in each client’s own words. Results differ from business to business, with the market, the budget and the starting point.
In AI software development the AI is a feature inside a product, such as search, summaries, classification or drafting, and the product still decides what happens next. An agent takes a goal and acts across systems on its own initiative. Features are simpler to control and cheaper to run, so many projects need a feature, not an agent.
We build an evaluation set from real examples, each with a clear description of a good answer, then run every prompt or model change against it and compare scores. Some checks are automatic, such as format and required facts, and some are reviewed by a person. Testing this way makes quality something you can track rather than a feeling.
We choose by testing candidate models on your examples for quality, speed, price and data terms, since the most famous model is not always the right fit. We build the product so the model sits behind a thin layer of your own code, which makes changing provider later a contained job rather than a rewrite.
Keep traditional software wherever the answer must be exact, such as calculations, permissions and records, and add AI where language, search or judgement is involved. Both can live in one product. We start by listing the features and sorting each into rule-based or AI, and we say plainly when a feature does not need AI at all.