AI development
AI development company: custom AI software built on the simplest approach that works
We build AI features and products for businesses that need them to work inside real systems. We also tell you when a simpler tool or a smaller build is the better answer.
In short
AI development is building software that uses machine learning models, mostly large language models today, to read, write, search and decide inside your business. Liftup designs, builds, tests and hands over AI chatbots, agents, document tools, voice agents and integrations, choosing the lowest-complexity approach that meets your accuracy and privacy needs.
The build ladder: start on the lowest rung
Each step up the ladder costs more to build, test and run. We begin at the bottom and climb only when the test results demand it.
| Rung | What it is | Choose it when |
|---|---|---|
| 1. Prompted model | A model with clear instructions and examples | The task is simple and needs no private data |
| 2. Retrieval (RAG) | The model answers from your documents, with sources | Facts live in your content and change often |
| 3. Tools and workflows | The model calls your systems in fixed steps | The process is known and repeats |
| 4. Agent | The model chooses steps and tools itself | The path varies from case to case |
| 5. Fine-tuning or custom model | Training on your examples | Tone or format cannot be reached otherwise, and you have lots of examples |
Most client needs land on rungs 2 and 3. Read the argument for the middle in RAG vs fine-tuning and custom AI vs off-the-shelf.
What we build
- Customer-facing assistants: AI chatbots and RAG chatbots.
- Internal tools: AI agents, custom GPTs and LLM apps.
- Back-office: AI document processing and AI automation.
- Phone: AI voice agents.
- Plumbing: AI integration into the software you already run.
We work with models from OpenAI, Anthropic and Google, and with open models when data cannot leave your environment. The model sits behind a thin layer so it can be swapped later.
How an engagement runs
- DiscoveryOne to two calls on the task, the data, the users and what failure would cost. If the use case is unclear, we suggest AI consulting first.
- Test setWe gather real examples with the right answers. This becomes the yardstick for every later decision.
- PrototypeA working version on the lowest rung that might pass. Scored against the test set.
- PilotA small group of real users, with logging and a person checking output.
- ProductionAccess control, monitoring, spending limits, fallbacks and error handling.
- HandoverCode, prompts, test set, documentation and a runbook. Optional monitoring and updates afterwards.
Want AI development done for you?A short call, a written plan and a fixed quote.
Get a proposalPrivacy, security and regulation
Every build gets a short data map: what personal data goes where, which provider sees it, and how long it is kept. For EU and UK customers we work with GDPR and UK GDPR in mind, with processing agreements where a provider handles personal data. For California we consider CCPA/CPRA, and for Australia the Privacy Act. Your legal adviser makes the final call.
If your system falls under the EU AI Act, risk class and transparency duties are listed in the plan. Our short explainer is at what is the EU AI Act. For agents, we apply the AI agent security checklist.
What you get, how long it takes, what the market says
- Working software in your environment, with source code you own.
- The test set and a script to re-run it.
- Documentation and a runbook for your team.
- Cost per use, listed by model, hosting and tools.
Small, focused builds can reach a pilot within a few weeks. Multi-system projects take longer. The quote gives dates for your scope.
Market data from Clutch puts typical AI development projects between $10,000 and $49,999, with an average of $120,595. Project size varies widely with scope and data. We give a fixed quote after a short call on the contact page; see what AI projects cost for the factors that move it.
How we report and related work
During build you get a short weekly note: test-set score, what changed, what is blocked. After launch, a monthly note covers usage, errors and cost, taken from the system's own logs.
We have no published AI case study yet. Our real, shipped work in adjacent areas includes Aura Realty, a custom CRM built for a property advisory, and We Make In India, a mobile app built with React Native and Node.js. Both show how we build and hand over working systems.
Sources: Clutch AI development pricing guide (market data).
Updated 30 October 2026
Questions, answered
Do you train your own models?
Can our data stay inside our own cloud?
Which provider do you recommend, OpenAI, Claude or Gemini?
What if the pilot does not reach the accuracy we need?
Do you support the system after launch?
Keep exploring
AI consulting services: decide what to build, what to buy and what to skip
Many AI projects fail because the wrong problem was picked, not because the technology was weak. Our consulting work is a short, written decision…
ComparisonRAG vs fine-tuning: the real difference
Both make a language model more useful for your business. They fix different problems, and choosing the wrong one is an expensive way to find that…
ComparisonCustom AI vs off the shelf: the real difference
It is not a two-way choice. There are four levels between a ready-made tool and a custom system, and most companies belong on the middle two.
Describe what you want the AI to do
Send a short brief on the contact page and we will reply with the approach we would try first.