AI app development
AI app development that works on real data, not just in the demo
We build LLM features and AI apps your users can rely on: assistants grounded in your documents, extraction that fills real fields, and agents that call your tools safely. You work directly with the AI product engineer who builds it.
- Grounded answers with sources
- Evals before launch, not after
- You own the code and prompts
Useful AI is an engineering problem, not a prompt
Almost anyone can get a large language model to produce an impressive answer once. The hard part is getting a good answer the thousandth time, on a messy PDF, from a user who phrased the question badly, at a cost that still makes sense at scale. That gap between a demo and a product is where most AI projects stall, and it is the part we focus on.
Loha.dev is a small software company in Albuquerque, New Mexico, run by an AI product engineer. We do LLM app development the way we build any software: a clear scope, typed code, tests, and weekly demos you can click through. The AI pieces get extra discipline, including retrieval tuned to your content, structured output your code can trust, evaluation sets that catch regressions, and guardrails for the cases where the model should say "I don't know".
Whether you want to build an AI app from scratch or add an assistant to a product you already run, we start from the job the user is trying to get done. Sometimes the right answer is a full agent. Often it is one well-placed model call that removes ten minutes of busywork. We will tell you which.
What you get when the AI is built properly
Answers grounded in your content
Retrieval-augmented generation (RAG) pulls the relevant passages from your docs, tickets or database before the model answers, and the app shows its sources so users can check them.
Output your code can depend on
We use structured output and schema validation so the model returns typed fields, not free text you have to parse and hope. Bad responses are caught and retried, not shown to users.
Quality you can measure
Before launch we build an evaluation set from real examples of your data. Every prompt or model change runs against it, so improvements are proven and regressions are caught early.
Costs that stay predictable
Model routing, caching, prompt trimming and sensible limits keep per-request costs in check. You see usage per feature, so a surprise bill is not how you find out something changed.
Privacy by design
We use provider API settings where customer data is not used for model training, send only the data a task needs, and can redact sensitive fields before anything leaves your system.
No lock-in to one model
A thin provider layer lets us move between OpenAI, Anthropic and open models as prices and quality change, without rewriting your product.
AI features we build
Most projects combine two or three of these. We pick the smallest set that solves the problem and ship it inside a real app: web, mobile, or both.
In-app assistants and copilots
A chat or command interface that knows your product, can look things up for the user, and hands off to a human or a form when it should.
Search and Q&A over your documents
RAG pipelines over manuals, policies, knowledge bases or support history, with chunking, embeddings and ranking tuned to how your content is actually written.
Document and data extraction
Turn invoices, forms, contracts, emails or scanned PDFs into clean, validated records, with confidence flags and a review queue for edge cases.
Agents and tool use
Models that call your APIs to book, update, search or draft, with explicit permissions, step limits and an audit log of every action they take.
Classification, routing and summaries
Triage incoming messages, tag records, summarize long threads or meetings, and route work to the right person automatically.
Evals, guardrails and monitoring
Test sets, automated grading, input and output checks, and logging so you can see what the AI is doing in production and fix it with evidence.
The app around the AI
Accounts, billing, admin screens, streaming UI and the backend, because an AI feature is only useful inside a product people can actually use.
How we build an AI app with you
The process front-loads the questions that decide whether an AI feature will work, so you are not finding out at launch.
Step 1: Use case and data review
Timeline: Week 1
We map the exact task, look at real samples of your data, and agree on what a good answer looks like. If AI is the wrong tool, we say so here.
Step 2: Proof of concept
Timeline: 1–3 weeks
A focused prototype on your actual data, plus a first evaluation set, to show whether the approach is accurate enough and what it will cost per use.
Step 3: Product build
Timeline: 4–10 weeks
We build the feature into a real app with the UI, backend, permissions and integrations it needs. You get a working demo every week.
Step 4: Evaluation and hardening
Timeline: 1–2 weeks
We expand the test set, add guardrails, tune retrieval and prompts, and handle failure modes like empty results, long inputs and off-topic requests.
Step 5: Launch and monitoring
Timeline: 1 week
Release to users with logging, cost tracking and feedback capture, then a short period of watching real usage and fixing what it reveals.
Step 6: Iterate
Timeline: Ongoing
Models improve and prices drop. We re-run your evals against new options and ship upgrades when they are measurably better.
Who this is for
Founders building an AI-first product
You have an idea that depends on LLMs and need someone who can take it from prototype to a dependable app without a large team.
Software companies adding AI features
You have users and a product, and want an assistant, smart search or automation that fits your existing stack and data rules.
Teams drowning in documents
Operations, legal, support or finance teams spending hours reading, copying and summarizing that a reliable pipeline could handle.
Anyone whose ChatGPT pilot stalled
The pilot looked great, but accuracy, privacy or cost made it hard to roll out. That is usually fixable with proper engineering.
Models and tools we use
We choose models per task, not per hype cycle. The rest of the stack is the same proven setup we use for every app we ship.
- OpenAI and Anthropic APIs
- Strong general models for reasoning, writing, tool use and structured output, with business data controls on the API.
- Open models
- An option when you need lower cost at volume, specific hosting, or more control over where data is processed.
- Retrieval and embeddings
- Vector and keyword search combined, with Postgres or a managed index, so answers come from the right passages.
- TypeScript, React and React Native
- One typed codebase for the web app, the mobile app and the AI logic, so schemas are shared end to end.
- Convex and Cloudflare Workers
- Realtime backends and edge functions for streaming responses, background jobs and low-latency APIs.
Find out if your AI idea will hold up
Answer a few short questions about the task and your data. We send back a free app plan with the approach, stack and a cost estimate.
What AI app development typically costs
Cost depends mostly on how much product surrounds the AI and how hard the data is. These are typical ranges.
AI proof of concept
- Typical cost
- $5,000–$12,000
- Timeline
- 2–4 weeks
- What's included
- One use case on your real data, a working prototype, a first evaluation set, and a written read on accuracy and per-use cost.
AI MVP
- Typical cost
- $15,000–$40,000
- Timeline
- 6–10 weeks
- What's included
- A production web or mobile app built around one or two AI features, with auth, backend, evals, guardrails and usage tracking.
Full AI product
- Typical cost
- $40,000–$120,000+
- Timeline
- 3–6 months
- What's included
- iOS, Android and web, multiple AI workflows or agents, integrations, admin tools, and monitoring. Ongoing iteration typically $1,500–$6,000/month.
These are typical ranges and estimates, not quotes. Model usage fees from the AI provider are separate and depend on volume. The free app plan gives you a specific estimate for your project.
Custom AI app vs a generic chatbot or ChatGPT wrapper
A thin wrapper around a chat model is quick to launch and easy to copy. A custom AI app is built around your data, your workflow and your users.
Swipe sideways to see the full table.
| What matters | Custom AI app | Generic chatbot / wrapper |
|---|---|---|
| Knowledge | Grounded in your documents and database, with cited sources | Relies on what the model already knows, so it guesses about your business |
| Actions | Calls your APIs to look up, create and update records with permissions | Can only produce text the user copies somewhere else |
| Reliability | Structured output, validation and an eval set run on every change | Untested prompt changes, quality drifts silently |
| Privacy | Business API settings, minimal data sent, redaction where needed | Often unclear what is sent, stored or used for training |
| Cost control | Model routing, caching and per-feature usage tracking | One model for everything, costs grow with every message |
| Experience | AI built into the screens and flows your users already use | A separate chat box bolted on the side |
| Defensibility | Your data, workflow and evals are hard for competitors to copy | Anyone with the same API key can rebuild it in a weekend |
Products we have built and run
We ship and operate our own software, so we know what it takes to keep a product working after launch, not just to demo it.
Star
A setlist and presentation app for worship teams, musicians and speakers. Build setlists and run slides on the big screen from a tablet, with realtime sync between devices and a web app alongside the mobile apps.
Visit Star (opens in a new tab)SentIt
Email for teams: a shared inbox product where a team works the same conversations together. The kind of message-heavy workflow where summaries, triage and drafting assistance earn their keep.
Visit SentIt (opens in a new tab)
Frequently asked questions
How much does it cost to build an AI app?
A focused AI proof of concept typically costs $5,000–$12,000, an AI MVP $15,000–$40,000, and a full multi-platform AI product $40,000–$120,000 or more. Model usage fees are separate and depend on volume. The biggest cost drivers are how messy your data is and how much app needs to be built around the AI feature.
How long does AI app development take?
A proof of concept on your real data usually takes 2–4 weeks, and an AI MVP ready for users takes about 6–10 weeks. Larger products with several AI workflows, mobile and web apps, and integrations take 3–6 months. We demo progress every week so you can redirect early.
Is my data used to train AI models?
Not when the app is set up correctly. We use provider API settings where customer data is not used for model training, and we send models only the data a task needs. For sensitive fields we can redact or mask values first, or use an open model hosted where you choose.
Should we use OpenAI, Anthropic or an open-source model?
It depends on the task, so we test rather than guess. We run your evaluation set against a few candidate models and compare accuracy, speed and cost. Many apps end up using more than one: a strong model for hard reasoning and a cheaper one for routine steps. The code is built so you can switch later.
What is RAG and do I need it?
RAG, or retrieval-augmented generation, means the app finds the relevant passages in your own content and gives them to the model before it answers. You need it whenever answers must come from your documents, policies or data rather than general knowledge. It also lets the app show sources, which builds user trust.
How do you stop the AI from making things up?
You reduce hallucinations with engineering, not hope. We ground answers in retrieved sources, require structured output, tell the model to say when it does not know, and validate results in code. An evaluation set of real questions measures accuracy before launch, and production logging catches new failure cases so we can fix them.
Can you add AI features to our existing app?
Yes. Many projects add an assistant, smart search, extraction or automation to a product that already exists. We review your codebase and data first, then build the AI feature to fit your stack and permissions. If your app uses React, React Native or TypeScript, we can usually work directly in your repo.
Who owns the code and prompts?
You do. On full payment you own the code, prompts, evaluation sets and all related IP, and the source lives in your own repository from the start. We can set up model provider accounts in your name, so you are not dependent on us to keep the app running.
Build an AI app people actually trust
Tell us what you want the AI to do. In return you get a free app plan with scope, stack and a realistic estimate.
or email matt@loha.devCall (732) 340-8396
Free, no obligation. We reply within one business day.