AI development
AI development — ML, LLMs, and agents that run in production.
Appsarmy is an AI development company. We build machine learning models, generative AI, and LLM-backed agents — engineered to run inside your network, on your hardware, against your data. AI is our second flagship craft alongside mobile app development, and part of our full software and AI services.
AI development services, from model to production.
Most AI work dies between the notebook and the network. Our AI development services cover the whole distance — the model, the serving layer, and the integration into the systems your business already runs on. Computer vision is deep enough to get its own page; everything else in our AI and machine learning practice is below.
Machine learning development
Custom models trained on your data — forecasting, classification, ranking, anomaly detection. We start from the decision the model has to support, not from the algorithm, and we ship the evaluation harness alongside the weights.
Models that hold up on real dataLLM & AI agent development
LLM development and AI agent development: retrieval over your own documents, tool-using agents with explicit boundaries, and copilots that sit inside your workflow. Built with retries, guardrails, and human approval where the action is irreversible. When that agent lives in a chat app, we ship it as a Telegram bot.
Agents that act, not just answerGenerative AI
RAG over your knowledge base, structured extraction from messy documents, and generation with an eval set behind it.
Custom AI solutions
AI integrated into the software you already run — ERP, core banking, CRM — rather than bolted on beside it.
MLOps & inference
Serving, versioning, monitoring, and retraining — the unglamorous half that decides whether the model survives.
Your data never leaves your network.
The AI systems companies actually ask us for.
LLM copilots & assistants
An assistant that answers from your documents and systems, with citations — not a chatbot that confidently invents policy.
RAG & document intelligence
Retrieval-augmented generation over contracts, policies, and tickets — chunking, embeddings, reranking, and an eval set that proves it retrieves the right passage.
Forecasting & predictive models
Demand, churn, credit risk, capacity. Trained on your history, backtested honestly, and shipped with the confidence intervals intact.
Anomaly & fraud detection
Real-time scoring on transaction and event streams — the backbone of our fintech and banking work, where a false negative costs money.
AI agents with tools
Agents that call your APIs, take actions, and escalate to a human when confidence drops — with every tool call logged and reversible.
Edge & on-prem inference
Models quantized and served on hardware you already own, inside your network. Applied hardest in our computer vision work on factory floors.
A path from "could AI do this?" to a system on call.
Feasibility
What decision improves, what data exists, and what "good enough" means — in writing, before any model.
Data
Auditing, labelling, and a held-out evaluation set built before training, so the score can't be gamed.
Baseline
The dumbest thing that could work, first. If a rule beats the model, you ship the rule and keep the budget.
Model
Fine-tuning, retrieval, or a custom architecture — whichever the evaluation actually rewards.
Deploy
Served behind an API on your infrastructure, on-prem or in your cloud, wired into the product.
Monitor
Drift, latency, and failure review in production — then retraining on the cases it got wrong.
The AI stack we build on.
An AI team that has put models into production.
Production bias
We are judged on what runs, not what demos. Latency, failure modes, and the 3am pager are design inputs from day one.
On-prem by default
Where data is sensitive, the model comes to the data. We deploy open-weight models inside your network rather than shipping your records to a third party. We weigh the cost side in on-prem vs cloud AI inference, one of the deep-dives on our engineering blog.
Software engineers first
An AI system is mostly software. Ours is built by people who can also ship the API, the pipeline, and the app around the model.
Direct access
You talk to the engineers training and serving the model, in your time zone — English-first, with IP protection and NDAs.
Work with us the way that fits.
AI feasibility sprint
A short, fixed engagement that answers whether the idea is buildable on your data — before you fund a build.
Dedicated AI pod
An ML and platform pod that works as an extension of your team, sprint after sprint.
Fixed-scope build
A defined system, a written scope, a clear price — with success metrics agreed up front.
AI development questions.
What AI development services do you offer?
Machine learning development, generative AI, LLM and AI agent development, and custom AI solutions integrated into your existing systems — plus the inference infrastructure underneath.
Can models run on-prem instead of a cloud API?
Yes, and it's our default where data is sensitive. We deploy open-weight models on your own hardware, so data never leaves your network.
What is AI agent development?
An LLM given tools, memory, and a control loop, so it can act rather than only answer. We build agents with explicit tool boundaries, retries, and human approval on irreversible actions.
Do you build generative AI and RAG?
Yes — retrieval over your own documents, copilots, and structured extraction, each shipped with an evaluation set so quality is measured rather than assumed.
How do you know the AI actually works?
Success metrics are agreed before the model is built. Every system ships with an eval set and monitoring, and we retrain on the cases it gets wrong.
Do you do computer vision too?
Yes — it's a core strength, covered on our computer vision page. This page covers the broader AI and machine learning practice.
Let's build your AI system.
business days