AI & Machine Learning Development

How will you be pushing the boundaries next?

AI agent development

Agents that carry a task from instruction to done: reading context, calling your systems and stopping when something looks wrong. Built with guardrails and audit trails, scoped to jobs where autonomy earns its keep.

Custom assistants

Assistants grounded in your documents and your rules. They answer with sources, admit what they do not know and stay inside the permissions you set.

ML Training

Custom models trained on your data for the narrow problems where they beat off-the-shelf options: forecasting, scoring, anomaly detection. Benchmarked first, so you see the numbers before you commit.

AI software development you can put in production

Most AI projects die between the demo and the deployment. ARITS does AI software development with the boring parts included: data pipelines, evaluation, monitoring, cost control. If a simpler model, or no model at all, would serve you better, we say so before you spend.

LLM development and integration

As an LLM development company we build assistants, copilots and automation on top of large language models, with the engineering that makes them dependable: prompt pipelines, structured outputs, evaluation suites, fallbacks for the days the model is wrong. Our AI integration services connect these systems to the software you already run.

RAG development services

Retrieval-augmented generation lets an assistant answer from your own documents and data instead of guessing. Our RAG development services cover the full pipeline: ingestion, chunking, retrieval tuning and citation, so answers come with sources your team can check. It is the fastest route to AI that knows your business.

Custom machine learning models

Forecasting, classification, anomaly detection: machine learning development for problems where an off-the-shelf model stops short. We start from your data, not from an algorithm in search of a use case, and we benchmark against the simple baseline first. If the baseline wins, you hear it from us.

Generative AI consulting

Generative AI consulting for teams deciding where AI actually pays: we map your workflows, identify the use cases with measurable return, and prototype the shortlist before you commit real budget. The deliverable is an honest ranking with costs and risks, sometimes including the advice to wait.

MLOps and data pipelines

Models decay without maintenance. We build the pipelines that keep AI systems healthy in production: versioned data, automated retraining, drift monitoring, cost tracking. The same platform-engineering discipline behind our infrastructure work applies here.

Responsible AI development starts here.

Privacy awareness

Your data stays under your control. We design AI systems so sensitive information is minimised, access is scoped and nothing leaves your environment without an explicit decision. Where regulation applies, GDPR or HIPAA requirements are built into the pipeline itself rather than promised in a policy document.

Regulatory alignment

AI in regulated industries needs evidence: what data trained the model, how decisions are made, who reviewed what. We document model behaviour and data lineage as part of delivery, so when an auditor or regulator asks, the answer is a file you already have.

Bias checks in data

Training data gets examined before a model does, because bias in means bias out. We test model behaviour across the segments that matter for your use case and report what we find plainly. Where a data gap exists, you learn about it before your users do.

Human-in-the-loop models

For consequential decisions, the system recommends and a person decides. We design review workflows, confidence thresholds and escalation paths so automation takes the volume while people keep the judgement. Full autonomy is earned gradually, with evidence, or it is not granted at all.

Our chosen tech stacks (always evolving!)

Common questions about AI development

What does an AI development company actually do?

It turns AI from a demo into a working part of your product: choosing the right technique, building data pipelines, integrating models with your software, and monitoring them in production. At ARITS this includes telling you when AI is the wrong tool for the problem.

What is RAG development?

RAG, retrieval-augmented generation, connects a language model to your own documents and data. The system retrieves relevant material first, then generates an answer grounded in it, with sources. It gives you an assistant that knows your business without training a model from scratch.

Do we need to train our own model?

Usually not. Most business problems are solved by integrating and steering existing models with good retrieval and evaluation, at a fraction of the cost. Custom training earns its keep for narrow, data-rich problems. We benchmark the cheaper route first and show you the numbers.

How do you keep our data private in AI projects?

Data handling is designed first: what the model sees, where it runs, what is logged, what is retained. Options range from fully self-hosted models to scoped API use with retention turned off. You get the trade-offs in writing and pick the posture that fits your compliance needs.

Our Blog - Read On!

Engineering Strategy

Software handover checklist: could you deploy without us?

What a software handover should leave you with: the assets you already hold, three tests the vendor must pass, and the one clause worth reading.

A road forking toward two map pins, one for Bangladesh and one for India

Engineering Strategy

Outsourcing to Bangladesh vs India: Where Each One Wins

We build software in Dhaka, so we sourced every number here, including the two where Bangladesh loses to India. A comparison you can check yourself.

Shield with code brackets on an emerald gradient banner reading AI Coding Agents in Production, Guardrails not vibes

AI & Agentic Engineering

How We Actually Use AI Coding Agents in Production

What changes when a software agency lets AI coding agents write production code: the workflow, the guardrails, and the parts we still keep human.

Talk to the engineers who would do the work

A 45 minute call, no pitch deck. Bring your constraints and we will tell you what is realistic, from a team that has shipped 400+ projects out of Dhaka and London.

Prefer to write first? Send us the details.

Practical AI development for actual use cases