Which AI use cases do you build?

Generative AI assistants, predictive analytics, recommendation engines, computer vision, document processing, chatbots and voice agents, AI agents and workflow automation, and forecasting and optimisation.

Can you add AI to our existing software?

Yes. We integrate AI through APIs and microservices into your current apps, ERP, CRM and data platforms, so you gain capabilities without rebuilding.

Do you build generative AI and LLM applications?

Yes. We build RAG assistants, copilots, content engines and agents grounded in your own data, with guardrails, evaluation and cost controls.

Can you work with computer vision and edge devices?

Yes. We build detection and inspection systems and optimise models to run on cameras, mobile devices and embedded hardware where latency or privacy demands it.

Can AI be useful for a small business?

Yes. Targeted projects such as a support assistant, document automation or demand forecast can pay back quickly. We recommend starting with one high-value use case.

Which technologies and models do you use?

We are tool-agnostic: PyTorch, TensorFlow, Hugging Face, leading commercial and open LLMs, and cloud platforms such as AWS, Azure and Google Cloud, chosen per project.

Should we use an off-the-shelf model or a custom one?

We start with the simplest option that meets your targets: prompting or retrieval over a pre-trained model, then fine-tuning, and a fully custom model only when the value justifies it.

How do you measure model quality?

With evaluation sets built from your real cases, tracking accuracy, precision and recall where relevant, latency, cost per prediction and business KPIs, before and after launch.

What is MLOps and why does it matter?

MLOps is the practice of deploying, monitoring and retraining models reliably. It keeps accuracy from decaying as data changes and makes releases repeatable and safe.

Can you deploy on our cloud or on-premises?

Yes. We deploy to AWS, Azure, Google Cloud, private cloud or on-premises, and can run models fully inside your environment when data residency requires it.

How do you keep our data secure?

Through encryption, least-privilege access, secure environments, audit logs and, where needed, private deployments. Sensitive data can be redacted before it reaches any model.

Do you train public models on our data?

No. By default your data is not used to train public models, and we choose providers and settings accordingly. Any fine-tuning happens in your controlled environment with your approval.

How do you address bias and responsible AI?

We test for bias on relevant groups, document model behaviour and limits, add human review for high-impact decisions and set up monitoring to catch drift.

Can you work in regulated industries?

Yes. We design controls to align with frameworks such as GDPR, HIPAA and SOC 2 and the EU AI Act where relevant. Formal compliance remains with your organisation and its assessors.

Who owns the code, models and data?

You do. Source code, trained models, prompts and datasets are yours, and we document them for your team.

How long does an AI project take?

A proof of concept typically takes 4 to 8 weeks. A production release usually takes 3 to 6 months, depending on scope, data readiness and integrations.

How much do AI development services cost?

Cost depends on use case, data, complexity, integrations and run costs. After discovery we provide a fixed-scope estimate for the pilot and a phased budget for production.

What engagement models do you offer?

Fixed-scope pilots, dedicated AI teams, time and materials, co-development with your engineers, and support retainers for MLOps and improvement.

How do we control ongoing AI costs?

By choosing right-sized models, caching, batching, usage monitoring and budgets, so cost per prediction stays visible and predictable as you scale.

What happens after launch?

We monitor performance, handle retraining and upgrades, fix issues and add features, or hand over to your team with documentation and training.

AI Development Services

AI development that ships to production and pays back

Aarvexa is your end-to-end AI engineering partner: strategy, data, models, applications and MLOps, delivered by one team and measured on business results, not demos.

Custom AI & GenAI
Production MLOps
Responsible by design

Talk to our experts See how it works

Aarvexa AI StudioProject: demand forecaster

Dataset1.2M records · cleaned

LIVEAI STUDIO01 / 07

DataTrainValidateDeploy

Delivery progress

Pipeline

Data ingested and cleaned

Models shipped

120

Prod-ready8 wks

Accuracy

Beats the target

…ms latency

Custom AI DevelopmentGenerative AI & LLMsMachine LearningComputer VisionNLP & Conversational AIAI AgentsPredictive AnalyticsMLOpsAI IntegrationEdge AIData EngineeringResponsible AICustom AI DevelopmentGenerative AI & LLMsMachine LearningComputer VisionNLP & Conversational AIAI AgentsPredictive AnalyticsMLOpsAI IntegrationEdge AIData EngineeringResponsible AI

What we build

AI development for every part of your business

From a first prototype to a monitored production system, we build AI that works in the real world, not just in a demo.

Custom AI

Custom AI Software Development

End-to-end AI products built around your problem, from first prototype to a monitored production system.

GenAI

Generative AI & LLM Applications

Assistants, RAG search, content engines and copilots grounded in your own data and workflows.

Machine Learning & Predictive Analytics

Forecasting, scoring, recommendation and anomaly models that turn history into foresight.

Vision

Computer Vision Solutions

Detection, inspection, OCR and video analytics that see what your teams cannot watch all day.

Language

NLP & Conversational AI

Chatbots, voice agents, document understanding and sentiment analysis across languages.

Agents

AI Agents & Intelligent Automation

Autonomous agents and workflow automation that plan, use your tools and finish the work.

Integration

AI Integration & Modernisation

Add AI to your existing apps, ERP, CRM and data platforms without a risky rebuild.

Platform

MLOps & AI Infrastructure

Pipelines, deployment, monitoring and cost control so models stay accurate, fast and affordable.

Live pipeline

How we take AI from idea to production

Five stages turn a business problem into a reliable, measured AI system. Hover to hold a stage, click to jump.

Step 01 of 5

Discover

We turn a business problem into a measurable AI use case with clear value, data needs and a realistic scope.

Opportunity and ROI workshops
Data and feasibility assessment
Success metrics agreed up front

Right problem, right scope

Where it applies

One AI engineering team, six industries

Pick the programme that matches your business. We reuse the same proven data, modelling and MLOps core.

AI that supports clinicians, not replaces them

Clinical documentation, imaging support, patient engagement and operational forecasting with privacy at the core.

Clinical note summarisation
Imaging and triage support
Patient chatbots and reminders
Capacity and no-show forecasting

Plan this project

Hospital opsCare AI

91%note accuracy

Notes drafted412
Time saved38 h
Flagged7

Open dashboard

Live in production

Monitored 24/7

Why Aarvexa

An AI partner that ships, not just advises

AI only pays off when it reaches production and moves a business metric. We pair modern models with solid engineering, testing and MLOps, so quality is measured, not assumed.

Who we work with

Fixed-scope pilotsDedicated AI teamsTime & materialsCo-developmentAI centre of excellenceSupport & MLOps retainers

Business outcomes first

We define success metrics before writing code and report on them throughout, so AI is judged on value, not novelty.

Responsible by design

Privacy, security, fairness and explainability are built in from day one, not bolted on at the end.

Production, not prototypes

Our teams ship monitored systems with MLOps, testing and handover, so models keep working after launch.

One accountable team

Data engineers, ML engineers, designers and product leads work as one team with a single point of ownership.

Technology

A stack built for production, proven on real projects

We choose the right tool for each layer, from the model to the monitoring dashboard, and swap parts when your platform changes.

Models & frameworks

PyTorchTensorFlowscikit-learnHugging FaceClaude / GPT / GeminiLlama / Mistral

Data & pipelines

PythonSparkAirflowdbtSnowflake / BigQueryKafka

GenAI & retrieval

LangChain / LangGraphRAG pipelinespgvectorPineconeFine-tuning / LoRAGuardrails

Cloud & MLOps

AWS SageMakerAzure MLVertex AIKubernetesMLflowTerraform

Apps & integration

Next.js / ReactFastAPI / NodeREST / GraphQLSalesforce / SAPMobile SDKsEdge runtimes

Responsible AI & security

Secure, fair and fully accountable

AI is judged by what it decides on your behalf. We design for that from day one.

Security first

Encryption, least-privilege access, secrets management and secure deployment across every environment.

Data privacy

Private cloud or on-premises options, PII redaction, consent handling and retention rules aligned with GDPR.

Fair and explainable

Bias testing, explainability tools and documentation so decisions can be reviewed and defended.

Governed and monitored

Audit trails, human-in-the-loop controls and drift monitoring keep models accountable in production.

Our process

From first idea to AI in production

Indicative timelines for a typical pilot. We confirm yours after discovery.

01Week 1–2

Discovery & roadmap

Use cases prioritised by value and feasibility, with a roadmap, budget range and success metrics.

02Week 2–5

Data foundation

Data access, quality, labelling and security set up so the model has solid ground to learn from.

03Week 4–8

Prototype & prove

A working proof of concept tested on your data, with results compared against the agreed targets.

04Week 8–16

Build the product

Production-grade engineering: APIs, interfaces, integrations, testing and security hardening.

05Week 14–20

Launch

Staged roll-out with monitoring dashboards, runbooks and training for your team.

06Ongoing

Operate & improve

MLOps, retraining, cost tuning and new features as your data and goals evolve.

FAQ

Frequently Asked Questions

Straight answers on AI development: applications, technology, safety, cost and timelines.

They are services that design, build, deploy and maintain AI-powered software: from machine learning models and generative AI applications to computer vision, chatbots, agents and the data and MLOps foundations behind them.

End-to-end AI delivery: use-case discovery, data engineering, model development, application engineering, integration, testing, deployment and ongoing monitoring, by one accountable team.

Start-ups, mid-sized companies and enterprises in healthcare, finance, retail, manufacturing, logistics, education and more, whether you are starting with AI or scaling existing efforts.

Not always. Many valuable use cases work with modest data, pre-trained models, retrieval over your documents or synthetic data. We assess feasibility in discovery before you commit.

Book a free consultation. We review your goals and data, suggest the best-fit use cases and outline a scoped pilot with clear success metrics.

Let's build it

Ready to build AI that delivers?

Book a free consultation. We will review your goals and data, show a live demo and map out a pilot you can test on your own use case.

Book a free consultation Explore our AI services