Custom AI Development Services

Build your AI product from scratch. Purrweb designs and builds AI products from the ground up — strategy and feasibility, architecture and model selection, data pipelines, and production infrastructure. This is AI development for a net-new product, not adding a feature to software you already run.

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AI development services cover the end-to-end process of designing, building, and deploying an AI product from scratch — architecture, model selection or training, data pipelines, and production infrastructure. It differs from AI integration, which adds AI features to software that already exists.

Starting from an idea rather than an existing app? You are in the right place. Already have a product and just need AI added? See our AI integration services — that is a different engagement from the ground-up build described here. As a software development company with a decade of product work behind us, our AI experts cover machine learning, deep learning, and generative artificial intelligence, and deliver tailored AI through end-to-end AI development.

What Our AI Development Services Include

AI development covers four connected phases: strategy and feasibility, model and architecture design, build and integration, and post-launch operation. Our AI development services map to those phases as named offers, so you can engage us for the whole journey or a single stage.

Across these services we develop AI tools a team actually uses — an AI platform, an AI chatbot, or a conversational AI assistant. Because Purrweb comes from a software development services background, an AI solution ships with the same rigour as any custom software development: a dedicated development team, responsible AI practices, and cloud-based AI infrastructure sized for enterprise AI adoption and AI implementation.

AI Strategy & Feasibility Consulting

Every AI solution starts with AI strategy and feasibility work — we assess data readiness, define success metrics, and decide which parts of the problem genuinely need machine learning. You get a go/no-go you can act on before a line of production code is written.

Custom AI/ML Model Development

We build custom AI and machine learning models around your data — training, fine-tuning, or combining classical ML with a large language model, and using deep learning or reinforcement learning where the problem calls for it, with the data pipelines to keep the model accurate in production.

Generative AI & LLM Product Development

We design generative AI products around a large language model — retrieval over your own data, guardrails, and evaluation, on foundation models such as OpenAI, Anthropic Claude, and open-weight Llama. The result answers from your knowledge, not a generic chatbot.

AI Agent Development

We build AI agents, not just chatbots — an AI agent plans multi-step tasks, calls tools and APIs, and acts inside your systems under policies you define, with explicit tool boundaries and human-in-the-loop checkpoints where the stakes require them.

Computer Vision & NLP Applications

We build vision and language AI applications — computer vision for quality inspection, document understanding, natural language processing, and robotic process automation — as production services with the data labelling and inference infrastructure to run at your volume.

MLOps & AI Infrastructure

A model is only a product once it is deployed and re-trainable. Our MLOps work covers the AI infrastructure — CI/CD for models, versioning, drift monitoring, and the cloud stack that serves inference — so your AI system keeps working after launch.

See how we scope a custom AI build

AI Development Services vs. AI Integration — Which Do You Need?

AI development means building a product around AI from day one; AI integration means adding AI capabilities to software you already run.

Choose AI development for a net-new product idea that needs the architecture, model, and infrastructure built from scratch. Choose integration when you have a working app and a specific feature request. If you already have a product and just need AI added, our AI integration services team handles that instead — getting this split right early saves the most expensive kind of rework.

Starting your AI product is AS SIMPLE AS 1-2-3

Schedule a call

After you fill out the form, our manager will contact you within 24 hours and arrange a call. If necessary, we are ready to sign an NDA.

At the meeting, we will get to know each other and discuss the project. We'll also talk about the approximate budget and deadlines.

Fill out the form

Dive into project details

After the first meeting, a business analyst runs a mini-audit: gathering technical and data requirements and framing the AI use case. To do that, we'll talk about your project, what your goals and restrictions are, and look at positive and negative references.

Prepare and submit
an estimate

We prepare a detailed estimate and presentation, which includes cost, timeline, team, and project roadmap.

After that, all that's left is to sign the contract,and we'll get started on your project! Now, let's talk about what happens after the estimate.

How We Build a Custom AI Product: Our Process

Discovery & Feasibility

Result: Feasibility report, success metrics, data audit

Average time frame: 1–2 weeks

A business analyst and an AI engineer study the problem, the available data, and the desired outcome. We decide whether the use case needs machine learning at all, and if so, which approach fits.

You leave this stage with a go / no-go you can defend to a board — scope, risks, and the metric that defines success.

Architecture & Model Selection

Result: AI architecture and model plan

Average time frame: 1–2 weeks

We design the AI architecture end to end: data flow, model choice (train, fine-tune, or foundation model plus retrieval), inference path, and the infrastructure that serves it.

Model selection is driven by your accuracy, latency, cost, and privacy constraints — not by whichever model is in the headlines.

Prototype / PoC

Result: Working proof of concept

Average time frame: 3–5 weeks

We build a proof of concept that proves the hard part — the model behaving on real data — before committing to the full build. This is often where an AI solution overlaps with an MVP development engagement: a small, real slice you can put in front of users or investors.

If the PoC does not hit its metric, you have spent weeks, not months, finding out.

Build & Training

Result: Production-grade AI product

Average time frame: 1–3 months

With the approach validated, we build the full product: the model and its training or fine-tuning loop, the data pipelines, the application around it, and any front-end. We work in two-week Agile sprints with the client in the loop.

Software development and AI development run together here — the model is only useful inside a real application.

Deployment & MLOps

Result: Deployed AI system with monitoring

Average time frame: In parallel with build

We deploy the model with the MLOps layer around it: model versioning, monitoring for accuracy drift, and a path to re-train. Inference runs on a cloud stack chosen for your load and budget.

This is the step that separates a demo from a product that survives contact with production traffic.

Ongoing Iteration

Result: Maintained, improving AI product

Average time frame: Depending on client needs

AI products are never finished at launch — data shifts, and models need re-evaluation. We support the product after release, watch the metrics, and iterate on the model and features.

If you plan to bring the work in-house later, you receive the documentation and a clean handover.

AI Tech Stack We Use

Our AI technologies are grouped by layer — the model, the framework it runs on, the retrieval and orchestration around it, and the MLOps and cloud that serve it in production. AI engineering choices here decide whether the product can scale AI workloads later without a rebuild.

Foundation models & LLMs

OpenAI, Anthropic Claude, and open-weight models such as Llama for generative AI and LLM products — chosen per use case for quality, cost, and data-privacy fit.

ML frameworks

PyTorch and TensorFlow for custom model development, fine-tuning, computer vision, and NLP.

Orchestration & retrieval

LangChain and vector databases for retrieval-augmented generation, so AI agents and assistants answer from your own data rather than guessing.

MLOps & cloud

AWS Bedrock and SageMaker, Google Cloud Vertex AI, and Azure AI for training, deployment, and inference — with the monitoring and versioning that keep an AI system reliable.

Industries We Build Custom AI For

Healthcare

We build custom AI for healthcare products where off-the-shelf tools fall short of regulatory and accuracy requirements — for example, a clinical-summary model over a patient record, or a triage assistant that routes symptoms to the right pathway.

Regulated data means the model, storage, and audit trail are designed for compliance from the first sprint.

Fintech

For fintech, a common from-scratch build is a fraud-detection or risk model trained on transaction data, or a document-understanding pipeline for KYC.

These are bespoke AI solutions because the signal lives in your data — a generic model has never seen it.

E-commerce & Retail

Retail AI products we build from scratch include demand-forecasting models that drive restocking, and recommendation systems trained on your own catalogue and behaviour.

The output is a predictive-analytics product, not a plug-in — owned by you and improving as data accrues.

Logistics

In logistics, we build route-optimisation and estimated-time-of-arrival models that account for traffic, weather, and historical performance.

Because the constraints are yours, the model is trained on your fleet and lanes rather than a public average.

Manufacturing

Manufacturing clients come to us for computer-vision quality inspection — a model that flags defects on the line faster and more consistently than manual checks.

We build the vision model, the labelling loop, and the edge or cloud inference that runs it at line speed.

How Much Does Custom AI Development Cost?

AI development cost depends on the tier: a proof of concept is the cheapest, a production AI product costs more because of model training and data engineering, and an enterprise system carries ongoing MLOps cost. AI/ML projects with model training and data pipelines commonly run above a standard app build.

We share cost drivers rather than a single headline number, because a fabricated range helps no one. For a rough app-cost reference point, see our guide on how much AI software development costs; for your specific build, we quote against scope after the feasibility stage.

Tier 1 — Proof of concept / prototype

The lightest engagement validates one model on real data. Purrweb's general project pricing starts around $3,000–$15,000, and a focused AI proof of concept sits at the lighter end of that band — you are paying to answer one question before a bigger commitment: does the model work on your data?

Tier 2 — Production AI product

A full AI solution with training, data pipelines, an application layer, and deployment costs more than a prototype, because model training, data engineering, and MLOps are real work beyond a typical app build. We scope it per project — the driver is model complexity and data volume, not screen count.

Tier 3 — Enterprise-scale with MLOps

At enterprise scale, ongoing MLOps, monitoring, retraining, and compliance dominate the cost, and they are recurring rather than one-off. We do not publish a fixed number here, because an honest one depends on data volume, latency targets, and regulatory load.

In-House AI Team vs. an AI Development Company

Building an in-house AI team gives you long-term ownership; hiring an AI development company gives you speed and a full team from day one. The right choice depends on how core AI is to your business and how fast you need to move.

Build an in-house team

You recruit ML engineers, data engineers, and MLOps specialists — roles that are scarce and slow to hire, with lead times measured in months before the first model ships.

Best when AI is core to your business long-term. For a single product or a first AI bet, the overhead rarely pays off.

Hire an AI development company

AI development companies bring strategy, ML, and infrastructure as one team, so you reach a working product faster and only carry the cost while you need it — folding in the surrounding AI services and application development so the model ships inside a real product.

Many clients start with an agency build, then hire in-house once it proves out — so we hand over clean docs and portable code. (Weighing it against a simpler assistant? See chatbot development cost.)

Why Work With Purrweb for AI Development

A mobile-first product background

AI products increasingly need a production-grade mobile or cross-platform front-end from day one — not just a backend model. Our mobile app development heritage means we ship the whole product, model and app together — unlike most AI dev agencies, which are enterprise-software generalists without that background.

A product team, not just model builders

We pair AI engineers with product designers and developers, so the model lands inside something people actually use. Strategy, design, build, and MLOps sit under one roof, whether the product needs generative AI solutions or a trained custom model.

A B2B agency you can hand off from

We use common frameworks and document as we go, so an internal team or another agency can pick the project up later. You own the product and the code.

Ten years and 46 niches

Purrweb has been building software products since 2014, headquartered in the UAE, across 46 niches. That breadth means we understand the business around the AI, not only the model.

Some facts about our AI development company

550+

Project released

Clients from all around the world

Working with customers from USA, UAE, Japan, and Germany

10 years

Of making apps for startups and businesses

>50К

Positive reviews collected by our customers' apps in the app stores

>7 years

Longest partnership with the client

200+

People on the team

Frequently Asked Questions

How much does custom AI development cost?

Custom AI development cost scales by tier. A proof of concept sits at the lighter end of Purrweb's $3,000–$15,000 project range; a production AI product with model training, data pipelines, and MLOps costs more, and we quote it per project after a feasibility review rather than publishing a fixed number.

How long does it take to build a custom AI product?

A proof of concept typically takes 3–5 weeks; a production-grade AI solution usually runs one to three months of build on top of discovery and architecture. The variable is data readiness and how much model training the use case needs.

What's the difference between AI development and AI integration?

AI development builds a product around AI from scratch — architecture, model, and infrastructure. AI integration adds AI features to software that already exists. If you have a working app and just need AI added, see our AI integration services.

Why do most AI projects fail?

The common failure modes are unclear scope, no plan for production infrastructure, and data that is not ready for the model. Projects that treat an AI build as an artificial intelligence research demo rather than a product — with deployment, monitoring, and re-training planned in — are the ones that stall. Our feasibility stage exists to catch these before the budget is committed.

Can AI be integrated with our existing systems and data?

Yes. Even a from-scratch AI product connects to your data sources and systems through APIs and retrieval, and we design that data flow as part of the architecture. If the work is mostly connecting AI to an existing app, our AI integration services team is the better fit.

What industries benefit most from custom AI development?

Industries where the valuable signal lives in private data — healthcare, fintech, logistics, retail, and manufacturing — benefit most, because an off-the-shelf model has never seen that data. Regulated sectors gain the most from custom over off-the-shelf, since compliance and auditability are built in.

How do I choose the right AI development company?

Look for a portfolio of shipped products, a team that combines machine learning with product engineering, and a clear plan for post-launch MLOps and support. A company that only builds models, with no path to production or handover, is a risk regardless of its research credentials.

Do you build AI agents, not just chatbots?

Yes. A chatbot answers questions; an AI agent plans multi-step tasks, calls tools and APIs, and takes actions in your systems under policies you set. We build agentic AI with explicit tool boundaries and human-in-the-loop checkpoints where the stakes require them.

Mariano Ponce

CEO

Moon Sun Fire Limited - Health & Fitness Startup