AI & ML Development Services

Clean data in, working product out. AI and ML development scoped to your budget, your data, and for teams that need results in production

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AI/ML Development in 2026

Data prep eats 40–60% of every ML project budget, yet most teams underestimate it until the money is already spent

87% of ML projects never reach production because the gap between a working notebook and a real system is wider than anyone expects

Companies that apply AI to the right problem compress decision cycles, cut operational costs, and create products their competitors cannot replicate

Pre-trained foundation models from OpenAI, Google, and Anthropic have cut the cost of entry for startups and mid-size teams by an order of magnitude

Common AI & ML Development Challenges

Nobody set success criteria before the project started

Vague goals like “we need AI” lead to months of research with no clear outcome. Without measurable targets, there is no way to know when the project is done or whether it delivered value.

Models degrade over time without anyone noticing

Data distributions shift. Upstream pipelines change. A model that worked last quarter produces worse results this quarter, and without monitoring, the team only finds out when customers complain.

The skill gap spans two disciplines

For prediction models, AI/ML work requires both software engineering and applied mathematics. Statisticians write fragile code. Engineers struggle with probabilistic systems. Most teams lack people who can bridge both worlds.

How We Help

Data-First Project Scoping

Every engagement starts with a data audit. The team assesses your data quality, volume, and accessibility so the project plan reflects reality from day one.

Production-Grade Architecture

Uinno designs every AI system for production deployment. Deployment pipelines and infrastructure decisions happen in week one, not as an afterthought after the model is ready.

Measurable Goals

We help to define success criteria, KPIs, and evaluation metrics before the project kicks off.

Continuous Monitoring

Every model should be ships with automated monitoring, performance alerts, and retraining triggers. Drift detection catches accuracy drops before they affect business outcomes.

One Team, Full Lifecycle

Uinno covers the entire path from strategy through deployment and post-launch support. No handoff between a consulting firm, a data science team, and an engineering team. One team owns the outcome.

Uinno's Success Stories

AI Platform That Redefines How Consultants Work

Moonox is a US-based AI workspace platform built around configurable AI agents.

It helps teams in sales, consulting, and software development manage project context, run meetings, and handle routine tasks without jumping between a dozen different tools.

COMING SOON

AI Agents. Age Validation Solution (KYC)

Uinno developed a machine learning solution for accurate user age validation using convolutional neural networks and variational autoencoders.

We built and trained the model exclusively with publicly available data due to security restrictions on real user data.

The system achieved 80-85% accuracy across various age groups and processed 250,000-350,000 users daily.

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The AI-Search Visibility Analytics Platform

Engineered by a former Meta Director, Bubbling empowers marketers with unprecedented data on brand visibility within AI-driven search ecosystems.

Utilizing a proprietary system that simulates authentic human dialogue, Bubbling reveals the true landscape of how brands are perceived and recommended by AI, moving beyond traditional search metrics to capture the future of digital discovery.

COMING SOON

What Our Clients Say

Uinno’s project management was highly professional and well-structured. They delivered all planned results within the agreed timeline on a fixed-price basis. They went significantly beyond what we expected for this budget. The team’s combination of technical expertise (systems architecture) and strong business analysis skills is rare and was exactly what we needed.

Adrian Hryn

Project Manager at Ukrainian Catholic University

Adrian Hryn

Project Manager at Ukrainian Catholic University

I'm not saying this to sell anything or congratulate anyone but we've started running beta tests with potential customers, and as a former marketer, I've been running my own tests too. The quality of the prompts produced is mind-blowing.

Emmanuel Dollé

Chief Executive Officer at Bubbling

Emmanuel Dollé

Chief Executive Officer at Bubbling

The new system built by Uinno works perfectly well and has received positive feedback from users. Uinno managed the project well and effectively handled small delays. Their flexibility, agility, and technical skills stood out.

Jukka-Pekka Rahkonen

System Architect at Envirovet Oy

Jukka-Pekka Rahkonen

System Architect at Envirovet Oy

Through Uinno’s expertise, we redesigned and built a national social aid platform helping millions access assistance. They led strategy to delivery with professionalism, upskilled our government team, and ensured seamless collaboration.

Kostiantyn Koshelenko

Deputy Minister of Digital Transformation at MSP of Ukraine

Kostiantyn Koshelenko

Deputy Minister of Digital Transformation at MSP of Ukraine

Uinno remains my top development partner. Across multiple projects, their delivery, communication, and attitude are consistently excellent. Despite the distance, they maintain a seamless workflow and keep me fully updated. The team you can trust completely.

Garreth Wills

Founder at UserKind

Garreth Wills

Founder at UserKind

Uinno has done a great job building our website. Before working with them, our website was a mess; since Uinno took over, the site has improved quickly. Their speed when completing tickets and the valuable suggestions they offer also stand out.

Douglas Campbell

Professor of economics, Founder & CEO of Insight Prediction

Douglas Campbell

Professor of economics, Founder & CEO of Insight Prediction

I’ve been working with Uinno for over 10 years on products across the USA, Australia, and the UK. At our peak, we had more than 80 specialists involved and the experience has been exceptional. The team is brilliant, responsible, and genuinely cares about what they do.

Ben Beath

6x tech startup founder (four exits)

Ben Beath

6x tech startup founder (four exits)

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AI & ML Development Services

Custom Machine Learning Solutions

Most companies sit on data that could predict demand, flag fraud, or personalize user experiences. The gap between raw data and a working model is where projects stall. Uinno uses proven frameworks and pre-trained models, then fine-tunes them on your data to solve the specific problem that costs you money right now.

Natural Language Processing (NLP) Solutions

Customer feedback sits buried in tickets. Support conversations go unanalyzed. Documents require hours of manual review. NLP turns unstructured language into structured insight, with foundation models from OpenAI, Anthropic, and Google adapted to your domain through fine-tuning and RAG architecture.

Computer Vision and Image Recognition

Manual quality inspection cannot keep up with production speed and identity verification still relies on slow human review? Computer vision pipelines run from data labeling through real-time inference, tuned to your accuracy and requirements.

Generative AI and LLM Integration

Teams everywhere have experimented with ChatGPT and hit the same wall of generic responses, no connection to internal data, and zero control over output quality. Uinno helps with RAG architecture that is grounded in your proprietary data, establishes guardrails that prevent hallucinations, and enables cost control that keep API bills from spiraling.

AI Consulting and Strategy

Too many AI projects start with a vague mandate, burn through six figures, and never define what success looks like. AI consulting at Uinno starts with a use case worth solving. Readiness assessment, data audit, ROI modeling, and validation through a PoC before anyone commits to full build.

AI-Powered Web APP Development Process

#1 Discovery and Data Audit

The team assesses your data quality, business goals, and technical constraints. You get a data readiness report that shows where things stand and what needs to happen before model work starts.

#2 Strategy and Architecture

The team defines the ML approach, selects the tech stack, and designs the system architecture. This stage determines whether the project needs a fine-tuned foundation model, a custom training pipeline, or a simpler solution.

#3 Data Preparation

This is the work nobody warns you about. Data cleaning, labeling, feature engineering, and pipeline setup account for 40–60% of total project effort. Skipping this step is the fastest way to waste money on a model that scores well in tests but breaks in production.

#4 Model Development and Training

The team works on top of existing foundation models from OpenAI, Anthropic, Google, and open-source alternatives. Fine-tuning, adaptation, and training happen on your data. Every experiment is tracked and documented for reproducibility.

#5 Testing and Validation

The team tests against holdout data, edge cases, and business-specific scenarios. If a model does not perform on your real-world data distribution, it does not ship.

#6 Deployment and Monitoring

The team deploys to production with automated monitoring, drift detection, and retraining pipelines. Models do not break with a loud signal. They degrade in silence. Monitoring catches the drift before business metrics drop.

What Sets AI/ML Development Apart

The code is a small fraction of the system

In traditional software, the codebase is the product. In AI/ML, the model is one component inside a larger system of data pipelines, feature stores, training infrastructure, serving layers, and monitoring tools. The code that defines the model itself can be under 100 lines. Everything around it is what takes months.

Testing requires a different mindset

Standard software has deterministic tests like input X produces output Y. ML models produce probabilistic outputs. Testing means statistical validation, performance benchmarks across data slices, and evaluation against business-specific thresholds.

Maintenance never stops

Traditional software works the same way until someone changes the code. ML models degrade as the real world shifts. Customer behavior changes. Market conditions evolve. A model that was accurate six months ago may produce poor results today without any code changes. Ongoing monitoring and retraining are not optional.

How We use AI IN DEVELOPMENT PROCESS

Planning

AI agents help surface logical gaps and cover non-functional requirements before a single line of code is written. Fewer surprises mid-sprint means fewer budget overruns and less rework.

Development

AI agents handle iterative code changes against a structured plan. The conversion from natural language to working, reviewed code is several times faster than manual development.

Testing

AI agents write test suites and test cases, and accelerate manual QA of complete features. Coverage goes up. Time investment goes down.

Code Review

A two-stage process. First, an AI agent runs a review against a predefined instruction set and filters out 70–80% of issues before they reach a human reviewer. Then an Engineering Manager validates and signs off.

Our non-negotiables

The team always remains code owners and validates every AI output. Accountability for incidents stays with humans, not tools.

AI & ML DEVELOPMENT COST

PoC

$2K – $4K

A focused experiment to validate whether an AI approach works on your data and your problem.

The output is a working prototype with performance benchmarks, enough to make a go/no-go decision with confidence.

Timeline:

1–3 weeks

Minimum Viable Product (MVP)

$10K – $20K

A production-ready first version with core AI functionality, integrated into your tech stack and ready for real users. Scoped tight, shipped fast, and ready to iterate on.

Timeline:

1– 2 months

Custom AI/ML Solution

$40K+

A trained, tested, and deployed system with monitoring, retraining pipelines, and integration across your existing infrastructure.

Timeline:

2–4 months

AI/ML TALENT ACQUISITION STRATEGY

In-house AI Team

Full control, deep context, long-term ownership. But hiring ML engineers, data scientists, and MLOps specialists takes 3–6 months and costs $500K+ per year in salaries alone. This makes sense for companies where AI is the core product, not a feature.

Freelance ML Engineers

Good for narrow tasks like model fine-tuning or data pipeline work. Less effective for end-to-end projects that require architecture decisions, infrastructure setup, and production deployment. Coordination overhead grows fast with multiple freelancers.

AI Development Agency or Partner

A full team (data engineers, ML engineers, MLOps, QA) that starts on day one, with a proven process for going from business problem to production system. Lower risk than building in-house, faster than recruiting, and more accountable than freelancers.

Hybrid Model

Your product team sets the direction. An external AI partner handles the technical execution. This works well for startups and mid-size companies that have strong product leadership but lack specialized ML engineering capacity.

Tools we use for SOFTWARE PRODUCT development

We are not attached to a particular technology stack. We solve your business challenges using the most fitting programming languages and frameworks.

HTML5 / CSS3

React.js (Next.js, Gatsby)

Bootstrap

Vue.js (Nuxt.js, VuePress)

Material UI

A-Frame

JavaScript & JQuery

Ember.js

Typescript

Svelte

Angular.js

С# (.NET, ASP.NET)

Node.js (Nest.js, Fastify, Express, Strapi)

Python (Django, Flask, FastAPI)

Ruby (Ruby on Rails, Sinatra)

Golang

Headless CMS

MySQL

DynamoDB

MongoDB

Oracle

Redis

SQLite

PostgreSQL

Firebase

Amazon Web Services (AWS)

Nomad

Microsoft Azure

Serverless

Google Cloud Platform

Terraform

Docker

CloudFormation

Ansible

Puppet

Kubernetes

Ethereum

Polygon (Solidity)

Solana (Rust)

Infura

Alchemy

Tatum

Moralis

Swift

Objective-C

Java

Kotlin

Flutter

React Native

Sketch

Figma

Adobe Illustrator

Adobe Photoshop

Adobe XD

InVision

Zeplin

Principle

Confluence

Slack

Jira Software

About Uinno

2018

founding year

250+

Clients worldwide

93%

client retention on long-term projects

We're the team that stopped 80% of fraud for a platform with 250K daily signups, earned government trust from the University of Melbourne, and won "Fintech Product of the Year" for humanitarian innovation.
‍
Born from experience with Toyota, Loreal, and NAB, Uinno brings Fortune 500-caliber engineering to anyone tired of working with vendors who just execute because your business deserves partners who think, care, and deliver like owners.

READ OUR STORY

FAQ

What AI and ML development services do companies typically need?



Most companies start with a specific business problem, not a technology wish list. Common entry points include predictive analytics for demand forecasting, NLP for customer support, computer vision for quality inspection, and recommendation engines for personalized experiences. A good AI development partner helps identify the highest-ROI use case first, validates it with a proof of concept in 2–4 weeks, and scales from there

What is the difference between AI development and ML development services?



AI is the broader discipline of systems that simulate intelligent behavior. ML is a subset that focuses on training models to learn patterns from data. In practice, most AI projects use ML as the core engine. An AI/ML development company delivers both, the ML models that power the intelligence and the application layer that serves it through APIs, dashboards, or automation workflows.

Should I hire an in-house AI team or work with an AI development partner?



It depends on whether AI is your core product or a feature. An in-house team gives you full control but takes time to hire and costs $500K+ per year. An external partner gives you a full team on day one with a proven process. Many startups and mid-size companies use a hybrid model: internal product leadership with an external team for ML engineering. This is the cooperation model we have with our top startup client Moonnox.

Can a small or mid-size business afford AI development services?



Yes. Pre-trained foundation models, cloud ML platforms like AWS SageMaker and Google Vertex AI, and open-source frameworks have reduced both cost and complexity. A startup can launch a working AI feature for $10K–$30K by focusing on a single high-impact use case and iterating on real user data. Uinno’s AI-powered delivery process also cuts timelines and costs by about half compared to traditional development.

AI & ML Development Services

AI/ML Development in 2026

Uinno’s Results

2019

800m

3x faster

Common AI & ML Development Challenges

How We Help

Uinno's Success Stories

What Our Clients Say

AI & ML Development Services

AI-Powered Web APP Development Process

What Sets AI/ML Development Apart

How We use AI IN DEVELOPMENT PROCESS

AI & ML DEVELOPMENT COST

AI/ML TALENT ACQUISITION STRATEGY

Full-Cycle SOFTWARE Development Services

Tools we use for SOFTWARE PRODUCT development

About Uinno

FAQ

we have the means to get you there !

We stand with Ukraine