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AI Product
Development for
Faster Time to Market

Our engineers use AI tools at every stage. From scoping and architecture to coding, testing, and deployment. You get the same senior-level delivery at a fraction of the traditional timeline and cost.

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REALNE WDROŻENIA, NIE DEMO AI

Instead of another technology demo, you get a team that puts AI to work in business every day.

THE 3 DELIVERY BLOCKERS

Why Most Software Projects
Cost More and Take Longer
Than They Should

Most software houses haven't changed how they work in years. You're paying for the same hours, the same bottlenecks, and the same overengineered product development process that slowed your last vendor down too.

Smiley Wrong

You're paying senior rates for junior-level tasks

Without incorporating AI into the development process, 40–60% of a senior developer's time goes to boilerplate code, writing tests from scratch, documentation, and manual code review. You pay senior rates for work that AI completes in minutes — and your product ships slower because of it.
X Circle

Estimates grow, deadlines slip, budgets stretch

Traditional development cycles have no built-in mechanism for catching problems early. Every sprint ends the same way: scope creep, renegotiation, new estimates. Incorporating AI into the process doesn't eliminate risk, but it detects it earlier, shortens reaction time, and keeps delivery on track instead of letting issues compound sprint after sprint.
Gallery Streamline

You get a product, but not the speed your market demands

Teams using AI-augmented development deliver features 2–3× faster than those running traditional product development processes. A product that takes nine months to build can be irrelevant before it reaches the market. In today's development cycles, speed is not a bonus. It is a competitive requirement.
WHAT WE OFFER

Comprehensive implementations based on process automation, machine learning, and
generative artificial intelligence.

We don't sell a single AI tool or one technology for everything. We cover the full range of expertise needed for a real implementation: from process automation, through data analysis and machine learning, to generative artificial intelligence.

Automation

Process automation and reduction of repetitive tasks

We implement process automation where your people spend the most time on tasks that don’t require their expertise. AI tools take over repetitive work-data entry, report generation, and handling requests, so your team can focus on decisions that truly require a person. Well-chosen automation tools increase the whole team’s productivity without increasing headcount.

Data and ML

Data analysis and machine learning for business decisions

Before we propose a solution, we conduct an in-depth analysis of your data - not just a sample, but the full set of information your company already has. Machine learning models help uncover patterns that are hard to spot in a spreadsheet and turn large-scale data analysis into concrete, actionable insights. The result: better, faster decisions based on facts rather than intuition, and more effective data management across the organization to support sound decisions.

Generative AI

Implementations based on generative artificial intelligence and natural language processing

We build solutions using generative AI and natural language processing wherever fast, natural communication with customers or employees matters - chatbots, automated summaries, and content generation. When image interpretation is needed, we use computer vision. The result is intelligent systems that work in real time, use AI rather than rigid rules, and can analyze text at a scale beyond the capacity of a human team.

Security

Data security and compliance in AI projects

We build every project on the principle that data security is a foundation of the architecture, not an afterthought. The AI systems we implement undergo risk assessment and access control checks to ensure they meet the regulatory requirements of your industry. This lets you deploy AI solutions without worrying about customer data leaks, regulatory breaches, or reputational risks that could outweigh the benefits of automation.

Optimization

Post-implementation support – monitoring and optimization of AI systems

Implementation is the beginning of our work together, not the end. After launch, we monitor how the AI systems perform, tracking response quality, costs, and latency in real time - before problems arise. That way, the solutions we implement remain effective over time. We continually fine-tune them using real data from your company, so they deliver lasting value rather than a one-off result.

HOW WE WORK

Companies that successfully implement AI
follow a clear plan.

Managing AI projects requires a different approach from a traditional IT project. That’s why we work in five clearly defined stages, each with a concrete deliverable. You know exactly which stage you’re at, what it costs, and what you receive at each step.

01
Weeks 1–2

Needs and scope analysis

Before we propose any tool, we map your actual business needs and assess where implementing AI will deliver value and where it would be an unnecessary expense. We work with data from your company, not assumptions, helping you avoid costly mistakes at the start of the project.

Result: Scoping document AI fit assessment Tech stack recommendation
02
Weeks 2 - 4

Architecture design and selection of AI tools

We design the system architecture and select AI tools that fit your company’s scale, budget, and security requirements. This is where we make the decisions with the greatest impact on the cost and timeline of the entire implementation, so we give this stage as much attention as the coding itself.

Result: Architecture diagram Model selection rationale Data flow map
03
Weeks 4–8

Rapid prototyping and user validation

Instead of spending months working in the dark, we build a working prototype and test its practical applications with real users. We gather feedback before you commit the full budget, then make a go/no-go decision together based on test data rather than gut feeling.

Result: Validated AI prototype Feedback report Go / No-go decision
04
Weeks 8–20

AI-assisted production deployment

Our team uses practical AI tools throughout coding and testing to build a complete solution ready for production. We develop automated tests alongside the code, rather than afterward, so deployment doesn’t end in a rush of last-minute fixes.

Result: Production-ready AI product Integration tests Deployment runbook
05
Launch

Launch, monitoring, and continuous optimization

Launch is not the end of the project - it’s the start of the system’s evolution. We regularly review its performance, analyze data from real-world use, and make improvements before small issues become costly problems. Your AI system keeps getting better instead of becoming outdated.

Result: Launch checklist Monitoring dashboard 30/60/90-day review
PROVEN IMPLEMENTATIONS

How we implemented AI in products that work and scale

The implementations below show how AI performs in practice across industries - from customer service and e-commerce to education, healthcare, and the public sector. Companies use AI to automate responses to customer questions and communication on social media, as well as to integrate with ERP systems that support teams in their daily work.

AI Business Plan Generator
Exegov

AI Business Plan Generator

Problem:

Founders needed a single process that would turn the data they entered into a complete business plan, OKRs, and a task list, without manually copying information between the tools they use every day.

Decision:

Selleo built a workflow powered by OpenAI, with structured JSON output and a dedicated Kanban workspace tailored to the needs of this business.

Result:

100% automation of business plan creation, 60% faster task setup, and planning reduced from weeks to minutes.
AI-powered e-commerce app
BrandActif

AI-powered e-commerce app

Problem:

BrandActif needed to redesign its product, add image recognition, and enable customers to shop directly from images posted on social media and viewed on different devices.

Decision:

Selleo built a progressive web app (PWA) with a real-time GraphQL API, image recognition, and scanning technology, ready for further integrations, such as with the client’s ERP systems.

Result:

The platform works across all browsers and operating systems, performs well on slow mobile networks, and supported a campaign featured in the first “shoppable” issue of Cosmopolitan.
AI-powered e-learning platform
Qstream

AI-powered e-learning platform

Problem:

Training teams at companies across various industries needed to create educational content faster without compromising learner engagement or learning outcomes in day-to-day work.

Decision:

Selleo enhanced the mobile-first platform with a custom AI-powered feature, advanced analytics, and scalable administrative processes.

Result:

Custom AI-powered content creation made course development five times faster, knowledge retention was 170% higher than with traditional formats, and short learning scenarios achieved an average engagement rate of 93%.
TECHNOLOGY STACK

AI tools our engineers use to deliver your product faster.

We select AI tools based on one criterion: whether they genuinely increase our team’s capabilities without compromising quality. As artificial intelligence becomes standard practice, choosing tools that fit business goals matters more than following short-lived trends. This technology stack shows what works in our process every day.

Language models

01
  • OpenAI GPT-4o
  • Anthropic Claude
  • Mistral
  • Meta Llama

When a product needs AI features, we choose a model based on the use case, budget, and latency requirements, because the provider affects both response quality and ongoing costs.

Orchestration and RAG

02
  • LangChain
  • LlamaIndex

When building AI for searching and summarizing data, we ensure reliable, hallucination-free answers, even for complex queries.

Vector databases

03
  • Pinecone
  • pgvector
  • Weaviate

When a product requires fast search or contextual memory, we use these solutions to help AI provide relevant answers and personalize results without manual data analysis.

AI cloud platforms

04
  • AWS Bedrock
  • GCP Vertex AI
  • Azure OpenAI

When infrastructure and compliance matter, we deploy AI on a platform suited to your scale, security requirements, and budget, providing a stable foundation for your product’s future growth.

Monitoring and MLOps

05
  • LangSmith
  • MLflow
  • Weights & Biases

LangSmith, MLflow, and Weights & Biases monitor quality after deployment, tracking performance, drift, latency, and costs to make the most of the models’ capabilities.

AI UX Frontend

06
  • React
  • Streaming APIs
  • WebSockets

React, streaming APIs, and WebSockets improve AI features in the interface, providing faster feedback, simpler interactions, and greater confidence in real-time responses.

AI-assisted development

The AI tools behind faster, safer delivery

We bring proven, widely-adopted AI tools into a senior-led engineering process — used with judgment, not as a replacement for it. Here is the stack our teams reach for, and where each part fits.
01

AI coding assistants

Tools embedded directly in the coding workflow, so engineers move faster on implementation, refactoring and everyday development.

  • Claude Code
  • Coursor
  • Codex
02

In-workspace AI editors

AI-native editors that let the team prompt, review and ship without leaving their development environment.

  • Hermes
  • Claude
  • Gemini
03

AI app builders

Tools that turn a prompt into a working app or interface — fast enough for prototypes and front-end scaffolding.

  • Claude Design
  • Figma Make
04

AI code review & testing

Automated review and test generation that catches bugs and regressions before they reach your users.

  • Lovable
  • Figma Make
  • Claude Design

Every tool is applied by senior engineers who own the architecture, product decisions and quality. AI removes the routine — people keep the judgment.

See how we work
COOPERATION MODELS

Three ways to work together that help us deliver faster

2–4 engineers

AI Development Partnership

We join your team

Who it’s for

For companies with an existing team that lack senior AI expertise and need support to move faster without disrupting ongoing delivery.

What you get

We bring 2–4 senior engineers into your team for 3 to 12 months. They work within your existing processes and add delivery capacity where you need it most, without expanding your HR department.
Let’s talk
Most popular
12–24 weeks

Full AI-Augmented Product Development

We handle the process from start to finish

What you getFor founders and CTOs who need to build and launch a product faster, without hiring a larger team or committing their own staff to a slow, traditional development process.
What you getWe handle the work from scope analysis through deployment, take responsibility for delivery throughout the process, and turn an approved scope into a finished product in 12 to 24 weeks.
Let’s talk
Flexible Time & Materials

AI Team Extension

Senior engineers on demand

Who it’s forFor teams that need 1–3 engineers with hands-on AI tool expertise to accelerate a single feature, release, or critical delivery milestone tailored to customer needs.
What you getExperienced engineers in a flexible Time & Materials model that helps speed up development without restructuring your team or placing additional demands on your company’s staff.
Let’s talk

TEAM

Meet the AI engineers behind your product

AI Solutions Architect

ARCHITECTURE & STRATEGY

AI Solutions Architect

Responsible for model selection, RAG architecture, and the integration roadmap. Selects the right LLMs, vector databases, and safeguards based on your product’s scope, budget, and latency requirements.

Delivery & Execution

DELIVERY & EXECUTION

Delivery & Execution

Implements the production AI stack. Handles prompts, agent loops, and streaming UX, taking the product from a validated prototype to a working solution that is critical to the project’s pace.

AI Evals & MLOps Lead

QUALITY & MONITORING

AI Evals & MLOps Lead

Defines evaluation criteria, model drift detection, and rollback procedures. Monitors cost per request, latency, and output quality long after deployment to keep the product stable in day-to-day use.

CHECK THE FIT

Is AI-powered development right for your project?

It works best when speed and cost matter more than experimenting with AI. We understand that clients are at different stages of maturity, so we also consider ethical issues and potential risks of implementation. If you have a project to deliver and want to move faster at a lower cost than with traditional development, this approach could be right for you.

This approach is right for you if…

You have a defined scope and you are ready to move from concept to delivery without slow discovery phases.
You want to use AI in product development workflows to cut build time, not to run a research project or proof of concept.
You want to launch in the next 3–12 months and need a team with the right AI capabilities to keep your competitive edge in a market that is not waiting.
Your current vendor delivers too slowly or too expensively, and you need an alternative that uses emerging technology as a standard, not a premium add-on.
You care about production-ready delivery, full code ownership, and a team that uses machine learning algorithms and modern AI tools as part of every sprint, not as a showcase.
faq

We start with market research, analyzing market trends, user needs, and your current product ideas. Then we map them against delivery risk, budget, and the fastest path to value through product discovery. This helps us remove weak assumptions before they enter the build. You get a clearer scope, fewer reversals, and a faster start.

We use generative AI to speed up coding, technical analysis, documentation, and test creation. When integrating AI, we define exactly where artificial intelligence improves speed and where senior engineering review protects delivery quality. This keeps the process practical, controlled, and production-focused. That is the same approach we apply in our AI Solutions work.

We build quality control into the full product development life cycle, not only into QA at the end. In our product development pipeline, testing, review, and validation run in parallel with implementation. This helps us catch issues before staging, reduce rework, and protect release speed. You can see the same delivery model in our custom software development.

We use rapid prototyping and early validation to optimize product features before full delivery starts. We compare options against user feedback, technical risk, and delivery cost, so weak ideas do not consume budget. This shortens decision cycles and gives you better release priorities. When mobile experience is part of the product, we connect that work with our mobile development team.

We work inside your setup and strengthen it with senior engineers and AI powered systems that reduce routine work. This helps your team move faster without adding communication layers or losing technical visibility. You keep ownership of priorities, architecture direction, and delivery decisions. We add throughput, structure, and execution where it matters most.

We treat revolutionizing product development as improving delivery speed and post-launch stability at the same time. For us, increased sustainability AI means fewer rewrites, fewer blocked sprints, and lower operational waste after release. We add monitoring, iteration loops, and early defect detection across the release cycle. This keeps the product usable, scalable, and easier to improve over time.

We connect delivery decisions with market research, launch timing, and your marketing strategies before scope is locked. This helps us prioritize the right release, not only the next release. We use business context to rank features, reduce noise, and focus effort where it creates the most value. You get a clearer path from strategy to shipped product.

Contact us

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