Do we need to rebuild our platform to add AI?

Almost never. Most LLM integrations work on top of existing architecture – connecting to your APIs, databases, and workflows without a full rebuild. We map the integration points in the first week so you know exactly what's involved before we start.

How do you make sure the AI output is reliable enough for production?

We build evaluation and testing into the delivery process – not as an afterthought. That means defining what "good output" looks like for your use case, testing against real data, and setting up monitoring so you know when something drifts after launch.

What happens if the AI feature doesn't perform as expected after launch?

We don't hand off and disappear. Every build includes handoff documentation your team can act on, and we offer ongoing retainers for teams that want continued support. If something breaks or drifts, we're set up to catch it and fix it.

How do you handle data privacy and EU AI Act compliance?

We build compliance into the architecture – data residency, audit trails, access controls – not as a retrofit after a legal review flags the gap. For clients in regulated industries or operating under EU AI Act risk tiers, we address this in the scoping phase, before a line of code is written.

What's the difference between starting with an AI Opportunity Sprint and going straight to a build?

The Sprint exists because most teams arrive with an idea, not a spec. It de-risks the build: you get a working POC, an integration map, and an ROI estimate before committing to a full engagement. If you already have a validated concept and a clear brief, we can scope a build directly – we'll tell you which makes more sense after the first call.

How long does it take to go from idea to working feature?

Our AI product development timeline depends on the scope and complexity of your project. A single AI feature or integration can go from brief to production in a matter of weeks, while a full AI-native MVP typically takes longer, depending on the number of features, data readiness, and integration requirements. We work in short cycles so you start seeing working software early, rather than waiting for a single final release.

AI product development. Built, tested, live

From first feature idea to working software in production, our AI product development engineers will take you through strategy, design, development, and launch of your dedicated product.

Who has benefited from Boldare's expertise?

See the companies that trusted Boldare to get it done.

AI in production – across industries

The same challenge shows up in every industry: teams that can describe the AI feature they need, but can't get it from idea to production. Here's what that looks like when it ships.

ENERGY

Predictive maintenance, demand forecasting, smart grid optimization – built for energy teams that run on real-time data.

• RAG for Technical Knowledge Internal AI assistants trained on engineering manuals, asset histories, and compliance documentation – field teams get accurate answers in seconds without calls to HQ.
• Anomaly Detection Real-time pattern recognition across grid telemetry and operational data that surfaces anomalies before they escalate – built on your existing data infrastructure, not a parallel system.
• Document Intelligence LLM pipelines that process regulatory filings, compliance reports, and maintenance logs in minutes – structured, audit-ready output integrated with your existing document workflows.
• Renewable Energy Optimization AI that balances variable solar and wind output against grid demand in real time – reducing curtailment and improving renewable utilization without replacing existing grid infrastructure.
• Demand Forecasting 72–96 hour load forecasting pipelines that factor in weather, grid state, and historical consumption – structured output that feeds directly into dispatch and planning systems.
• Predictive Maintenance ML models trained on sensor and SCADA data that flag equipment degradation weeks before failure – integrated with existing monitoring infrastructure, no rip-and-replace required.

E-COMMERCE

Personalisation, search, and inventory intelligence – AI that directly moves conversion and margin.

• Semantic Search LLM-powered search that understands buyer intent, not just keywords – integrated with your existing product catalogue and returning relevant results from day one.
• Product Recommendation Engine Models trained on your catalogue, browsing behaviour, and purchase history that improve with every transaction – not generic collaborative filtering applied to your data.
• Dynamic Pricing AI pricing models that respond to demand signals, inventory levels, and margin targets in real time – without manual rule updates or dedicated pricing headcount.
• Returns Prediction ML models that flag high-return-risk orders before dispatch — based on product type, customer history, and order composition – reducing reverse logistics cost directly.
• Demand Forecasting & Inventory Intelligence SKU-level demand forecasting that reduces overstock and stockouts – integrated with your warehouse and procurement systems, not a standalone dashboard nobody checks.
• Customer Support Automation LLM-based support agents that handle tier-1 queries 24/7 — order status, returns, product questions – with clean handoff logic for complex cases and full integration with your existing helpdesk.

TRAVEL

Travel products compete on personalization and availability – two problems AI solves directly, at scale, without proportional headcount growth. Travel products compete on personalization and availability – two problems AI solves directly, at scale, without proportional headcount growth.

• Dynamic Pricing AI pricing models that adjust rates in real time based on demand signals, booking windows, and competitor data – without manual intervention or dedicated revenue management headcount.
• AI Itinerary Generation LLM-powered itinerary builders that generate personalised travel plans based on preferences, budget, and travel history – output that feels curated, not templated, and integrates with your booking flow.
• Customer Support Automation LLM-based agents that handle booking changes, cancellations, and tier-1 queries 24/7 – with escalation logic for complex cases and full integration with your booking and CRM systems.
• Demand Forecasting Forecasting models that predict booking volume by route, destination, and segment – so revenue teams price ahead of demand, not in reaction to it.
• Personalised Recommendations Models trained on travel history, browsing behaviour, and preference data that surface relevant destinations, upgrades, and ancillary products at the right moment in the booking journey.
• Review & Sentiment Intelligence LLM pipelines that aggregate and analyse guest reviews across platforms – structured output by property, route, or experience type that feeds directly into product and operations decisions.

LOGISTICS & TRANSPORTATION

Logistics runs on documents, deadlines, and data that's rarely clean – exactly the conditions where AI delivers the fastest, most measurable ROI.

• Document Intelligence LLM pipelines that process customs forms, invoices, bills of lading, and freight documentation in seconds – structured output integrated with your TMS or ERP, not a separate tool your ops team has to check.
• Route Optimization AI that calculates optimal routes in real time based on live traffic, weather, vehicle capacity, and delivery time windows – replacing static rule sets that haven't been updated in years.
• Demand Forecasting Shipment volume forecasting by lane, customer, and season that lets capacity planning run ahead of demand – integrated with your existing TMS and procurement workflows.
• Delivery Delay Prediction ML models that flag at-risk shipments before delays happen — based on route conditions, carrier performance history, and weather data – so you can act before the customer calls.
• Supply Chain Anomaly Detection Real-time monitoring across supplier, carrier, and inventory data that surfaces disruption risk before it hits operations – structured alerts, not another dashboard to monitor manually.
• Customer Communication Automation AI agents that handle shipment status queries, exception notifications, and delivery confirmations across email, chat, and SMS – without manual dispatch or additional customer service headcount.

CONSTRUCTION MATERIALS

Construction materials businesses sit on more procurement, pricing, and supply chain data than any team can manually process – and most of it has never been used to make a single decision.

• Demand Forecasting ML models that predict material demand by project pipeline, region, and seasonality – so procurement runs ahead of shortages instead of reacting to them after margins are already hit.
• Procurement Intelligence AI that monitors supplier pricing, lead times, and risk signals across your supply base – and flags anomalies before they affect cost or delivery commitments to your customers.
• Technical Documentation Generation LLM pipelines that generate product spec sheets, safety data sheets, and compliance documentation from raw product data – at scale, in multiple languages, without manual writing.
• Inventory Optimization Forecasting models that reduce overstock and stockouts at depot and distribution centre level – integrated with your ERP and order management systems, not a standalone planning tool.
• Order Processing Automation LLM-based pipelines that extract and validate data from purchase orders, delivery notes, and invoices – reducing manual entry errors and processing time per transaction.
• Warranty & Claims Processing LLM pipelines that classify, route, and partially resolve warranty claims based on product data and claim history – reducing manual handling time per case.

Tell us what you're building. We'll take it from there.

Tell us where you are – we'll help you work out the next step.

Customer DevelopmentAleksandra Krzyżakbusiness@boldare.com

CHOOSE YOUR PATH

Three ways to get AI into your product

Start where it makes sense for your stage – validate the idea, ship the feature, or scale what's already working.

ASSESS

AI opportunity sprint

In one week, you'll know whether your AI idea is worth building – and what it will take to build it. We run a technical feasibility check, map the integration points with your existing product, and deliver a working proof of concept with an ROI estimate your stakeholders can act on. No slide decks. No discovery that turns into a second discovery. A working POC and a decision-ready output.

1 week → feasibility + integration map | 2 weeks → full POCFixed-price with credit – sprint fee applies toward BUILD if you continue. Pre-paid retainer – for recurring discovery sprints across your product areas.

What you get:

1
Technical feasibility assessment
A clear answer on whether your AI idea is buildable on your current stack – and what would need to change if it isn't.
2
Proof of concept
A working prototype that demonstrates the core AI behaviour in the context of your product – not a demo built in isolation.
3
Integration map
A documented overview of how the AI feature connects to your existing systems, APIs, and data sources.
4
ROI estimate
A business case your stakeholders can evaluate – projected impact, implementation cost, and recommended next step.

Start with a Sprint →

BUILD

AI product development

Our AI product development process covers LLM integration, RAG systems, AI agents, or a full MVP – from brief to production. Strategy, UX, and engineering in one team.

Scoped AI feature: 4–8 weeksAI MVP from scratch: 3–6 monthsFixed priceMilestone-based fixed priceT&M

What you get:

1
Scoped feature or MVP
Working software in production – not a prototype handed off to another team to finish.
2
LLM integration / custom model
The right AI approach for your use case – RAG, fine-tuning, LLM orchestration, or agent development – selected and implemented by the same team.
3
UX for AI features
Interaction design that makes AI output trustworthy and usable – not just technically functional.
4
QA & production readiness
Tested, documented, and deployed. Ready for real users from day one.
5
Handoff documentation
Your team can maintain, extend, and iterate on everything we build – without us in the room.

Tell us what you're building →

SCALE

AI Platform Development

Your AI feature works in production. Now it needs to handle more users, more data, and more use cases – without breaking, drifting, or becoming a maintenance burden your team can't manage. We build the infrastructure, monitoring, and governance layer around what you've already shipped.

4–12+ monthsMilestone-based fixed priceFixed-scope phases + rolling retainerT&M

What you get:

1
Scalable AI infrastructure
Cloud-native architecture that handles production load – designed for the data volumes and latency requirements of a platform, not a feature.
2
Model monitoring & evaluation
Continuous tracking of model performance, output quality, and drift – so your AI keeps working as your data and user behaviour evolve.
3
Multi-model orchestration
Architecture that coordinates multiple AI models and agents across your product – without coupling that makes every change a risk.
4
EU AI Act & DSGVO compliance layer
Data residency, audit trails, and governance documentation built into the platform – not retrofitted after a compliance review flags the gap.
5
Ongoing development retainer
A dedicated team that continues to build, improve, and extend the platform as your product evolves.

Ask AI what your AI platform needs to scale →

LLM integration in production – what it actually looks like

From a travel agency chatbot that handles hundreds of repetitive inquiries, to an AI-powered construction prototype delivered in 14 days – these are real integrations, built on real platforms, with results you can measure.

CASE STUDY

Consultants buried in repetitive inquiries. AI cleared the queue. We built a conversational chatbot for Planet Escape that handles routine travel questions – so their team could get back to planning trips, not answering the same emails.

CASE STUDY

One prompt replaces 30 minutes of CMS work. We built an MCP server that connects Claude directly to our Netlify CMS – and eliminated the entire manual publishing workflow.

CASE STUDY

14 days. One prototype. Investor-ready. We ran intensive workshops, followed by vibe coding sessions – and delivered a fully clickable, AI-powered prototype in two weeks.

Before you get in touch – the questions worth answering first

If something's holding you back, it's probably answered here.

It depends on the scope, but most clients have a working proof of concept within one week – that's what the AI Opportunity Sprint is for. A full feature in production typically takes 4–12 weeks. We scope this precisely before any commitment.

AI-enhanced software development solutions: blog corner

AI is everywhere, but what’s it really like on the frontlines of AI implementation? Get an inside look at the daily thoughts and challenges faced by Boldare AI engineers – the real stuff that happens when AI meets actual digital products.

BLOG

Rebuilding isn't the default answer. Here's how to retrofit AI into an existing product - and when it actually makes sense to rebuild.

BLOG

A confident AI answer and a wrong one look identical. This guide breaks down the interface work behind trustworthy AI products: showing where an answer came from, signaling confidence honestly, and placing review checkpoints where a mistake actually costs something.

ARTICLE

Product design in the age of AI: What needs to change in 2026

BLOG

What to decide before building an AI MVP - from data readiness and model choice to fallback behavior and kill criteria.

Trusted by product teams across industries

Here's what clients say about working with us.

Norbert Baumann

VP R&D – Sonnen

"They treat the customer portal as their product and this resulted in the high quality of their work."

Head of Software Development

Prisma

"They had a very short ramp-up time and were dedicated to delivering."

Zvonko Grujic

Director Digital Engineering – Maxeon Solar Technologies

"I feel that my opinions and observations matter and that the team will adjust their actions based on our feedback."

Jerome Defillon

Chief Technology Officer – Novolyze

"We were impressed with their capacity to embrace an unknown domain and challenge the strong assumptions presented."

Fabio Zecchini

Chief Technology Officer – Musement TUI Group

Boldare delivers results that meet our standards and expectations."

Christian Jennewein

Head of Engineering – BlaBlaCar

"Their customer-focused, Agile approach inspired us, and we discovered that we shared a similar mindset."

Allan Wilson

President - Team Alert

"I was really impressed with how much they cared about our product."

Real-world AI insights from our engineers

In the APBC Tech Series, we share hands-on experience from real software delivery. These videos go beyond theory and demos, showing how we build, scale, and maintain digital products — and how AI supports everyday engineering work in real-world development workflows.

Streamlining development after 200+ sprints. The leap to full AI automation https://www.youtube.com/embed/eXalt27JBWU?si=tCyTg3iaYugeO0gk

Beyond the hype: real-world AI development workflows https://www.youtube.com/embed/Ok6UxSY75R4?si=RfyCi9__YO_vuCKM

How to use MCP and AI to speed up testing in your digital product?https://www.youtube.com/embed/4ZF-T4NhqD0?si=aqqTGUj9f-cuKmVc

Is AI ready for real software development?https://www.youtube.com/embed/KSQ-3jGzlE0?si=gnR_jDQPEu9fEpUp

How to AI-Augment Your Dev Team with Cursor AI IDE?https://www.youtube.com/embed/ftuoBJyETqU?si=jdhmRLBM1c01p5gn

Tell us what you're building. We'll take it from there.

Tell us where you are – we'll help you work out the next step.

Customer DevelopmentAleksandra Krzyżakbusiness@boldare.com

Join our Team

Get in touch

Digital product creators & consultants

Services

Gliwice

Zwycięstwa 52 44-100 Gliwice Poland

Warsaw

Krucza 50 00-025 Warsaw Poland

Wroclaw

Wyspa Słodowa 7 50-266 Wroclaw Poland

Cracow

Kurniki 9 31-156 Cracow Poland

Boldare S.A. z siedzibą w Gliwicach, przy ul. Zwycięstwa 52, zarejestrowana w Sądzie Rejonowym w Gliwicach, X Wydział Gospodarczy Krajowego Rejestru Sądowego pod nr KRS 0000914518, NIP 6312698829, REGON 38958555. Wysokość kapitału zakładowego i wpłaconego 100 000,00 zł.

AI product development. Built, tested, live

Who has benefited from Boldare's expertise?

AI in production – across industries

Tell us what you're building. We'll take it from there.

Three ways to get AI into your product

AI opportunity sprint

What you get:

AI product development

What you get:

AI Platform Development

What you get:

LLM integration in production – what it actually looks like

Before you get in touch – the questions worth answering first

How long does it take to go from idea to a working AI feature?

Do we need to rebuild our platform to add AI?

How do you make sure the AI output is reliable enough for production?

What happens if the AI feature doesn't perform as expected after launch?

How do you handle data privacy and EU AI Act compliance?

What's the difference between starting with an AI Opportunity Sprint and going straight to a build?

How long does it take to go from idea to working feature?

AI-enhanced software development solutions: blog corner

Trusted by product teams across industries

Norbert Baumann

Head of Software Development

Zvonko Grujic

Jerome Defillon

Fabio Zecchini

Christian Jennewein

Allan Wilson

Real-world AI insights from our engineers

Tell us what you're building. We'll take it from there.

Services

Gliwice

Warsaw

Wroclaw

Cracow