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How we work
Assess, build and run, with one accountable team before and after go-live.How we work 2 weeksfrom scattered ideas to a ranked, delivery-ready plan.The AI Assessment Data scientists and engineers shipping to production, then maintaining it.Data and AI engineering Your data stays yours, governed from the first day.Trust and governance
Featured stories
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4.2 hto collect 2,800 financial reports with fifty agents in parallel, down from 210 hoursRead the story 35%more coverage of points of sale for Heineken field teamsRead the story 50k+cable designs tested virtually for CoficabRead the story 800K+calls a month transcribed, summarised and classified at NOSRead the story
Resources
Information Hygiene: The Security Discipline That Matters More Than EverRead the blog Artificial Intelligence Made in Portugal: ‘We grew more than 60% in one of the most competitive markets in the world,’ says DareData Partner.All news
Company
2019Founded in Lisbon by engineers. Today we build and run AI for large companies across Europe.About DareData 130+engineers, data scientists and principals in Lisbon, Porto and LondonMeet the team We are hiring engineers who want to build AI that runs.See open roles 50+customers served. Tell us about yours.Talk to us

SolutionsIndustries

Consumer & Retail

Brands, stores and shopping centres with distributed teams and demanding customers.

See the storiesTalk to us

We help consumer and retail businesses get ahead of demand, understand customers better and remove friction between product and purchase.

  1. 01

    Supply chain efficiency

    Our AI workflows optimize supply chain operations, ensuring timely delivery and reducing waste.

  2. 02

    Consumer insights

    Data platforms that consolidate and analyze behavioral, transactional, and market data to have a clearer picture of what customers want, when, and why.

  3. 03

    Product development

    Generative AI solutions that analyze consumer feedback, market signals, and internal sales data to surface consumer preferences and market needs.

AI in production, story by story.

Pick one to see the result, the client behind it and the full story.

All success stories
Use Case · Delivery Route Optimisation€100kin fuel savingsHeineken field teams gain 35% more coverage of points of saleWe built a routing engine to optimise daily vehicle routes, reduce travel time, and boost operational efficiencyFull success story Use Case · Workforce Knowledge Assistant150kmessages exchangedSonae Sierra gives 550 employees one governed AI workspaceOne governed workspace that gives thousands of employees cited answers from company documentation and specialist agents, without per‑user licensing.Full success story 94%customer correspondence accuracySogrape turns distributor emails into SAP orders, matching customers with 94% accuracyDistributor order emails turned into SAP ordersFull success story 70%accuracy in detecting bluffA poker model detects bluffs from eye behaviour with 70% accuracyUsing Machine Learning to create better Poker PlayersUndisclosed ClientFull success story >10000foosball games coveredChutAI brings live AI analytics to more than 10000 foosball gamesReal‑time computer vision and live commentary on a foosball tableUndisclosed ClientFull success story
€100kin fuel savings
Consumer & Retail · OptimisationHeineken field teams gain 35% more coverage of points of saleRead the story
150kmessages exchanged
Consumer & Retail · AI assistantsSonae Sierra gives 550 employees one governed AI workspaceRead the story
94%customer correspondence accuracy
Consumer & Retail · AutomationSogrape turns distributor emails into SAP orders, matching customers with 94% accuracyRead the story
70%accuracy in detecting bluff
Consumer & Retail · Computer visionA poker model detects bluffs from eye behaviour with 70% accuracyRead the story
>10000foosball games covered
Consumer & Retail · Computer visionChutAI brings live AI analytics to more than 10000 foosball gamesRead the story

Questions

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Can one assistant serve teams across many sites?

Yes. One of our assistants serves 550 active users across a distributed workforce, with cited answers from company documentation.

What happens after go-live?

The team that builds your solution runs it. We monitor quality and adoption, fix what drifts and improve the solution as your business changes.

Where does our data live?

You decide. We deploy in our cloud, in your own cloud account or on your servers, and your data stays inside that perimeter under your rules.

Do we need an AI strategy before we start?

No. If you already have a use case with an owner, workable data and internal agreement, we can build it. If not, the AI Assessment finds and ranks the opportunities first.

Can you work with our own data and IT teams?

Yes. We adapt to your stack and work side by side with your engineers. We can also plan the move from a DareData team to your own team over time.

Which AI models do you use?

We are not tied to one provider. GenOS works with models such as Claude, GPT, Gemini, Mistral and Llama, and we pick per task for accuracy, cost and where the data may go.

Who owns what we build together?

Ownership is agreed in the contract before work starts. Ask us for our standard terms on the first call.

How is a project priced?

It depends on scope, length and the team it needs. After a first conversation we send a proposal fitted to your case, not a price list.

How do you keep our data secure?

Every solution runs on GenOS, with guardrails, access rules and a log of each answer, action and model call. Your security and risk owners review the design before anything goes live.

How soon do we see results?

The AI Assessment takes two working weeks and ends with a ranked plan. Most of our builds reach production in two to six months, depending on scope and data.

Other industries

Bring us your hardest AI problem.

Large enterprises trust us to carry AI all the way into production. We shape the use case, build it into your systems and run it with the governance your business needs.

  • 01Use cases chosen for value, not hype
  • 02Systems built into your stack and data
  • 03Run, monitored and improved after go-live
Talk to usSee success stories