datarabbit

Datarabbit is a Poland-based AI and data engineering consultancy that works with organizations to design, build, and deploy data-driven and machine learning solutions. The company focuses on translating raw data into actionable business insights and operationalizing AI models at scale. They work across healthcare, fintech, retail, and nonprofits, with particular depth in regulated environments where compliance and data governance matter.

The team has hands-on experience across the full data and AI stack: from architecture and platform selection through MLOps, LLMOps, and production deployment. They partner with AWS (and work with Azure and GCP) and list Adam Mickiewicz University and TD SYNNEX among their partners.

Services and capabilities

Datarabbit organizes its work around eight core areas. AI/ML covers text extraction from images, sentiment analysis, predictive maintenance, and chatbot deployment. Generative AI and LLMs includes advisory on when and how to apply large language models safely, including risk assessment and practical implementation. The company has worked with clients to migrate from legacy NLP approaches to LLM-based solutions where it makes sense, and to reject LLMs where they add complexity without value.

Big data solutions includes platform selection, design, and implementation for storage and processing at scale. Advanced data analysis transforms raw datasets into performance dashboards and visualization systems for continuous insight extraction. MLOps/LLMOps helps teams automate model versioning, experiment tracking, evaluation, and promotion from lab to production—critical for teams shipping models at speed.

Cloud solutions covers migrations, cost optimization, and expansion on AWS, Azure, and GCP. The company explicitly mentions helping enterprises bridge the gap between wanting to move to the cloud and lacking in-house expertise. Chatbots are positioned as conversational agents for knowledge bases, appointment scheduling, and customer support. Software consulting rounds out the offering, particularly for DevOps and cases where data could unlock future value.

Notable work

Datarabbit's case studies reveal specific, complex engagements in healthcare and adjacent domains. For Carta Healthcare, a clinical data management platform, datarabbit conducted an end-to-end evaluation of their AI stack in Q4 2023. Carta was running legacy NLP pipelines and needed clarity on whether and how to adopt LLMs while staying HIPAA-compliant. Datarabbit delivered a Technical Analysis and Recommendations document, defined MLOps guardrails for model versioning and promotion, and architected a data layer that kept PHI within approved controls. The outcome: Carta revamped its entire AI approach, retired brittle subsystems, improved maintainability and shipping cadence, and saw visibly better performance metrics on generated outputs. Andrew Crowder, VP Engineering, noted they were impressed by "their ability to quickly research and answer questions at a level that was easy to understand — and their honesty about what AI could and couldn't handle."

For Hazel Health, a school-based telehealth provider serving 5 million K-12 students across 18 states, datarabbit executed a cloud migration from Heroku to AWS under a tight deadline. The company faced a $250k–$500k Heroku renewal and needed to move before that contract came due. Datarabbit built a Kubernetes-based architecture on Amazon EKS using Terraform and Terragrunt for infrastructure-as-code, and re-engineered CI/CD pipelines with GitHub Actions and FluxCD. The result: Hazel avoided the renewal cost (50–75% savings on projected run rates), automated deployments that previously required manual oversight, and freed its platform team to focus on patient-facing features instead of infrastructure firefighting. Ben Mehling, Hazel's CTO, said the team "performed exceptionally well" across staffing gap, migration acceleration, and architecture pressure-testing.

Other projects mentioned include Ensuring Safety at Scale for a global charity (manual content review automated with AI), Enhancing Meeting Efficiency with AI-Powered Meeting Summarization, MRI analysis toolset development for medical research, OCR-based invoice processing for accounting, MLOps for genetic AI advancement, and intelligent chatbots for retail customer support.

How they work

Datarabbit engages on both advisory and implementation tracks. The Carta Healthcare case shows the advisory model: rapid evaluation, deliverables (Technical Analysis, architecture diagrams, prioritized mitigations), and extended engagement through rollout. The Hazel Health project shows the implementation model: datarabbit brought engineering capacity to execute a multi-month cloud migration in parallel with the client's internal team, acting as both hands-on builders and expert consultants. The source does not specify day rates, project minimums, or formal SLAs, but the company invites prospects to book discovery calls to discuss goals and map a plan.

Team and credentials

The source does not name founders, specify total headcount, or list certifications (ISO 27001, SOC 2, GDPR). However, the case studies reference engagements where HIPAA and GDPR compliance were enforced by design, and Hazel's engagement mentions "over 20 successful healthcare technology engagements" executed with compliance-first precision across HIPAA/GDPR, ISO, and FDA frameworks. AWS is listed as a partner. The company actively hires and posts open roles; careers language suggests they seek "enthusiasts of creating data-based solutions."