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b.telligent

Munich, Germany

250+Contact for ratesFounded 2005
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About

b.telligent is a Munich-headquartered data and AI consultancy working on data platforms, machine learning and analytics for enterprises across the German-speaking region, with nine offices.

Who They Work With

Enterprises adding an AI layer onto a data platform their own team will run afterward, in manufacturing, insurance or media.

Based on 9 client engagements with a source URL · researched 20 Sept 2026

Our Take

Four of the 23 published success stories carry AI substance, and each names its client and describes the architecture: an Azure sales assistant at KUKA, grown out of a proof of concept; the Cargofleet Assistant at idem telematics, where one model decides whether a question needs data and writes the database query while a second composes the answer; and a customer-feedback classifier at CSS Insurance, handling around 60,000 submissions a year across four languages with multi-label categories and sentiment.

What none of them publishes is a business outcome—the only figures are latency, under a second to search KUKA's sales corpus and about a second per image at Nordex—so there is no accuracy, cost or time-saved number to check.

Nordex is the one to read carefully in any case: the client calls it a pilot project, the engagement ended at an MVP on Azure Machine Learning, and the Kubernetes target architecture is designed rather than built.

The weaker fit is a buyer who wants an AI specialist—the other 19 stories are data platform, warehouse migration, governance and MarTech work, and the firm states its aim is to hand the system to your team and leave, so settle early who operates it afterward.

Key strengths

  • Stack is named, not implied: GPT-4o and GPT-4o mini behind a FastAPI service at KUKA, a fine-tuned BERT model at CSS, ONNX Runtime on Azure ML managed endpoints at Nordex
  • Evaluation method is published too: an LLM-as-a-judge loop at KUKA, a reproducible benchmark at Nordex, a question catalog and internal field tests at idem telematics
  • Both sides are named: client leads Dr. Vasko Isakovic (KUKA), Stefan Tietze (Nordex) and Jens Zeller (idem telematics) beside b.telligent's Dr. Sebastian Petry and Martin Graf

Good to know

  • No AI study publishes an accuracy, cost or hours-saved figure, only latency—ask for the measured result behind the KUKA assistant and the CSS classifier before scoping your own
  • Data Science & AI is one of seven service lines—confirm the engineers who built the published assistants are on your team, not a platform group with an AI brief
  • The group contracts through separate German, Austrian, Swiss and Romanian companies with a Cluj delivery office—confirm which entity signs and where the work is staffed
Gabor Kiss

Reviewed by Gabor Kiss

Founder & Curator, AIAgencies.eu · 20 Sept 2026

Based on the agency's publicly available portfolio. Own this agency? Claim your profile to provide additional context or request a review update.


How we evaluate agencies

Notable Clients

Every engagement documented in a published case study

Documented engagements: KUKA, idem telematics, CSS Insurance, Nordex, KNF, ProSiebenSat.1, OTTO, DKV Mobility, and medi. All 9 carry a source URL on file.

KUKAAI sales assistant on Microsoft Azure: containerized front end and FastAPI back end, GPT-4o for domain answers with GPT-4o mini for summarization, dynamic upload for live content updates and LLM-as-a-judge quality assurance; 3-6 months, delivered as a proof of concept then scaledSource
idem telematicsCargofleet Assistant, an LLM agent built into the Cargofleet 3 telematics platform on the firm's own LLM Code Foundation: a first model decides whether a query needs data and generates the database call, a second composes the answer; live and historical data added as separate milestones, evaluated with a question catalog and field testsSource
CSS InsuranceAutomated categorization of around 60,000 customer feedbacks a year in four languages: BERT fine-tuned on the insurer's manually labeled history, multi-label with sentiment per category; validation surfaced inconsistencies in the manual labeling it replaced. 7-12 months, no accuracy figure publishedSource
NordexVisual quality inspection in nacelle assembly: benchmarking and ONNX optimization of an existing model, an MVP on Azure Machine Learning at about one second per image, plus an MLOps and drift-monitoring framework and an Azure Kubernetes target architecture. The client describes it as a pilot project and a foundation for next stepsSource
KNFIoT cloud platform for worldwide pump lifetime testing on Azure IoT Edge, Data Explorer, Digital Twins and Managed Grafana, up to 10 data-acquisition devices per test unit read in near real time. Data engineering, not AI deliverySource
ProSiebenSat.1Unified Data Lake on Databricks; the VP Data Platforms is quoted on the integrated team and the product teams now using itSource
OTTOMigration of the existing marketing planning platform to Google Cloud, per the named testimonial from OTTO's senior business designerSource
DKV MobilityData strategy and governance work on Snowflake, from corporate purpose through data vision to implementation, per the named testimonial from the business unit lead for Data & IntelligenceSource
mediMigration from a Microsoft on-premises platform to Azure, per the named testimonial from medi's head of analyticsSource

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