Tackling complex
challenges

Where enterprise expertise meets modern technology. We build digital solutions that deliver real ROI.

who we are

We are Sciscry /saɪ skraɪ/

A software studio combining deep enterprise expertise with modern data platforms and AI. We engage deeply with your challenges — whether it's a decade-old SAP landscape, a complex data pipeline, or an AI assistant your team actually uses.

We are not generalists. We go deep in our domains and deliver working software that drives ROI.

Deep Expertise

Domain specialists in SAP, data engineering, AI, and environmental data.

Modern Stack

Cloud-native tools, LLMs, and platforms that scale with your business.

Real ROI

We measure success in business outcomes — not lines of code or slide decks.

what we do

Our Services

We specialise in four high-complexity domains where experience and precision translate directly into competitive advantage.

Enterprise Planning

End-to-end SAP expertise across the full analytics stack. We handle migrations and modernization: from BW and BW/4HANA to S/4HANA Embedded Analytics, SAP Datasphere, and SAC. Strategy, implementation, and beyond.

ABAP BTP CDS Views OData BW/4HANA S/4HANA Datasphere SAC

Data Engineering

Scalable pipelines and cloud-native data platforms built for operational complexity. From ingestion to orchestration to reporting — across retail, logistics, and regulated industries where reliability is non-negotiable.

Python AWS Redshift PostgreSQL MongoDB Terraform Ansible PowerBI Serverless

AI & Intelligent Agents

From document intelligence and RAG pipelines to autonomous agentic workflows — we build AI systems grounded in your data that your teams actually adopt. Including computer vision and deep learning for real-time monitoring use cases.

AWS Bedrock AWS SageMaker Azure AI Foundry LLMs RAG Computer Vision Power Automate

Weather & Remote Sensing

Integrating NWP model outputs, satellite imagery, and real-time sensor data into operational systems. We build the infrastructure to turn environmental data into decision-ready signals for logistics, agriculture, energy, and beyond.

Python AWS Lambda AWS S3 Serverless PostgreSQL Azure Functions

our stack

SAP ABAPBTPCDS ViewsODataBW/4HANAS/4HANADatasphereSAC
Cloud AWS BedrockAWS SageMakerAWS LambdaAWS S3AWS RedshiftAzure FunctionsAzure AI FoundryBigQueryBigQuery MLLookerServerlessTerraformAnsible
Data & AI PythonPostgreSQLMongoDBPowerBIPower AutomateComputer VisionDeep LearningLLMsRAGClaude AgentsMCP Servers
Edge Raspberry PiIntel RealSenseOpenCVONNX RuntimeTensorFlow LiteLinuxembedded C++
in practice

Use Cases

A sample of the complex problems we've solved for real organizations.

Fashion Retail

Financial Planning Platform for a Global Fashion Group

Implemented a full SAP-based financial planning suite for a major international fashion retailer, covering balance sheet reporting, P&L analysis, assortment planning, and merchandise management. Unified multi-country financial data into a single SAC-driven planning layer for controlling and buying teams worldwide.

Group-level visibility across markets · self-service planning enabled
S/4HANAEmbedded BWABAPOData
SAP Financial Planning Architecture
Food Retail

Integrated Corporate Planning for a Large Food Retail Group

Designed and extended an SAP-driven planning ecosystem for a large European food retail corporation spanning thousands of locations. Delivered solutions across financial controlling, real-estate portfolio management, and logistics coordination — enabling consistent group-level insight at enterprise scale.

Multi-domain integration across finance, real estate & logistics
BW/4HANACDS ViewsDatasphereSAC
Integrated Corporate Planning Architecture
Enterprise AI

Company Knowledge Assistant with Deep PDF Ingestion

Built an LLM-powered knowledge assistant for an organization with a large corpus of internal documentation — technical reports, scientific literature, procedural PDFs, and structured datasets. The system ingests, chunks, and indexes documents automatically, then answers natural-language queries with cited sources. Staff went from manually searching hundreds of PDFs to getting precise answers in seconds.

Hundreds of PDFs made queryable · cited answers in seconds
LLMsRAGPDF IngestionPythonLocal LLMsopen-webui
Agentic Knowledge Assistant Architecture
Logistics

Operations Planning Optimization for a Relocation Services Company

Built a data-driven planning optimization engine for a moving and relocation services company. Modeled resource allocation, vehicle routing, and crew scheduling to replace manual planning processes — improving operational predictability and significantly reducing planning overhead.

Automated planning workflows · reduced operational overhead
PythonPostgreSQLAWSTerraform
Route Optimization and Resource Allocation
Developer Tooling

MCP Server for SAP Datasphere

We built and actively use an MCP (Model Context Protocol) server that exposes SAP Datasphere APIs to Claude-powered agents. During development, this lets us interact with Datasphere entities, run queries, and inspect metadata through natural language — dramatically reducing the back-and-forth between documentation and implementation. The result is faster delivery cycles and fewer integration errors on SAP projects.

Faster delivery on SAP projects · agent-driven development workflow
MCPClaude AgentsSAP DataspherePythonOData
MCP Server SAP Datasphere Integration
Edge AI

Real-Time Computer Vision on Embedded Hardware

Designed and deployed an end-to-end computer vision system running on constrained edge hardware — Raspberry Pi with Intel RealSense depth cameras. The system performs real-time object detection, behavioral analysis, and anomaly flagging entirely on-device, with no cloud dependency for inference. Demonstrates that production-grade deep learning isn't limited to the cloud: we can bring AI to the physical environment, directly where the data is generated.

On-device inference on Raspberry Pi + RealSense · zero cloud latency
Computer VisionDeep LearningRaspberry PiIntel RealSenseopencvEdge AI
Edge AI On-Device Inference Pipeline
the people

Our Team

Two founders, complementary domains, one shared obsession: deliver added value for the customer.

Philipp Beer

Philipp Beer

Founder & CEO

MSc in Data Science, entrepreneur, and former management consultant. Philipp combines a rare depth of business domain knowledge with genuine technical understanding — making him the person clients talk to when the problem is hard to define, not just hard to solve. He leads customer relationships at Sciscry, bridging the gap between what a business needs and what technology can deliver.

Piero Ferrarese

Piero Ferrarese

Co-Founder, Data & Technology

Ph.D. in Physics and MSc in Meteorology. Piero is the kind of engineer who doesn't accept "it can't be done" — an analytical thinker who works through complexity until a solution emerges. He leads the technical side of Sciscry: AI system design, LLM-powered agents, data platform architecture, edge computing, and anything else that requires first-principles thinking and a bias for getting it built.

who trusts us

Trusted Across Industries

We work with companies where the stakes are high and complexity is the norm.

Fashion Retail
Food Retail
Logistics
Enterprise AI
Edge AI
Sciscry delivered what others said was too complex. On time, on budget, and it actually works.

— Operations Director, Manufacturing Group

let's talk

Facing a complex challenge?

We'd like to hear about it.

info@sciscry.ai

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