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Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data,[1][2] and apply knowledge and actionable insights from data across a broad range of application domains.

Data Science

Dig deep into your data and gain insight. Uncover patterns and compute stats you can trust

Data Analysis

Build graphs, KPIs and interfaces which can be used to take business decisions

Data Visualization

A data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion.

Data Pipelines

Perform Data Aggregation and/or Manipulation, usually the first steps in Data Science

Data Wrangling

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Big Data

Create Machine Learning Models from scratch, train them, and break new boundaries

Create AI

Apply an already pre-trained Machine Learning Model from IBM Watson, Google Cloud or others

Apply AI

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

AI Data Analysis

Uses natural language to query and analyze structured/unstructured data. Enables business users to extract insights without SQL or BI tools.

Conversational Analytics

Analyze networks like social graphs, supply chains, or transactions.

Graph Analytics & ML

Uses AI to clean, normalize, and transform data for analysis. Reduces manual ETL work.

Data Preparation & Cleansing

Cluster customers by behavior or value to drive marketing strategies.

Customer Segmentation ML

Recommends actions based on predictive models and optimization algorithms. Used in logistics, pricing, and planning.

Prescriptive Analytics

Use ML to spot unusual transactions or metrics before they escalate.

Anomaly & Fraud Detection

Forecasts trends and behaviors using historical data and ML models. Common in finance, marketing, and operations.

Predictive Analytics

Auto-generates summaries, dashboards, and narratives from raw data. Speeds up decision-making for non-technical users.

Automated Insights

Let teams explore data by asking questions in natural language.

Conversational Data Analysis

Predict sales, inventory, or demand trends over time with AI models.

Time Series Forecasting

AI Enterprise Model Tuning

Combines LLMs with enterprise knowledge bases for grounded, real-time responses.

RAG (Retrieval-Augmented Generation)

Optimize models for faster deployment & reduced cost.

Model Compression & Quantization

Ensure ML decisions are equitable across demographics.

Bias & Fairness Detection

Manage AI models that mix text, images, voice, and structured data.

Multi-Modal Model Management

Tracks model performance, bias, drift, and hallucinations in production. Ensures compliance and reliability.

Model Evaluation & Monitoring

Designs and tests prompts to improve model output without retraining. Often used in copilots and chatbots.

Prompt Engineering & Optimization

Generates domain-specific data to augment training sets or simulate rare scenarios.

Synthetic Data for Tuning

Customizes foundation models using enterprise-specific data for better accuracy and relevance.

LLM Fine-Tuning

Make models transparent & compliant for regulators.

Explainability & Model Audit

Automate ML from data cleaning to model deployment.

AutoML Pipelines

GenAI

Generating hypotheses, simulations, molecular designs, or models in healthcare, pharma, and materials science.

Scientific Discovery

Technology companies focus on developing software, hardware, and digital services that power modern business. This sector includes everything from product development to infrastructure and innovation. On Spryte, tech teams range from agile startups to enterprise-grade providers, all using their expertise to build the future of digital solutions.

Technology

Databases

APIs

SaaS

AI & ML

Cloud

Tools

Framework

The pharmaceutical industry discovers, develops, produces, and markets drugs or pharmaceutical drugs for use as medications to be administered to patients, with the aim to cure them, vaccinate them, or alleviate the symptoms. Pharmaceutical companies may deal in generic or brand medications and medical devices.

Healthcare

In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which new candidate medications are discovered.

Drug Discovery

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex.

Bioinformatics

Pharmaceutical industries mainly focus on developing, manufacturing, and marketing drugs or pharmaceuticals which are licensed for the use as medication.

Pharma

The pharmaceutical subsector focuses on the research, development, manufacturing, and marketing of pharmaceutical drugs and medications for various medical conditions. This includes prescription medications, overthecounter drugs, and biopharmaceutical products.

Pharmaceuticals

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