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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

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.

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

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

Create AI

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

AI Enterprise Model Tuning

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

RAG (Retrieval-Augmented Generation)

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

LLM Fine-Tuning

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

Make models transparent & compliant for regulators.

Explainability & Model Audit

Automate ML from data cleaning to model deployment.

AutoML Pipelines

AI Data Analysis

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

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

GenAI

Chatbots and virtual assistants for customer support, internal HR automation, IT helpdesks, and interactive applications.

Conversational AI

AI copilots that surface institutional knowledge and support employee productivity with natural language queries.

Enterprise Knowledge Assistants

Generate voiceovers, clone voices, or build IVR systems with lifelike AI speech.

Voice & Speech Solutions

Use AI to generate realistic synthetic datasets for ML training or software testing.

Synthetic Data & Test Data Generation

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

Scientific Discovery

Augmenting traditional search by generating complete answers with citations.

Generative Search

Automatic code synthesis, documentation, testing, and debugging tools integrated into developer workflows.

Code Generation

AI-generated financial summaries, earnings statements, and investment insights.

Financial Report Generation

Real-time voice replication or translation used in global communication and localization tools.

Voice Cloning & Translation

Implement smart search with semantic understanding and retrieval from enterprise data.

AI-Powered Search & Q&A

Generative tools for UI/UX mockups, branding assets, and creative ideation in design teams.

AI for Design

Creating visuals from text prompts for design, advertising, art, and prototyping purposes.

Image Generation

Speed up software development with AI pair programming, testing, and bug fixing.

Code Assistance & Automation

Automatically create natural language summaries of dashboards or trends.

AI-Driven Reporting & Analytics Narratives

Automated generation of ad copy, SEO content, social posts, and product descriptions.

Marketing Content Generation

Interactive training environments, digital twins, and simulation for learning and operations.

AI-driven Simulation & Training

Automatically summarize contracts, reports, or emails to extract key information for decisions.

Document Summarization & Insights

Generate marketing copy, blogs, emails, or product descriptions with AI tools.

Text Generation & Content Creation

Conversational bots for customer support, sales, or internal helpdesks, automating interactions.

AI Chatbots & Virtual Assistants

Build and maintain FAQs, help centers, or policy docs with AI to reduce manual work.

Knowledge Base Automation

Create marketing banners, product visuals, or social graphics from simple prompts.

Image & Creative Asset Generation

Produce personalized explainer videos or avatar-driven content for marketing or training.

Video & Avatar Generation

Dynamic content or product recommendations based on user behavior and preferences.

Personalization Engines

Auto-generating business documents like contracts, proposals, meeting notes, legal documents, and reports.

Document Automation

Drafting legal text, flagging compliance issues, and automating contract workflows.

Legal & Compliance Assistance

Creating music, sound effects, podcasts, and ambient sounds using AI, often used in entertainment and advertising.

Audio Generation

Generating synthetic video content, including product demos, explainer videos, and virtual avatars.

Video Generation & Editing

AI-powered virtual people used in customer experience, retail, marketing, or e-learning.

Digital Humans / Avatars

Deliver tailored suggestions, web content, or email personalization to boost engagement.

Personalized Recommendations & Dynamic Content

Generating human-like voice for virtual assistants, media production, accessibility, and more.

Speech Synthesis

A highly regulated industry, today’s finance firms are highly dependent on technology. Their requirements range from highly technical and intensive computational modelling, real-time data analysis, fraud prevention, to front-end needs such as marketing and customer retention, always burdened with the highest security requirements of any private sector. 

Finance

Venture Capital

Investment banking deals primarily with the creation of capital for other companies, governments, and other entities.

Investment Banking

Asset management is a systematic approach to the governance and realization of value from the things that a group or entity is responsible for, over their whole life cycles. It may apply both to tangible assets and to intangible assets.

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March Capital Partners

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