Data Science

Analytics, machine learning, and data pipelines that turn raw data into decisions.

Data science is about getting answers from the data your business already collects: sales records, sensor readings, support tickets, market feeds. We build the pipelines that clean and combine that data, the models that find patterns in it, and the dashboards that put the results in front of the people who make decisions.

You need this when reporting takes days of manual spreadsheet work, when you suspect there is a signal in your data but nobody has time to look, or when a product feature depends on a prediction, a classification, or a recommendation.

We have done this for our own products. Pairenthesis cleans and structures messy datasets through a conversational interface, and Trading Town turns financial data into insider-insight analytics. Your project gets the same engineers, working in Python with PostgreSQL, MongoDB, and the cloud data tools that fit your scale.

Discuss Your ProjectRead the FAQ

What's Included

  • Data pipelines and ETL

    Automated jobs that pull data from databases, APIs, and files, clean it, and load it into a warehouse or analytics database on a schedule.

  • Exploratory analysis

    A structured first look at your data to find quality problems, obvious patterns, and the questions worth a deeper investigation.

  • Machine learning models

    Forecasting, classification, anomaly detection, and recommendation models trained on your data and deployed as services your software can call.

  • Dashboards and reporting

    Interactive dashboards and scheduled reports that replace manual spreadsheet work and show the metrics your team actually tracks.

  • LLM and AI integrations

    Document extraction, search, summarization, and conversational interfaces built on large language models, with guardrails and evaluation.

  • Data model and warehouse design

    Schemas and storage choices that keep queries fast and costs down as data volume grows.

How a Data Science Engagement Works

  1. 01

    Question framing

    We agree on the specific decisions the work should support, which keeps the project focused on outcomes rather than on interesting detours.

  2. 02

    Data audit and pipeline

    We assess data quality, fill the gaps we can, and build the pipeline that delivers clean, reliable data for analysis.

  3. 03

    Model and dashboard build

    Analysis, models, and visualizations are developed iteratively, with results shared early so you can challenge assumptions.

  4. 04

    Deploy and measure

    Models and dashboards go into production with monitoring, and we check real-world accuracy against the baseline we agreed on.

Who This Is For

  • Businesses drowning in spreadsheets and manual reports
  • Fintech and trading teams working with market and filings data
  • Product teams adding predictions or recommendations to their app
  • Operations leaders who want forecasting instead of guesswork
  • Companies with years of data and no analysis of it

Tools and Technologies

  • Python
  • pandas
  • scikit-learn
  • Jupyter
  • Apache Airflow
  • PostgreSQL
  • MongoDB
  • Metabase
  • AWS
  • Google Cloud

Related Work

Trading Town logo

Data Science · Financial services

Trading Town

Trading Town turns SEC Form 4 insider filings and congressional trading disclosures into ranked, searchable data with interactive charts, daily reports, and an API for investors who want evidence behind each trade.

Read case studyVisit site
Pairenthesis logo

Website Development · Data and analytics

Pairenthesis

Pairenthesis cleans and profiles messy data, answers plain-language questions with figures computed in code, and forecasts outcomes, with every step written to an audit ledger. No code required.

Read case studyVisit site

Data Science FAQ

Do we need a lot of data to benefit from data science?
Less than most people assume. Useful analysis often starts with a few thousand well-structured records, and many business questions are answered with careful descriptive statistics before any machine learning is involved. What matters more is data quality and whether the question is specific. If your data is thin, we will say so and suggest what to collect first.
What does a data science project cost and how long does it take?
A scoped project, such as an exploratory analysis or a single dashboard, is quoted as a fixed price after a short data audit. Larger efforts involving pipelines, models, and ongoing tuning run on time-and-materials with monthly reviews. Timelines depend heavily on the state of your data, so we estimate only after we have seen it.
How does a typical engagement run?
We begin by framing the decisions the work should support, then audit your data and build the pipeline that feeds it. Analysis and models are developed in short iterations with results shared as we go. The final step deploys dashboards or models to production with monitoring. You can continue with a support package or take over internally.
Can you build AI features using large language models?
Yes. We build document extraction, semantic search, summarization, and conversational interfaces on top of commercial and open models, and our own product Pairenthesis is built around a conversational interface for data work. Every LLM feature we deliver includes evaluation against real examples, cost controls, and fallbacks, because a demo that works once is not the same as a feature that works reliably.
Will our data leave our systems?
Only if you want it to. We can work entirely inside your cloud account or on-premises environment, with access controlled by your team. When a project needs external services, such as a hosted model API, we document exactly what is sent, anonymize where possible, and get your sign-off before anything is connected.

Related Services

  • Cloud Services

    Scalable cloud infrastructure and migration services to enhance performance, security, and cost-efficiency.

  • Website Development

    Custom websites and web applications built with modern technologies to deliver exceptional user experiences and business results.

  • AI & Automation

    Language model features, intelligent document processing, and workflow automation that remove manual work from your business.

Need Data Science?

Send us a short description of what you are trying to achieve. We reply within 24 hours with questions, a rough estimate, or both.

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