Do you train custom machine-learning models?

No. We build on foundation models and modern data tooling — pipelines, LLM-based extraction, retrieval, and analytics. If you need custom statistical modeling, we'll say so and help you scope it.

What's the difference between your Data Science and AI & ML services?

Data Science is the data layer: getting data clean, connected, and ready. AI & ML is what you build on top — RAG assistants, agents, LLM features. Most AI projects need the data work first.

Can you make our data AI-ready?

Yes, that's the core of this service: ingestion, cleaning, structure, and retrieval, so an AI feature has something reliable to work with.

Do you build dashboards and BI?

Yes. For deep BI we bring in our Power BI practice; the pipeline underneath is what keeps the dashboard current.

Which databases and warehouses do you work with?

PostgreSQL/pgvector by default, cloud warehouses like BigQuery and Snowflake, and your existing databases. The choice depends on your stack and data volume.

How do you handle data security and compliance?

Delivery on Azure or AWS with GDPR-aligned contracts; access runs through your existing permissions and is logged. Requirements are agreed before the build starts.

Data Engineering & Data Science Services

Data engineering and applied data science — pipelines, quality, retrieval, and dashboards

Data engineering that gets your data clean, connected, and ready to use — for dashboards, for decisions, and for AI. A dedicated senior team, honest about which data work actually moves the needle.

Problems We Solve

Most data problems start before any model: the data is scattered, messy, or locked where no one can use it. The pains we get called in for:

Your data lives in five systems and none agree
- Numbers differ depending on which tool you ask, and there's no single source of truth. We integrate the sources, reconcile them, and give you one consistent view.
The data isn't ready for the analysis you want
- Reports take days because someone cleans and reshapes spreadsheets by hand every time. We build pipelines that ingest, clean, and structure the data once, on a schedule.
The answers are locked in documents and PDFs
- Contracts, reports, and forms hold the information, but nothing can query it. We extract and structure that content so it becomes searchable and analyzable.

You want AI, but your data isn't ready for it
- An AI project stalls because the data is incomplete, inconsistent, or unstructured. We handle the data-readiness work — quality, structure, and retrieval — that the AI depends on.
Dashboards exist, but no one trusts them
- Numbers look wrong or out of date, so teams go back to spreadsheets. We fix the pipeline underneath and rebuild reporting people rely on.
Reporting eats a day every week
- Someone rebuilds the same report by hand on a schedule. We automate the collection and refresh so the report updates itself.

What We Do

The data work that makes decisions and AI possible.

Data Pipelines & Integration
- Connect your sources and move data between them reliably, with checks so nothing silently breaks.
Data Cleaning, Quality & Validation
- Deduplicate, validate, and standardize so the data can be trusted downstream.
Documents & Unstructured Data to Structured
- Turn PDFs, forms, and text into structured, queryable data with LLM-based extraction.
Search & Retrieval Over Your Data
- Vector and hybrid search so people and AI can find the right record or passage.
Analytics & BI Dashboards
- Reporting people actually use, built on a pipeline that stays current.
Data Readiness for AI
- The ingestion, structure, and retrieval an AI feature needs before it can work.

How We Work

Step 1

Assess data and goal

What decision or feature the data must support, and what shape it's in today.

Step 2

Model and integrate

Design the data model and connect the sources into reliable pipelines.

Step 3

Clean, validate, structure

Deduplicate and validate so the data can be trusted; turn documents into structured records where needed.

Step 4

Make it searchable and usable

Indexing, vector or hybrid search, and retrieval so people and AI reach the right data.

Step 5

Surface and integrate

Dashboards, APIs, or an AI feature on top, wired into your systems and permissions.

Step 6

Monitor and maintain

Checks and observability so a broken source raises a flag instead of quietly corrupting the data.

Which Service Fits

Data Science, AI & ML, and Power BI solve different layers. Where each one fits.

	Data Science (this page)	AI & ML	Power BI
Best for	Getting data clean, connected, and ready	Building AI on top of that data	Dashboards and reporting
You have	Scattered, messy, or unstructured data	Data that's ready, and a use case for AI	Data in place, need to see it
You want	Pipelines, quality, structure, retrieval	RAG assistants, agents, LLM features	Reports people trust
Starts with	A data assessment	A proof of concept	A reporting model
Where to go	This page	AI & ML service	Power BI service

Technologies We Use

A modern data stack, matched to your systems.

Languages

Python, C#/.NET, Node.js/TypeScript, SQL

Data & Storage

PostgreSQL / pgvector, cloud warehouses (BigQuery, Snowflake), your existing databases

Pipelines & Integration

n8n, ETL/ELT, APIs, scheduled jobs

LLM & Retrieval

OpenAI, Azure OpenAI / Azure AI Foundry, embeddings, vector & hybrid search

Analytics & BI

Power BI (and your existing BI tools)

Cloud & Delivery

Azure, AWS, Docker, CI/CD, OpenTelemetry

What Makes Us Different

Honest About Scope
- We tell you when it's a data-readiness job, a BI job, or genuinely an AI job.
Data Quality Built In
- Validation and checks from the first pipeline, so the numbers downstream hold up.

Built for Retrieval and AI
- The data we prepare is structured for search and LLM use from the start.
Senior, Integrated Delivery
- A dedicated team that wires data into your systems and permissions.