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Data Pipelines & Analytics

Pipelines that turn raw data into decisions

We build data pipelines that ingest, classify, and analyze at scale. LLM-assisted classification, vector-based similarity search, structured analytics, and dashboards your team actually uses.

Overview

What we deliver

Enterprise data pipelines no longer need to be hand-coded ETL jobs. We build hybrid pipelines that combine traditional data engineering with LLM-assisted classification, semantic search, and AI-generated insights.

Why choose this service

Key benefits

01

AI-assisted classification

LLMs classify, categorize, and score records that rule-based systems miss.

02

Semantic search built in

Vector embeddings so your data is queryable by meaning, not just exact match.

03

Analytics and reporting

Dashboards, scheduled reports, and PDF/Excel exports integrated with your existing BI tools.

04

Scales with your data

Async processing, queues, and cloud-native architecture that grows from 10k to 10M records without rewrites.

How we work

Our process

01

Data Source Audit

Where does data live, what formats, and what outputs does the team need?

02

Pipeline Architecture

ETL design, storage, classification strategy, and reporting surfaces.

03

Build & Validate

Ship the pipeline. Test on your real data. Measure classification accuracy.

04

Dashboards & Automation

Reports, scheduled runs, alerts, and downstream integrations.

Applications

Common use cases

Procurement and spend analysis with duplicate detection
Product catalog classification and taxonomy generation
Multi-organization reporting platforms
Anomaly detection in financial or operational data
Content classification, tagging, and discoverability

Technologies

Tools we use

Python / Pandas
FastAPI
Postgres / Supabase
Qdrant / Pinecone
OpenAI / Gemini
AWS S3 / DynamoDB
Trigger.dev
Recharts / Plotly

FAQ

Common questions

Do you use traditional ETL or LLMs for classification?

Both. Rules handle the deterministic part, LLMs handle fuzzy classification and edge cases.

How do you handle large datasets?

Async workers, queues, checkpointing, and incremental processing. We've shipped pipelines for millions of records.

How can we help you?

Tell us about your product. We'll tell you how we'd build it, and how fast.

Let's Work Together →