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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.

01Overview

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.

02Why 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.

03How 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.

04Applications

Common use cases

01Procurement and spend analysis with duplicate detection
02Product catalog classification and taxonomy generation
03Multi-organization reporting platforms
04Anomaly detection in financial or operational data
05Content classification, tagging, and discoverability

05Technologies

Tools we use

Python / PandasFastAPIPostgres / SupabaseQdrant / PineconeOpenAI / GeminiAWS S3 / DynamoDBTrigger.devRecharts / Plotly

06FAQ

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.

Trusted in production by

Maxiom
MindTime
Franck Muller
Backyard Design
TrueClaim
Bank Marketing Center
True Terpenes
Restaurant SupplyRestaurant Supply
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How can we help you?

We'll tell you how we'd build it, and how fast.

Book a call