Building data acquisition, ML pipeline, and front-end infrastructure for an agentic retail intelligence product.

Profitmind is building an agentic retail intelligence platform that gives retailers continuous, quantified view of their competitive position across pricing, assortment, promotions, and inventory. The engineering scope is broad and technically demanding.
* The data acquisition layer requires maintaining scrapers for hundreds of retail websites, each with its own anti-bot protections, platform fingerprints, and structural quirks.
* A separate machine learning pipeline ingests that raw data, validates it, matches products across retailers, and generates the assortment and pricing intelligence that clients act on.
* A front-end platform layer translates all of it into business-ready dashboards that non-technical retail teams can use with minimal training.

As a startup competing in a fast-moving AI market, Profitmind faces a recurring engineering reality: demand ebbs and flows. Periods of rapid feature development alternate with infrastructure hardening and pipeline stabilization. Scaling a full internal team sized for peak demand wasn't viable — Profitmind needed senior engineers with specific, complementary skills in data engineering, ML infrastructure, and front-end platform development who could adapt as priorities shifted, without the delays and overhead of traditional hiring.

Admios has partnered with Profitmind since January 2023, embedding a multi-disciplinary team structured to cover each of the three core technical layers of the platform. The stack spans Python-based scraping pipelines orchestrated with Kafka and Redis, containerized workloads running on Kubernetes and AWS, and a React and TypeScript front end backed by a PostgreSQL data layer.

The engagement has evolved considerably as the platform has matured. Early in the engagement, the team scaled to six engineers during a period of intensive scraping infrastructure build-out.

More recently, all three active engineers have integrated Claude Code deeply into their daily workflows: the scraping specialist built a Claude-backed code generator that automatically produces new product scrapers; compressing what was once a significant per-source effort into a repeatable, partially automated workflow. The front-end engineer has been restructuring repositories to follow agentic-friendly conventions. The pipeline engineer is designing new microservices with AI-agent compatibility as a first-class requirement from the start; a direction that now reflects Profitmind's company-wide engineering standard.

The Admios team's contribution runs through every layer of what Profitmind sells. The results track with Profitmind's own trajectory:

The client roster has grown to include some of the most recognizable names in U.S. retail, and Profitmind earned the 2025 Retail Innovation Award from the Retail Technology Show alongside recognition from the National Retail Federation. In 2026, the company closed a $9 million Series A financing round.

Admios remains an active partner as Profitmind pushes toward an AI-agent-first engineering model. The Admios team is among the first to put that model into practice — working at the intersection of data acquisition, ML infrastructure, and front-end platform in ways that the broader Profitmind team is beginning to replicate.