Hire a RAG Developer · Minneapolis, MN

Hire a RAG developer in Minneapolis.

A customer service agent at a Minneapolis retailer earns $35 per hour and handles 15 queries per hour. 500 spec-sheet queries per day at 4 minutes each is 33 hours of daily search time. That is $1,155 per day spent retrieving product information that already exists in a document library.

Target Corporation runs 1,900 stores and Best Buy runs 1,000+. Both are headquartered here. Retail companies at that scale cannot staff customer service agents to manually search 100,000+ SKU catalogs for spec sheets, compliance certificates, and supplier documentation. A RAG system on that content answers in under 5 seconds.

UnitedHealth Group in nearby Minnetonka faces the same problem at a different scale: insurance documentation that adjusters and customer service teams cannot search effectively at volume. We scope engagements to fit the size of the document set and the complexity of the retrieval requirements.

How we work

Describe your product catalog or document search problem.

When product catalog search becomes a cost center.

At 1,000 SKUs, a customer service team can train on the catalog. At 10,000 SKUs, they rely on keyword search. At 100,000 SKUs, keyword search stops working because agents do not know the right product terminology to search by. A customer asking "does this TV have low input lag for gaming" cannot be answered by searching for "input lag": the spec sheet says "response time 1ms with game mode enabled."

The agent either escalates to a supervisor, guesses, or spends 4 minutes searching. All three outcomes cost money. Escalation creates a second agent cost. Guessing creates returns and complaints. Searching extends handle time and reduces throughput.

The same cost pattern appears in supplier compliance management. A Minneapolis retailer sourcing from 500 suppliers across Asia, Europe, and North America has compliance documents in different formats, languages, and certification standards. Compliance teams manually track which certificates are current, which are expiring in 90 days, and which products are affected by a new regulatory requirement.

This manual tracking process is what a RAG system with structured metadata can eliminate. The question is not "can AI do this." The technical components exist. The question is whether the ingestion pipeline handles the format diversity of 500 supplier documents. That is the engineering problem.

What the numbers look like.

500 spec queries per day at 4 minutes each

33 hours of daily agent time on document search. At $35/hour for a customer service agent, that is $1,155 per day, or roughly $290,000 per year. A RAG system reduces each query to under 5 seconds.

15 queries per hour versus 45+ with RAG

Agents spending 4 minutes per spec lookup handle 15 queries per hour. Agents spending under 30 seconds handle 45+. The capacity gain means fewer agents needed for the same query volume, or the same agents handling higher-complexity issues.

Supplier compliance: 90-day expiration window

A RAG system with structured metadata on certificate effective dates can surface 'these 12 supplier certificates expire in the next 90 days' as a daily automated report. That report currently requires a compliance analyst to review all supplier records manually.

Healthcare documentation at UnitedHealth scale

UnitedHealth Group in Minnetonka manages insurance documentation across millions of member records and thousands of policy documents. Customer service teams that cannot retrieve the right benefit document at the point of a member call generate downstream errors and appeals.

What we build.

Delta-based catalog ingestion pipeline

New SKUs are ingested on a nightly schedule without full re-indexing. Discontinued products are flagged in metadata rather than deleted. A daily change set of 500-2,000 items processes in under 30 minutes. The pipeline handles PDF spec sheets, CSV exports from the product information management system, and supplier-provided Excel files.

Semantic section chunking for spec sheets

Technical specifications, regulatory compliance, compatibility notes, and marketing descriptions are separated into distinct chunks with a section_type field. Queries that are clearly technical route to chunks tagged as specifications first. Comparison queries retrieve across section types.

Hierarchical metadata filtering for large catalogs

Before vector search runs, queries filter by product category, manufacturer, compatibility, and price tier. This narrows 100,000 items to 200-400 candidates before semantic matching. Retrieval latency stays under 500ms at this scale.

Supplier compliance normalization layer

Supplier documents in varying formats are processed through a structured extraction step that normalizes certification standard, certificate number, effective date, and product scope into a consistent metadata schema before embedding. Queries like 'which battery suppliers are RoHS compliant' run against the normalized schema.

Benchmark on real agent queries

We build the evaluation set from actual agent queries or representative examples you provide. You see retrieval accuracy and answer accuracy numbers before deployment. The benchmark is a deliverable, not an internal QA step.

How we work.

Scoping call

One hour. You describe the catalog size, update frequency, query types, and current search pain points. We identify the right ingestion and retrieval architecture.

Pilot on a catalog sample

We build on a representative subset: one product category, a few thousand SKUs, a set of real agent queries. You measure accuracy before we build the full system.

Production build and handoff

Fixed scope, typically 4-8 weeks. You get the system, the ingestion pipeline, the evaluation benchmark, and documentation for your team to add new document types.

Full process overview →

Questions about product catalog and retail RAG systems.

Can a RAG system handle a product catalog that updates daily as new SKUs come in and old ones discontinue?: Yes. The ingestion pipeline runs on a schedule (typically nightly) and processes delta updates rather than full re-indexing. New SKUs are ingested and embedded immediately. Discontinued SKUs get a status: discontinued metadata tag applied to their existing chunks rather than deletion, so queries for legacy products still return results but the system can surface a deprecation notice alongside the answer. Full re-indexing is reserved for model changes or schema restructuring, not routine catalog updates. At 100,000+ SKUs, a delta-only update strategy keeps ingestion time under 30 minutes for a typical daily change set of 500-2,000 items.
How do you maintain retrieval accuracy across a 100,000+ item catalog without index bloat?: Index bloat at large SKU counts is a real problem, but it is a metadata filtering problem more than a size problem. The key is hierarchical filtering before semantic search: product category, manufacturer, compatibility tags, and price tier are all filterable metadata fields. A query for a USB-C cable power adapter narrows from 100,000 items to 200-400 before the vector search runs. At that scale, recall is high and latency stays under 500ms. We configure the filtering hierarchy based on how your agents actually query. The vector store itself scales to millions of items without degradation, but the filtering layer is what keeps the user experience fast.
What is the right chunking strategy for product spec sheets that mix technical specs, marketing copy, and compliance notes?: Product spec sheets require semantic sectioning, not fixed-size chunking. We split at section boundaries: technical specifications, regulatory compliance, compatibility, warranty, and marketing description are each separate chunks with a section_type metadata tag. The embedding strategy differs by section: technical specs benefit from a dense retrieval model fine-tuned on technical text. Marketing copy benefits from a general-purpose model. At query time, if the query is clearly technical ('what is the operating temperature range'), we boost the technical-specs chunk type in reranking. If the query is a comparison question, we retrieve across section types. This approach outperforms flat character-count chunking significantly on heterogeneous spec sheet content.
How do you build a RAG system for supplier compliance documents where each supplier formats their documentation differently?: Supplier compliance documents are a normalization problem first and a retrieval problem second. Before embedding, we run each supplier document through a structured extraction step that pulls the compliance standard (ISO, UL, RoHS, etc.), the certificate number, the effective date, and the product scope into a metadata schema. This normalization happens regardless of whether the original document is a PDF from a German manufacturer, a spreadsheet from a Chinese supplier, or a web page from a US distributor. Once the metadata is normalized, retrieval works against a consistent schema. The chunked text handles the unstructured content. A query for 'which of our current battery suppliers are RoHS compliant for EU distribution' runs against the normalized metadata first, then retrieves the relevant compliance text for the shortlisted suppliers.

Scope your Minneapolis document retrieval project.

Describe your catalog size, update frequency, and the queries your team spends the most time answering manually. We reply within one business day.

Hire a RAG developer in Minneapolis.

When product catalog search becomes a cost center.

What the numbers look like.

500 spec queries per day at 4 minutes each

33 hours of daily agent time on document search. At $35/hour for a customer service agent, that is $1,155 per day, or roughly $290,000 per year. A RAG system reduces each query to under 5 seconds.

15 queries per hour versus 45+ with RAG

Supplier compliance: 90-day expiration window

Healthcare documentation at UnitedHealth scale

What we build.

Delta-based catalog ingestion pipeline

Semantic section chunking for spec sheets

Hierarchical metadata filtering for large catalogs

Supplier compliance normalization layer

Benchmark on real agent queries

How we work.

Scoping call

One hour. You describe the catalog size, update frequency, query types, and current search pain points. We identify the right ingestion and retrieval architecture.

Pilot on a catalog sample

We build on a representative subset: one product category, a few thousand SKUs, a set of real agent queries. You measure accuracy before we build the full system.

Production build and handoff

Fixed scope, typically 4-8 weeks. You get the system, the ingestion pipeline, the evaluation benchmark, and documentation for your team to add new document types.

Questions about product catalog and retail RAG systems.

Can a RAG system handle a product catalog that updates daily as new SKUs come in and old ones discontinue?

Yes. The ingestion pipeline runs on a schedule (typically nightly) and processes delta updates rather than full re-indexing. New SKUs are ingested and embedded immediately. Discontinued SKUs get a status: discontinued metadata tag applied to their existing chunks rather than deletion, so queries for legacy products still return results but the system can surface a deprecation notice alongside the answer. Full re-indexing is reserved for model changes or schema restructuring, not routine catalog updates. At 100,000+ SKUs, a delta-only update strategy keeps ingestion time under 30 minutes for a typical daily change set of 500-2,000 items.

How do you maintain retrieval accuracy across a 100,000+ item catalog without index bloat?

Index bloat at large SKU counts is a real problem, but it is a metadata filtering problem more than a size problem. The key is hierarchical filtering before semantic search: product category, manufacturer, compatibility tags, and price tier are all filterable metadata fields. A query for a USB-C cable power adapter narrows from 100,000 items to 200-400 before the vector search runs. At that scale, recall is high and latency stays under 500ms. We configure the filtering hierarchy based on how your agents actually query. The vector store itself scales to millions of items without degradation, but the filtering layer is what keeps the user experience fast.

What is the right chunking strategy for product spec sheets that mix technical specs, marketing copy, and compliance notes?

Product spec sheets require semantic sectioning, not fixed-size chunking. We split at section boundaries: technical specifications, regulatory compliance, compatibility, warranty, and marketing description are each separate chunks with a section_type metadata tag. The embedding strategy differs by section: technical specs benefit from a dense retrieval model fine-tuned on technical text. Marketing copy benefits from a general-purpose model. At query time, if the query is clearly technical ('what is the operating temperature range'), we boost the technical-specs chunk type in reranking. If the query is a comparison question, we retrieve across section types. This approach outperforms flat character-count chunking significantly on heterogeneous spec sheet content.

How do you build a RAG system for supplier compliance documents where each supplier formats their documentation differently?

Supplier compliance documents are a normalization problem first and a retrieval problem second. Before embedding, we run each supplier document through a structured extraction step that pulls the compliance standard (ISO, UL, RoHS, etc.), the certificate number, the effective date, and the product scope into a metadata schema. This normalization happens regardless of whether the original document is a PDF from a German manufacturer, a spreadsheet from a Chinese supplier, or a web page from a US distributor. Once the metadata is normalized, retrieval works against a consistent schema. The chunked text handles the unstructured content. A query for 'which of our current battery suppliers are RoHS compliant for EU distribution' runs against the normalized metadata first, then retrieves the relevant compliance text for the shortlisted suppliers.

Scope your Minneapolis document retrieval project.

Describe your catalog size, update frequency, and the queries your team spends the most time answering manually. We reply within one business day.