Hire a RAG Developer · Chicago, IL

Hire a RAG developer in Chicago.

An operator on a Chicago manufacturing floor at 2am needs the correct non-standard maintenance procedure for a specific valve model. The supervisor is unavailable. The procedure manual is a 1,200-page PDF. Getting the wrong procedure has safety and liability consequences.

This is the same problem faced by insurance claims adjusters at CNA or Zurich: the right policy language exists in the library, but finding it in a 60-90 minute search window during claim review is not workable at scale.

A RAG system built for Chicago industrial and insurance document libraries returns the right answer in seconds, with the document reference attached, scoped as a fixed-price engagement.

How we work

Tell us about your procedure library or policy documents.

The problem in Chicago's industrial base.

Chicago's manufacturing and logistics sector sits on a mountain of procedure documentation. CN Rail's logistics operations require maintenance and safety procedures for hundreds of equipment types. Caterpillar's regional operations generate service manuals, parts specifications, and safety bulletins continuously. A single large facility might maintain 50,000+ pages of active technical documentation.

The documents are often kept in SharePoint, a legacy DMS, or paper binders. The search tools in these systems match keywords, not meaning. An operator searching for "non-return valve shutdown sequence" needs to know the right terminology first, which defeats the purpose.

For insurance, the scale is different but the problem is identical. CNA Insurance, Zurich North America, and Allstate (headquartered in Northbrook, 20 miles north of the Loop) each have claims adjusters working through complex coverage questions against policy form libraries that run hundreds of forms. A commercial property adjuster handling 15 claims daily cannot spend 60 minutes per claim searching for applicable endorsements.

In both cases, the cost is not just time. Manufacturing: wrong procedures create equipment damage and safety incidents. Insurance: adjusters who cannot find the right language make incorrect coverage decisions, generating errors that become disputes and litigation.

What a RAG system changes for these workflows.

Natural language queries on technical documents

An operator can ask 'how do I isolate the hydraulic circuit on a 345 GC excavator before removing the pump' and get the correct procedure, not a list of keyword matches. The query does not need to match the exact terminology in the document.

Strict confidence thresholds for safety-critical content

If the retrieval confidence is below a defined threshold, the system returns a fallback: contact a supervisor, do not generate a plausible-sounding answer. This is configurable per deployment and per document type.

Policy form search with coverage filter

Insurance adjusters can filter by policy type, coverage line, jurisdiction, and effective date before the semantic search runs. 'What does our Commercial Auto form say about hired and non-owned vehicles in Illinois' returns the right endorsement, not a list of tangentially related forms.

Full audit trail per query

Every query logs the user, timestamp, question text, retrieved chunks with similarity scores, and the generated answer. Required for liability documentation in manufacturing incidents and for claims file documentation in insurance.

The technical components.

Procedure manual ingestion with section preservation

Technical manuals have hierarchical structures: chapter, section, subsection, step. We preserve this hierarchy during chunking so retrieval returns complete steps in context. Step 4 of a 6-step procedure does not get split into a separate chunk from step 3.

Equipment and model metadata tagging

Each procedure chunk gets tagged with applicable equipment models, serial number ranges, part numbers, and revision dates extracted from the document headers. A query can then filter to 'show me procedures that apply to this specific model number' before running the semantic search.

Policy form chunking with endorsement cross-reference

Insurance policy forms reference endorsements, exclusions, and definitions defined elsewhere in the form. We index these cross-references so a query on an exclusion retrieves both the exclusion text and the relevant definition that governs its scope.

Voice input integration for floor environments

For manufacturing floor deployments, we pair the RAG backend with a Whisper-based voice-to-text front end. Operators speak their query; the pipeline returns a short formatted answer with the procedure number, revision date, and a direct link to the source document.

Evaluation suite against your real questions

We build a test set from actual queries your team has submitted (or would submit) and measure recall@3, answer accuracy, and citation correctness before deployment. You get a benchmark, not a demo.

Questions about industrial and insurance RAG systems.

How do you handle safety-critical procedure retrieval where the wrong answer has real consequences?: Safety-critical retrieval requires strict confidence thresholds and explicit fallback behavior. We configure the system to return a result only when retrieval confidence exceeds a defined threshold (e.g., top-1 similarity score above 0.82). Below that threshold, the system responds with 'I cannot find a matching procedure in the approved documentation, contact a supervisor.' It does not generate an answer by reasoning from partial context. Every retrieval event is logged with the query, returned chunks, similarity scores, and user ID for audit review.
CNA Insurance and Zurich have policy libraries that update frequently. How do you handle document versioning?: Each policy document gets a version metadata field (effective_date, version_id, status: active/superseded). When a new policy version is ingested, the prior version's chunks are marked superseded in the vector store metadata. Active queries return only the current version by default. Adjusters can specify a historical date to retrieve the version in effect on that date, useful for claims involving incidents that occurred under a prior policy form.
Can operators on a factory floor actually use a RAG interface, or does it require technical users?: The interface layer is configurable. For floor environments, we typically pair the RAG backend with a simple voice-to-text input (using Whisper) so operators can speak a query rather than type. The response is formatted for display on a tablet or industrial panel: a short direct answer, the procedure reference number and revision date, and a link to the full document. No technical knowledge required. The complexity is in the retrieval pipeline, the interface can be as simple as a form field.
Hyatt has hundreds of operational procedures across properties globally. How do you handle multi-site document organization?: Multi-site document libraries are organized via namespace segmentation in the vector store and structured metadata. Each document gets location, department, document_type, and region fields. A query from a Chicago property can be scoped to Chicago-specific procedures or expanded to the global library depending on the query type. Brand-level policies apply globally; property-specific SOPs are scoped locally. This is a metadata filter strategy, not a physical separation of indexes, so the system stays manageable as new properties add documents.

Scope your Chicago document retrieval project.

Describe your document library, the queries your team needs to answer, and any safety or compliance requirements. We reply within one business day.

Hire a RAG developer in Chicago.

A RAG system built for Chicago industrial and insurance document libraries returns the right answer in seconds, with the document reference attached, scoped as a fixed-price engagement.

The problem in Chicago's industrial base.

What a RAG system changes for these workflows.

Natural language queries on technical documents

Strict confidence thresholds for safety-critical content

Policy form search with coverage filter

Full audit trail per query

The technical components.

Procedure manual ingestion with section preservation

Equipment and model metadata tagging

Policy form chunking with endorsement cross-reference

Voice input integration for floor environments

Evaluation suite against your real questions

We build a test set from actual queries your team has submitted (or would submit) and measure recall@3, answer accuracy, and citation correctness before deployment. You get a benchmark, not a demo.

Questions about industrial and insurance RAG systems.

How do you handle safety-critical procedure retrieval where the wrong answer has real consequences?

Safety-critical retrieval requires strict confidence thresholds and explicit fallback behavior. We configure the system to return a result only when retrieval confidence exceeds a defined threshold (e.g., top-1 similarity score above 0.82). Below that threshold, the system responds with 'I cannot find a matching procedure in the approved documentation, contact a supervisor.' It does not generate an answer by reasoning from partial context. Every retrieval event is logged with the query, returned chunks, similarity scores, and user ID for audit review.

CNA Insurance and Zurich have policy libraries that update frequently. How do you handle document versioning?

Each policy document gets a version metadata field (effective_date, version_id, status: active/superseded). When a new policy version is ingested, the prior version's chunks are marked superseded in the vector store metadata. Active queries return only the current version by default. Adjusters can specify a historical date to retrieve the version in effect on that date, useful for claims involving incidents that occurred under a prior policy form.

Can operators on a factory floor actually use a RAG interface, or does it require technical users?

The interface layer is configurable. For floor environments, we typically pair the RAG backend with a simple voice-to-text input (using Whisper) so operators can speak a query rather than type. The response is formatted for display on a tablet or industrial panel: a short direct answer, the procedure reference number and revision date, and a link to the full document. No technical knowledge required. The complexity is in the retrieval pipeline, the interface can be as simple as a form field.

Hyatt has hundreds of operational procedures across properties globally. How do you handle multi-site document organization?

Multi-site document libraries are organized via namespace segmentation in the vector store and structured metadata. Each document gets location, department, document_type, and region fields. A query from a Chicago property can be scoped to Chicago-specific procedures or expanded to the global library depending on the query type. Brand-level policies apply globally; property-specific SOPs are scoped locally. This is a metadata filter strategy, not a physical separation of indexes, so the system stays manageable as new properties add documents.

Scope your Chicago document retrieval project.

Describe your document library, the queries your team needs to answer, and any safety or compliance requirements. We reply within one business day.