RAG Development · Phoenix, AZ

Custom RAG development in Phoenix.

Phoenix runs on mid-market companies. Carvana, Avnet, Insight Enterprises, GoDaddy, Republic Services, and the back-office operations of Banner Health and Magellan Health each have document libraries that a SaaS search tool cannot fully reach and budget profiles where any meaningful capex sits firmly on the CFO's radar. The build-vs-buy conversation is real here in a way it is not at a $500M-EBITDA Fortune 100 buyer.

We build cost-honest RAG systems for Phoenix mid-market operators in healthcare, e-commerce, electronics distribution, and the semiconductor supply chain growing around the TSMC Arizona fab. The architecture decisions get anchored in dollars. Build versus buy gets a real comparison. The number we quote is the number you pay.

Pricing is scoped to your corpus depending on corpus scale, source count, and the security posture your enterprise buyers will review.

How we work

Tell us the corpus, the team size, and the search cost you are trying to take out.

Honest build-vs-buy math.

Glean at $40 per user per month for an 80-person ops team is $38,400 per year. ChatGPT Enterprise at roughly $60 per user is $57,600 per year. Microsoft Copilot bundled into an existing E5 SKU adds $360 per user annually on top of the base license. Each of those products is a real tool and each has a real use case, but none of them indexes the private spec-sheet library at Avnet, the customer-inspection report archive at Carvana, the clinical-policy corpus at Banner, or the payer-contract library at Magellan. The questions that matter remain unanswered.

Azure AI Search and AWS Bedrock Knowledge Bases solve part of the problem. They give you a hosted vector index and a basic retrieval surface. They do not give you the ingestion pipeline for messy real-world documents (electronics component datasheets with embedded tables, healthcare claims correspondence in mixed PDF formats, used-vehicle inspection reports with photo annotations), the evaluation harness, the citation builder, or the audit trail your CISO will demand. You end up paying for the managed service plus engineer time to wire the rest, and the engineer time is usually two to three times the license cost.

A custom RAG carries a fixed build cost scoped to the project. Ongoing inference (Anthropic, OpenAI, or Bedrock Claude depending on the environment) plus vector-store cost runs $1,500 to $6,000 per month at typical mid-market production volumes. A team running 3,000 queries per day on a tuned pipeline lands near $2,500 per month. The break-even math against a Glean license is roughly eight months for an 80-person team and shorter as the team grows.

The number that actually matters is whether the system works well enough to be used. A pretty RAG with 40 percent recall gets abandoned in week three. We build the evaluation harness first, benchmark against 100 to 200 real questions from your team, and report recall, MRR, and answer accuracy before declaring the project done. If the numbers are not above your bar, we tune the pipeline or we tell you the corpus is not retrieval-ready and give back the budget.

What we build for Phoenix teams.

Six components, each priced and benchmarked against the commercial alternatives a mid-market CFO will compare them to.

Cost-engineered inference path

Model tiering between Claude Haiku 4.5 for citation lookup and Claude Sonnet or GPT-5 for synthesis. Embedding caching by content hash so unchanged chunks do not get re-embedded. Per-query cost telemetry surfaced in CloudWatch or Datadog so spend is visible before it surprises finance.

Healthcare-document ingestion at scale

Clinical-policy libraries, claims-data correspondence, denial letters, and appeal packets parsed with table-aware tools. Metadata for facility, payer, service line, and policy effective date preserved so filters run before vector search.

Electronics-component spec-sheet retrieval

Distributor and manufacturer datasheets with embedded parametric tables ingested with table-aware extraction. Metadata for manufacturer part number, package, voltage range, and lifecycle status so a field-applications engineer at Avnet or Insight returns the right cell, not a paragraph.

Customer-inspection and intake retrieval

Used-vehicle inspection reports at Carvana shape, customer-onboarding intake at GoDaddy shape, and warranty-history corpora ingested with VIN, account, and date metadata. Designed for the support-ops use case where the answer has to come back in seconds.

TSMC-cluster export-control architecture

Export-control-sensitive technical documents partitioned with attribute-based access enforced at the IdP layer. Private-link VPC endpoints, customer-managed KMS keys, and audit logging that maps to the export-control posture a TSMC Arizona supplier needs in front of a buyer review.

Evaluation harness and audit log

Test set of 100 to 200 real questions from your team with reported recall, MRR, and answer accuracy. Every production query logged with user, chunks retrieved, and answer returned for SOC 2, HIPAA, or export-control audit needs.

Built for the Phoenix mid-market cost shape.

Phoenix is a mid-market town. The local Fortune 1000 footprint (Avnet, Insight Enterprises, Republic Services, Carvana, GoDaddy, ON Semiconductor) operates at revenue scales where any meaningful capex is a real budget conversation, not a rounding error. The CFO signs the PO. The IT director ran the security review. The head of operations actually uses the tool. Every aspect of how we scope, price, and ship a RAG engagement reflects that buyer.

The healthcare cluster is the second pillar. Banner Health operates across most of the metro Phoenix hospital system and into rural Arizona. Change Healthcare (now part of Optum) processes claims data at industrial scale from its Phoenix campus. Magellan Health runs behavioral-health utilization-review corpora out of Scottsdale. The retrieval problem in each case is bounded, the ROI math is clear, and the architecture has to handle HIPAA without theater. We deploy inside your existing cloud account with customer-managed KMS keys, role-based access at the chunk level, and audit logging that satisfies an OCR review.

The semiconductor supply chain around the TSMC Arizona fab in north Phoenix is the third pillar and the fastest growing. The suppliers, packagers, and downstream electronics distributors (Avnet alone moves billions in components) carry technical-document libraries with export-control implications. The architecture we ship into this segment isolates export-controlled material at the index layer and enforces US-persons access where the underlying technology demands it. The pricing is scoped the same way regardless of segment, because the heavy lift is the ingestion path, not the compliance posture.

Questions from Phoenix buyers.

How does the cost of a custom RAG actually compare to Glean or ChatGPT Enterprise for an 80-person operations team?: Glean lists at roughly $40 per user per month for the enterprise tier. ChatGPT Enterprise lands near $60. For an 80-person operations team at a Phoenix mid-market like Avnet, Insight, or Carvana, that is $38,000 per year for Glean or $58,000 per year for ChatGPT Enterprise in license cost, every year, ongoing. A custom RAG carries a fixed build cost plus ongoing inference and infrastructure (typically $1,500 to $6,000 per month at this team size). After year one, the custom build is usually cheaper. It also answers questions the SaaS option literally cannot, because Glean indexes the document corpora it has connectors for and a private spec-sheet library or a customer-intake document set may not be on that list. The right move is sometimes both: a SaaS search layer for the broad knowledge base, a custom RAG for the corpus that matters.
Where does Azure AI Search or Bedrock Knowledge Bases fit, and where does a custom build actually beat them?: Azure AI Search and AWS Bedrock Knowledge Bases give you a hosted vector index and a basic retrieval surface against your documents. They are useful, they are cheap, and for a clean document set with simple Q&A needs they may be all you require. Where they fall short is on the ingestion side and on the evaluation side. They do not give you the messy-document parsing path (scanned compliance records, electronics-component spec sheets with embedded tables, customer-inspection PDFs with mixed structure), the table-aware extraction that real corpora need, the evaluation harness against actual user questions, or the citation builder your compliance or operations team will demand. Most Phoenix mid-market deployments need a custom ingestion pipeline plus the hosted vector index, not the hosted retrieval product as a whole. We routinely build on top of Azure AI Search or Bedrock and add the missing pieces, which keeps the fixed price honest.
We are a healthcare operator at Banner Health or Change Healthcare scale. What is the actual ROI math on a RAG for clinical-policy or claims-data retrieval?: Banner Health maintains clinical-policy libraries across thirty-plus hospitals. Change Healthcare processes billions of claims-data documents annually with associated EOBs, denial letters, and appeal correspondence. Magellan Health runs behavioral-health utilization-review corpora. In each case the relevant retrieval target is a clinical or claims analyst billing roughly $90,000 to $130,000 fully loaded who spends 20 to 30 percent of their week on document search. For a 40-person team, that is $720,000 to $1.6M per year of search cost. A custom RAG plus roughly $3,000 per month in inference and vector-store cost pays back in roughly two months. The harder question is whether the system actually cuts search time, which is why we build the evaluation harness against real questions from your team and report recall and answer accuracy before declaring the project done.
How do you handle the security-and-compliance review that an Arizona mid-market or TSMC Arizona supplier will run?: Phoenix has a mid-market security culture that takes SOC 2, HIPAA where relevant, and the export-control framework around the TSMC Arizona fab seriously. We deploy inside your existing cloud account (AWS, Azure, or GCP), use customer-managed KMS keys for everything at rest, enforce role-based access at the chunk level, and ship the audit log into your existing observability stack (CloudWatch, Azure Monitor, Datadog, or Splunk). For TSMC suppliers and the broader semiconductor cluster, export-control-sensitive technical documents stay in a partitioned index with attribute-based access enforced at the IdP layer. The architecture passes a typical Arizona enterprise security review on the first pass; if your CISO needs something specific (FIPS 140-2 modules, private-link VPC endpoints, customer-managed encryption keys with hardware HSM backing), we map it before kickoff rather than after.

Ready to run the numbers?

Tell us the corpus you have, the team size, and the search cost you are trying to take out. We reply within one business day with a rough scope, a price range, and an honest build-vs-buy comparison.

Custom RAG development in Phoenix.

Pricing is scoped to your corpus depending on corpus scale, source count, and the security posture your enterprise buyers will review.

Honest build-vs-buy math.

What we build for Phoenix teams.

Six components, each priced and benchmarked against the commercial alternatives a mid-market CFO will compare them to.

Cost-engineered inference path

Healthcare-document ingestion at scale

Electronics-component spec-sheet retrieval

Customer-inspection and intake retrieval

TSMC-cluster export-control architecture

Evaluation harness and audit log

Built for the Phoenix mid-market cost shape.

Questions from Phoenix buyers.

How does the cost of a custom RAG actually compare to Glean or ChatGPT Enterprise for an 80-person operations team?

Glean lists at roughly $40 per user per month for the enterprise tier. ChatGPT Enterprise lands near $60. For an 80-person operations team at a Phoenix mid-market like Avnet, Insight, or Carvana, that is $38,000 per year for Glean or $58,000 per year for ChatGPT Enterprise in license cost, every year, ongoing. A custom RAG carries a fixed build cost plus ongoing inference and infrastructure (typically $1,500 to $6,000 per month at this team size). After year one, the custom build is usually cheaper. It also answers questions the SaaS option literally cannot, because Glean indexes the document corpora it has connectors for and a private spec-sheet library or a customer-intake document set may not be on that list. The right move is sometimes both: a SaaS search layer for the broad knowledge base, a custom RAG for the corpus that matters.

Where does Azure AI Search or Bedrock Knowledge Bases fit, and where does a custom build actually beat them?

Azure AI Search and AWS Bedrock Knowledge Bases give you a hosted vector index and a basic retrieval surface against your documents. They are useful, they are cheap, and for a clean document set with simple Q&A needs they may be all you require. Where they fall short is on the ingestion side and on the evaluation side. They do not give you the messy-document parsing path (scanned compliance records, electronics-component spec sheets with embedded tables, customer-inspection PDFs with mixed structure), the table-aware extraction that real corpora need, the evaluation harness against actual user questions, or the citation builder your compliance or operations team will demand. Most Phoenix mid-market deployments need a custom ingestion pipeline plus the hosted vector index, not the hosted retrieval product as a whole. We routinely build on top of Azure AI Search or Bedrock and add the missing pieces, which keeps the fixed price honest.

We are a healthcare operator at Banner Health or Change Healthcare scale. What is the actual ROI math on a RAG for clinical-policy or claims-data retrieval?

Banner Health maintains clinical-policy libraries across thirty-plus hospitals. Change Healthcare processes billions of claims-data documents annually with associated EOBs, denial letters, and appeal correspondence. Magellan Health runs behavioral-health utilization-review corpora. In each case the relevant retrieval target is a clinical or claims analyst billing roughly $90,000 to $130,000 fully loaded who spends 20 to 30 percent of their week on document search. For a 40-person team, that is $720,000 to $1.6M per year of search cost. A custom RAG plus roughly $3,000 per month in inference and vector-store cost pays back in roughly two months. The harder question is whether the system actually cuts search time, which is why we build the evaluation harness against real questions from your team and report recall and answer accuracy before declaring the project done.

How do you handle the security-and-compliance review that an Arizona mid-market or TSMC Arizona supplier will run?

Phoenix has a mid-market security culture that takes SOC 2, HIPAA where relevant, and the export-control framework around the TSMC Arizona fab seriously. We deploy inside your existing cloud account (AWS, Azure, or GCP), use customer-managed KMS keys for everything at rest, enforce role-based access at the chunk level, and ship the audit log into your existing observability stack (CloudWatch, Azure Monitor, Datadog, or Splunk). For TSMC suppliers and the broader semiconductor cluster, export-control-sensitive technical documents stay in a partitioned index with attribute-based access enforced at the IdP layer. The architecture passes a typical Arizona enterprise security review on the first pass; if your CISO needs something specific (FIPS 140-2 modules, private-link VPC endpoints, customer-managed encryption keys with hardware HSM backing), we map it before kickoff rather than after.

Ready to run the numbers?

Tell us the corpus you have, the team size, and the search cost you are trying to take out. We reply within one business day with a rough scope, a price range, and an honest build-vs-buy comparison.