RAG Development · Los Angeles, CA

Custom RAG development in Los Angeles.

A custom RAG system carries ongoing inference and infrastructure cost of $1,500 to $8,000 per month. A three-person aerospace research team in Hawthorne or El Segundo costs $450,000 per year fully loaded. A senior gaming engineer at Riot in West LA costs more. If a retrieval system cuts a quarter of their week off technical-document search, the build pays back inside the first quarter.

We build cost-honest RAG systems for LA aerospace, media, gaming, and e-commerce teams. The architecture decisions get anchored in dollars, not vibes. Build versus buy gets a real comparison. The number we quote is the number you pay.

Pricing depends on document scale, security posture, and whether the deployment lives in commercial or government cloud — we scope it after a discovery call.

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.

ChatGPT Enterprise at roughly $60 per user per month sounds cheap until you map it against what your team actually searches. The product cannot reach your private corpus without a custom connector, so the questions that matter (the ITAR-cleared engineering library, the post-production notes archive, the Snap Trust-and-Safety policy corpus, the Riot QA database) are invisible to it. A 50-engineer team runs $36,000 per year for a tool that answers questions Google would answer for free.

Azure AI Search and AWS Bedrock Knowledge Bases solve part of the problem. They give you a hosted vector index and a retrieval surface against your documents. They do not give you the ingestion pipeline for messy real-world data (scanned MIL-STD specs, post-production scripts with proprietary markup, gaming-engine documentation with versioned branches), the evaluation harness, the citation builder, or the security posture your CISO will accept. 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's fixed build cost depends on document scale, security posture, and deployment environment. Ongoing inference (Anthropic, OpenAI, or Bedrock Claude depending on which environment you operate in) plus vector-store cost runs $1,500 to $8,000 per month at typical production volumes. A team doing 5,000 queries per day on a tuned pipeline lands near $3,000 per month. The break-even math against engineer-search-time is two to four months for any technical team above 20 people.

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 before we ship or we tell you the corpus is not retrieval-ready and give back the budget.

What we build for LA teams.

Six components, each priced and benchmarked against the commercial alternatives.

GovCloud and FedRAMP-aligned deployments

ITAR-cleared architecture in AWS GovCloud or Azure Government. US-persons access enforced at Okta or Entra ID, inference via Bedrock GovCloud or Azure OpenAI Government, no data leaves the cleared boundary including during embedding generation.

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.

Technical-library extraction at scale

MIL-STD specs, post-production scripts, gaming-engine docs with versioned branches, and aerospace test reports parsed with table-aware tools. Metadata for spec number, revision, classification level, and asset ID preserved so filters run before vector search.

Content-moderation policy retrieval

Policy libraries chunked at the rule level with cross-references intact. Precedent-decision databases ingested with metadata for content type, jurisdiction, and decision date. Designed against California AB 587 transparency reporting and platform-specific audit needs.

Evaluation harness with measurable recall

Test set of 100 to 200 real questions from your team. Recall at 5, MRR, and answer-accuracy benchmarks reported before the engagement closes. If the numbers are not above your bar, we tune or we hand back the budget.

Streaming generation with citation builder

Sub-500-ms time-to-first-token so reviewers and engineers stay in flow. Citations render with source name, version, and section so the answer can be acted on or audited. Streaming via Bedrock, Azure OpenAI, or direct provider APIs depending on environment.

Built for the LA cost-and-corpus shape.

The LA technical economy is concentrated in three places where custom RAG actually pays for itself. Aerospace runs from Hawthorne (SpaceX), El Segundo (Raytheon, Boeing satellites, the Aerospace Corporation), and the broader South Bay defense footprint. The corpora here are ITAR-controlled, the buyers are program managers and chief engineers, and the alternative is a junior engineer's afternoon. Gaming runs from West LA and Santa Monica (Riot, Activision Blizzard's Santa Monica office, dozens of mid-size studios) where the docs are versioned game-engine references, design documents, and QA test archives. Media and entertainment runs from Burbank, Culver City, and Hollywood (NBCUniversal, Disney, Netflix's LA presence, Snap, the post-production houses) with policy libraries, post-production scripts, and brand safety corpora that need to be queryable across thousands of employees and contractors.

The e-commerce and consumer cluster is the fourth axis. Snap, DoorDash, Service Titan, and the rest of the LA consumer-tech ecosystem each run sales-enablement, merchant-onboarding, and support-knowledge corpora that benefit from retrieval the same way an Austin SaaS company does, but at LA labor cost. A sales-engineering hour in Santa Monica costs roughly what a senior engineering hour costs in Austin, so the ROI math flips earlier.

California AB 587 (the social-media transparency act) and the California Privacy Rights Act both touch any retrieval system that processes user-generated or moderator-facing content. We design the audit trail so the data your transparency report needs (decisions made, policies applied, appeal outcomes) falls out of the query log rather than being reconstructed quarterly by a compliance team.

Questions from Los Angeles buyers.

How does the cost of a custom RAG compare to just buying ChatGPT Enterprise for the team?: They solve different problems and the comparison is worth doing carefully. ChatGPT Enterprise lands at roughly $60 per user per month with no access to your private corpus unless you build a connector. For a 50-person engineering or research org, that is $36,000 per year in license cost and the system still cannot answer the questions your team actually asks because the corpus it needs (ITAR-controlled technical docs, internal Confluence, contract libraries, post-production notes) is not in the index. A custom RAG carries ongoing inference and infrastructure cost (typically $1,500 to $8,000 per month depending on volume) on top of the one-time build. After year one, the custom build is usually cheaper, and it answers questions the SaaS option literally cannot. The right move is often both: ChatGPT Enterprise for general work, custom RAG for the corpus that matters.
We deal with ITAR-controlled technical documents at our aerospace operation. Can a RAG system actually be deployed under those controls?: Yes, but the architecture has to reflect it from day one. ITAR (22 CFR 120-130) restricts technical-data access to US persons and requires the data to stay inside controlled environments. For SpaceX, Northrop, Boeing's LA-area operations, or Lockheed's aerospace work in the broader region, we deploy inside AWS GovCloud or Azure Government with FedRAMP-aligned controls, US-persons access enforced at the IdP layer (typically Okta or Azure Entra ID with attribute-based access), and inference against models that are explicitly cleared for the environment (AWS Bedrock in GovCloud, Azure OpenAI in Azure Government). No data ever leaves the cleared boundary, including embedding generation. We have shipped this pattern and can walk through the specific control mappings on a discovery call.
What's the actual ROI math for putting a RAG system in front of an aerospace or gaming engineering team?: The dominant cost in any technical organization is engineer time. A junior aerospace engineer at SpaceX or Northrop costs roughly $180,000 per year fully loaded. A senior gaming engineer at Riot or Activision costs more. Industry studies and our own measurements put technical-document search time at 20 to 30 percent of an engineer's week. For a 30-person team, that is $1.1M to $1.6M per year of search cost. A custom RAG that cuts that in half pays back within a couple of months. The harder question is whether the RAG actually cuts search time, which is why we build an evaluation harness against real questions from your team and report recall and answer accuracy before declaring the project done.
How do you handle content-moderation and brand-safety corpora for a media or social company in LA?: Snap, Netflix, and the trust-and-safety teams at Riot and Activision each maintain large policy libraries that have to be applied consistently across hundreds of moderators and millions of pieces of content. The retrieval problem is to surface the relevant policy clause, precedent decision, and appeals history for a specific moderation case in under two seconds so the reviewer can decide before context-switching costs eat the day. We chunk the policy corpus at the rule level with cross-references intact, ingest the precedent-decision database with metadata for content type, jurisdiction, and decision date, and run retrieval as hybrid BM25 plus dense vectors with metadata pre-filters. The system surfaces 'this case looks like these three prior cases and these four policy rules apply' with citations to the exact rule and precedent. Reviewer-side latency drops, decision consistency goes up, and the audit trail satisfies what California AB 587 transparency reporting wants.

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 Los Angeles.

Pricing depends on document scale, security posture, and whether the deployment lives in commercial or government cloud — we scope it after a discovery call.

Honest build-vs-buy math.

What we build for LA teams.

Six components, each priced and benchmarked against the commercial alternatives.

GovCloud and FedRAMP-aligned deployments

Cost-engineered inference path

Technical-library extraction at scale

Content-moderation policy retrieval

Evaluation harness with measurable recall

Streaming generation with citation builder

Built for the LA cost-and-corpus shape.

Questions from Los Angeles buyers.

How does the cost of a custom RAG compare to just buying ChatGPT Enterprise for the team?

They solve different problems and the comparison is worth doing carefully. ChatGPT Enterprise lands at roughly $60 per user per month with no access to your private corpus unless you build a connector. For a 50-person engineering or research org, that is $36,000 per year in license cost and the system still cannot answer the questions your team actually asks because the corpus it needs (ITAR-controlled technical docs, internal Confluence, contract libraries, post-production notes) is not in the index. A custom RAG carries ongoing inference and infrastructure cost (typically $1,500 to $8,000 per month depending on volume) on top of the one-time build. After year one, the custom build is usually cheaper, and it answers questions the SaaS option literally cannot. The right move is often both: ChatGPT Enterprise for general work, custom RAG for the corpus that matters.

We deal with ITAR-controlled technical documents at our aerospace operation. Can a RAG system actually be deployed under those controls?

Yes, but the architecture has to reflect it from day one. ITAR (22 CFR 120-130) restricts technical-data access to US persons and requires the data to stay inside controlled environments. For SpaceX, Northrop, Boeing's LA-area operations, or Lockheed's aerospace work in the broader region, we deploy inside AWS GovCloud or Azure Government with FedRAMP-aligned controls, US-persons access enforced at the IdP layer (typically Okta or Azure Entra ID with attribute-based access), and inference against models that are explicitly cleared for the environment (AWS Bedrock in GovCloud, Azure OpenAI in Azure Government). No data ever leaves the cleared boundary, including embedding generation. We have shipped this pattern and can walk through the specific control mappings on a discovery call.

What's the actual ROI math for putting a RAG system in front of an aerospace or gaming engineering team?

The dominant cost in any technical organization is engineer time. A junior aerospace engineer at SpaceX or Northrop costs roughly $180,000 per year fully loaded. A senior gaming engineer at Riot or Activision costs more. Industry studies and our own measurements put technical-document search time at 20 to 30 percent of an engineer's week. For a 30-person team, that is $1.1M to $1.6M per year of search cost. A custom RAG that cuts that in half pays back within a couple of months. The harder question is whether the RAG actually cuts search time, which is why we build an evaluation harness against real questions from your team and report recall and answer accuracy before declaring the project done.

How do you handle content-moderation and brand-safety corpora for a media or social company in LA?

Snap, Netflix, and the trust-and-safety teams at Riot and Activision each maintain large policy libraries that have to be applied consistently across hundreds of moderators and millions of pieces of content. The retrieval problem is to surface the relevant policy clause, precedent decision, and appeals history for a specific moderation case in under two seconds so the reviewer can decide before context-switching costs eat the day. We chunk the policy corpus at the rule level with cross-references intact, ingest the precedent-decision database with metadata for content type, jurisdiction, and decision date, and run retrieval as hybrid BM25 plus dense vectors with metadata pre-filters. The system surfaces 'this case looks like these three prior cases and these four policy rules apply' with citations to the exact rule and precedent. Reviewer-side latency drops, decision consistency goes up, and the audit trail satisfies what California AB 587 transparency reporting wants.

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.