Why does our demo RAG work but production accuracy is bad?

Demo RAG passes on cherry-picked queries. Production RAG fails on edge cases, ambiguous queries, and long-tail intent. The fix is an eval harness FIRST (so you can measure), then hybrid retrieval (dense + sparse + reranker), then domain-tuned embeddings, then prompt engineering. Skipping the eval harness is why most RAG plateaus at 60%.

Can the build run inside our VPC?

Yes. I can ship to AWS, GCP, or Azure inside your VPC with self-hosted vector DB (Qdrant, pgvector) and self-hosted LLMs (vLLM with Llama, Mistral, or Qwen). Adds 20 to 30% to the build cost but solves data-residency and compliance requirements common in healthcare, finance, and legal.

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Custom AI Agents + RAG Build

Production RAG and custom agents are not a wrapper around the OpenAI API. They are eval harnesses, vector databases, hybrid retrieval, reranking, prompt-injection defenses, and observability. I have built this at scale (800M profiles at recruitRyte) and will build it for you.

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Who This Is For

B2B SaaS hitting the wall on basic ChatGPT integrations
Companies with proprietary data that cannot leave their VPC
Teams whose RAG demo works but production accuracy is 60%
Recruiting firms needing custom candidate-sourcing pipelines
B2B sales teams needing custom account-intelligence agents

What You Get

>1 to 3 production agents with full eval harness
>Vector database setup (Qdrant, Pinecone, or pgvector)
>Hybrid retrieval with dense + sparse + reranking
>Fine-tuned embeddings on your domain corpus when needed
>Observability stack (Portkey, LangSmith, or custom)
>Cost monitoring + token budgeting
>Production deployment on your VPC or managed service
>Documentation + team enablement session

Engagement Tiers

Each tier scoped on a discovery call. Most clients start with a pilot to test the fit, then expand from there.

Discovery Sprint·1 week

Architecture memo + eval-harness scoping for your specific use case. Output is the build-or-skip decision document.

Build·30 to 60 days

1 to 3 production agents with eval harness, vector DB, retrieval pipeline, observability, and deployment.

Ops Retainer

Ongoing optimization, eval-harness expansion, new feature builds, monitoring and incident response.

Process

01.

Scoping

2-week scoping engagement defining agent boundaries, eval metrics, and success criteria.

02.

Eval harness first

Build the eval harness before the agent. Quality cannot improve what is not measured.

03.

Iterative build

Ship in 2-week sprints with eval-gated releases. Weekly demos to your team.

04.

Production deployment

Deploy to your environment, document the runbook, train your team.

05.

Retainer transition

Optional ongoing retainer for optimization, new features, and incident support.

Proof

Real builds. Named clients. Architecture detail.

recruitRyte

800M-Profile Vector Search at sub-second latency

sub-900ms p95 search across 800M+ indexed candidate profiles with under 2% recall loss after quantization

QdrantHybrid RetrievalCross-Encoder RerankingFine-Tuned EmbeddingsBinary Quantization+3 more

Alacer (Velocity FinCrime Solution Suite)

Alacer: Real-Time Fraud Detection with Explainability

production fraud detection with per-decision SHAP explanations, sub-second Redis-backed inference

Isolation ForestSHAPRedisPythonUnsupervised Learning

FamePilot

FamePilot: Aspect-Based Review Sentiment at Scale

aspect-level sentiment extraction with LLM-generated review reports across customer feedback at scale

spaCyRelation ClassifierPrompt EngineeringPythonNLP

FAQ

RAG is enough for most cases (latest information, source attribution, fast iteration). Fine-tuning matters when you have a structured task with 50K+ examples, when output format must be locked down, or when inference cost at scale matters. I have shipped both. The audit step tells you which fits your problem.

Want this for your team?

Book 30 min. We will talk through your specific situation and I will tell you whether this is the right fit or not.