Service · S.07
RAG & AI Automation Services
Retrieval-augmented generation and agentic workflows that ship to production.
How we work
The process we follow.
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Step · 01
Corpus + evals
Before retrieval, the corpus. Before prompts, the eval set. We build benchmarks from real questions before writing a single embedding.
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Step · 02
Retrieval that works
Naive vector search fails at production scale. Hybrid search (BM25 + dense), re-ranking, query rewriting, and chunk strategy all matter. We test combinations against the eval set.
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Step · 03
Generation with guardrails
Cited answers, structured output validation, refusal heuristics, fallback chains. The model produces what's in the corpus, not what it imagines.
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Step · 04
Agentic when warranted
Single-shot RAG for Q&A. Agent loops for multi-step automation (research, comparison, document drafting). We use the simpler tool when it works.
Pricing
Fair, fixed, written down.
Starts at
$5,000
Typical timeline
3–6 weeks
Package · 01
RAG sprint
$5,000
2–3 weeks
- Up to 10k documents ingested
- Q&A interface or API
- Eval harness baseline
Package · 02
Production RAG
$12,000
4–6 weeks
- Hybrid search + re-ranking
- Multi-source retrieval
- Citation rendering
- Cost + accuracy dashboards
Package · 03
Agentic automation
$18,000+
6–10 weeks
- Multi-step agent workflows
- Tool-calling integrations
- Human-in-the-loop checkpoints
- Production observability
Press clippings
What clients actually said.
“Finding someone who can actually ship LLM features in production is rare. The studio shipped, then helped me hire a verified builder for the rollout.”
Alex Chen
CEO · Lore Protocol
“Working with CODERCOPS was seamless. They understood the nuances of AI-driven interviews and built a product that feels incredibly human. Our users love the realistic experience.”
Sarah Johnson
Founder · PrepAI
“QueryLytic has democratized data access across our organization. Marketing, sales, and ops teams can now get insights without waiting for engineering. CODERCOPS delivered beyond our expectations.”
Michael Torres
CTO · DataFlow Analytics
The toolkit
The stack we trust.
Models
- Claude (Anthropic)
- GPT-4/5 (OpenAI)
- Gemini
- Open-source
Retrieval
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
- Hybrid (BM25+dense)
Frameworks
- LangChain
- LlamaIndex
- Vercel AI SDK
- Custom
Eval / Obs
- Braintrust
- LangSmith
- Helicone
- Custom dashboards
Boring choices on purpose. Plain-stack code outlives the consultant. If you have a stack already, we'll meet you there.
What “RAG” actually means
Retrieval-Augmented Generation. The model doesn’t answer from its training data; it answers from your data, retrieved fresh on every query. Trained on truth, grounded in evidence, citable.
This is the difference between a chatbot that hallucinates and a chatbot that knows. Between a research assistant that sounds smart and one that’s actually correct. Between a demo and a tool you can put in front of paying customers.
What “agentic” actually means
A model that can decide what to do next, take an action, observe the result, and continue. Multi-step automation with a goal, not a fixed prompt template.
Done well, agents are powerful. Done poorly, they’re slow, expensive, and unreliable. Most “agent” demos break at step three because the model loses context, hallucinates a tool call, or misreads its own previous output.
We build agents only where they’re warranted: research synthesis, multi-document comparison, automated drafting, complex troubleshooting. We use observability tools that let you see every step the agent took, every tool it called, every result it observed — so when it goes wrong (and it will), you can debug it.
What we ship
- Document ingestion pipelines. Crawl, chunk, embed, version. Re-ingestion when source content changes. Idempotent, observable, restartable.
- Hybrid retrieval. BM25 + dense vector + re-ranking. Better than naive vector search by 20–40% on real eval sets.
- Citation rendering. Every claim links to its source. Users trust the system because they can verify it.
- Cost guardrails. Per-query budgets, per-day caps, per-user rate limits. Surprises don’t happen.
- Eval harness. Run benchmarks on every change. Regression-proof your prompts. Sleep at night.
- Drift monitoring. Detect when accuracy drops on a content category and alert before users complain.
When NOT to use RAG
Some teams come to us asking for RAG when they should just use structured output + a database query. RAG is the right tool when the answer requires synthesis from text content. If your data is in a SQL database and the question maps to a query, that’s a text-to-SQL feature, not RAG. We’ll tell you which one fits.
Common questions
Things people ask first.
The 80/20 case (vector search + LLM) is easy. The last 20% — citation accuracy, hybrid search, edge cases, drift, scale — is where production failures happen. That's where we live.
We've shipped RAG over corpuses from 100 documents to 10 million. The architecture differs at each scale; we pick what fits.
Eval set built from real questions, scored against expected answers. We benchmark every prompt change and every model swap. You see the dashboard.
We use them when the task genuinely requires multiple steps + decision-making. Most use cases don't — they're better solved with structured prompts. We don't sell agents you don't need.
We can, but rarely recommend it. In 2026, prompting + retrieval covers 95% of use cases. Fine-tuning is for narrow classification or style-mimicking, not knowledge.