72% of enterprise RAG implementations fail in year one. We build the ones that don't — with adversarial testing, LLM-as-judge evaluation, interchangeable pipelines, and production groundedness validation.
An interactive map of our evaluation infrastructure. Click nodes to explore.
Most teams ship a prototype and pray. Here's what separates production-grade RAG from a demo.
5-10 hand-picked queries the developer knows work. Manual spot-checking at demo time, never revisited. No metrics, no regression testing, no automation.
Auto-generated test cases across multiple adversarial categories. Hit Rate, MRR, latency, and cost tracked per question. LLM-as-judge with chain-of-thought reasoning. CI/CD quality gates on every change.
Zero adversarial probes. System confidently hallucinates when asked tricky questions. No handling of out-of-scope, false-premise, or ambiguous queries. Users discover failures in production.
Auto-generated adversarial suites test hallucination bait, false premises, cross-municipality confusion, numerical precision, boundary edge cases, and more. Each judged for severity and failure type.
"It usually looks right." No validation. In medical contexts, chatbots have claimed drug side effects absent from source materials. Errors discovered by users.
Every production response is validated against source chunks by a fast validator model. Ungrounded responses trigger automatic retry with correction instructions. Exhausted retries return a safe fallback — never a hallucination.
"We use Ada embeddings with 500-token chunks because that's what the tutorial said." No experimentation, no comparison, no data-driven decisions.
Multiple chunking strategies, pipeline architectures, configurable models, search modes, and prompt versions — all swappable via admin UI. Run the same test set across configurations and apply the winning config to production with one click.
No per-query, per-user, or per-feature cost breakdown. Surprised by monthly bills. No understanding of which queries drive costs. Tool outputs consume 100x more tokens than user messages.
Model registry with pricing for every Bedrock model. Per-question cost tracking in test runs. Total cost computed for every evaluation. Test different models and see exact cost impact before deploying.
Every adversarial question is auto-generated from real production data using an LLM, then judged by another LLM for severity and failure type.
Every component of the pipeline uses a Strategy + Registry pattern. Swap, test, compare, deploy — without changing a line of code.
Abstract base class with @register_chunker decorator. Each version is a separate file implementing the strategy interface.
LangGraph-based pipelines with @register_pipeline decorator. Each pipeline defines its own parameter schema and node graph.
Single source of truth for all Bedrock model IDs with per-token pricing. Swap generation, utility, embedding, and validation models independently.
Unified SearchConfig schema across all search surfaces. Tune retrieval independently of generation.
Immutable prompt versions stored in the database. Override system or validator prompts per test run for controlled A/B comparison.
PipelineConfig supports N-stage validation via JSON config. Stack multiple validators with independent models and retry budgets.
A full admin dashboard with real-time progress, color-coded results, and one-click config application.
| Category | Question | Result | Confidence | Severity |
|---|---|---|---|---|
| HALLUCINATION_BAIT | What's the exact fee for a conditional use permit? | PASS | 0.95 | — |
| FALSE_PREMISE | Since the zoning board was dissolved in 2019, who handles appeals? | PASS | 0.91 | — |
| CROSS_MUNICIPALITY | Do the same noise ordinances apply in both districts? | PASS | 0.88 | — |
| NUMERICAL_PRECISION | What's the maximum fence height in residential zones? | PASS | 0.97 | — |
| OUT_OF_SCOPE | What are the federal tax implications of rezoning? | PASS | 0.93 | — |
| AMBIGUOUS_QUERY | Can I build something on my property? | PASS | 0.86 | — |
| BOUNDARY_EDGE_CASE | My parcel is split between R-1 and C-2 zones. Which rules apply? | FAIL | 0.72 | minor |
| COMMON_MISCONCEPTION | Don't I need a permit to trim a tree on my own property? | PASS | 0.90 | — |
Representative sample. Real test runs contain many more questions across all categories.
A RAG system that gives residents instant, accurate answers from thousands of pages of municipal ordinances — with citations, not hallucinations.
Thousands of pages of zoning codes, building permits, noise ordinances, and fee schedules across multiple municipalities. Residents need exact answers — wrong information about permits or property rights has real consequences. Traditional RAG prototypes hallucinate fees, confuse jurisdictions, and can't handle edge cases.
A multi-municipality RAG platform with 4 iteratively-evolved chunking strategies, 2 swappable pipeline architectures, 8-category adversarial test suites auto-generated from real data, runtime groundedness validation with automatic retry, and per-query cost tracking. Every response is citation-backed or safely declined.
Every response passes through groundedness validation. If claims can't be verified against source documents, the system retries or returns a safe fallback — never a hallucination.
“We knew AI could transform how municipalities access public records, but building it right required deep expertise. Guru Cloud & AI helped us architect a RAG system that actually understands government documents and integrated it seamlessly into our platform. They made the complex feel manageable.”
RAG isn't just about retrieval accuracy — the user experience matters. Here's what production-grade output actually looks like.
Every claim links back to its source. Citation badges open a legal-code-style modal with the exact ordinance text rendered in proper typography — section number, title, and full content. Users verify answers without leaving the chat.
Fee schedules, zoning comparisons, and permit requirements are rendered as formatted tables. A hover-reveal expand button opens any table full-screen for easy reading — critical when ordinance data spans many columns.
Responses use full GitHub-flavored markdown: headers, bold, lists, code blocks, and links. Complex answers about multi-step processes are structured and scannable, not walls of text.
Every response has thumbs up/down with categorized feedback (factually incorrect, poor behavior, other) and optional notes. This data feeds back into evaluation and improves the system over time.
This is the real GovToKnow assistant running against live municipal data. Ask about building permits, zoning, noise ordinances, or fees.
Live demo using Happytown — a showcase municipality. Responses are generated from real ordinance data via our production RAG pipeline.
Switch between pipeline modes to compare quality, latency, and cost on the same test set.
One model handles everything: tool use, response generation, and context synthesis. Simpler, lower cost, faster — ideal for straightforward retrieval.
A utility model plans and curates search queries. A generation model writes the final response. Higher quality for complex multi-hop questions.
Test → Compare → Apply. The winning configuration goes live with one click.
Whether you're building from scratch or fixing a broken pipeline, we bring the evaluation infrastructure that separates production systems from prototypes.