# Tricky Wombat

> Enterprise AI search platform built on context-first architecture. We engineer the full context pipeline — from query intent classification to retrieval, assembly, generation, and evaluation — so every AI answer is accurate, sourced, and relevant.

## About

- [Homepage](https://www.trickywombat.ai): AI-powered search that finds better answers from your data
- [Team Knowledge](https://www.trickywombat.ai/search-small-teams): AI search for teams under 50 people
- [Enterprise Search](https://www.trickywombat.ai/enterprise-search): AI search for organizations with 100-5000+ employees
- [Technical Discovery](https://www.trickywombat.ai/technical-discovery): AI search for engineering teams across repos, wikis, and docs
- [Process Mining](https://www.trickywombat.ai/process-mining): Surface how work actually runs across SOPs, tickets, and tribal knowledge
- [Pricing](https://www.trickywombat.ai/price/calculator): Transparent pricing calculator

## Signals (Articles)

- [GPT-6 Astra crossed the AGI line and it changed almost nothing about your AI strategy](https://www.trickywombat.ai/signals/chat-gpt-6-astra-agi): Individual users report 80% productivity gains from AI. Only 6% of organizations can tie it to earnings. GPT-6 Astra's benchmark score swings 37 points on the same weights depending on the infrastructure around it. The next model leap will not close that gap.
- [A company brain determines the return on every other investment you make](https://www.trickywombat.ai/signals/company-brain-explanation): DBS Bank made a knowledge base the precondition for scaling AI and reported S$750 million in value while running an order of magnitude more models in production than peer banks. The difference was never the model. It was the company brain underneath it.
- [RAG as a service is the rational default for enterprise AI](https://www.trickywombat.ai/signals/rag-as-a-service): RAG as a Service (RaaS) creates significant enterprise value
- [Your YouTube catalog is a knowledge base your audience cannot search](https://www.trickywombat.ai/signals/youtubers-have-a-body-of-work-that-is-buried): A channel with 10,000+ subscribers is a knowledge base locked in the one format that cannot be searched. After YouTube cut long-form recommendations 80%, the archive only compounds when audiences can query it directly.
- [RAG failure points are an architecture problem, not a model problem](https://www.trickywombat.ai/signals/rag-seven-failure-points): RAG sits under 51% of enterprise AI, yet 80% of projects are projected to fail. A NAACL 2025 study found chunking configuration influences retrieval quality as much as the embedding model. The controlling variable is your data pipeline, not your model.
- [Your enterprise chatbot is only as good as the knowledge behind it](https://www.trickywombat.ai/signals/chatbot-with-knowledge): Vodafone moved first-time resolution from 15% to 60%. The enterprise chatbot knowledge base, not the model, decides whether your AI understands your company.
- [RAG beats fine-tuning for most enterprise use cases](https://www.trickywombat.ai/signals/rag-vs-fine-tuning): Discover why retrieval-augmented generation (RAG) outperforms fine-tuning for most enterprise AI use cases, boosting accuracy, control, and scalability.
- [Solving the Enterprise AI Build-Buy Dilemma](https://www.trickywombat.ai/signals/rag-build-vs-buy): Enterprise AI use cases flipped from 53% bought to 76% bought in a single year. Buying clears the deployment hurdle. Only the 6% with mature data foundations earn real returns on either path.
- [Harness engineering is the missing discipline in enterprise AI adoption](https://www.trickywombat.ai/signals/harness-engineering): The AI model is the brain, but you need more than brains; you need a harness to create true value.
- [How people use AI matters more than which AI they use](https://www.trickywombat.ai/signals/why-design-matters-for-ai): Organizations spending identical amounts on identical AI models see returns ranging from 2% to 300%. The variable is how the product is designed, not which model powers it.
- [What exactly IS prompt engineering?](https://www.trickywombat.ai/signals/what-is-prompt-engineering): Prompt engineering is where AI results start, not where they peak
- [How to roll out AI implementations](https://www.trickywombat.ai/signals/technology-roll-out): Are there techniques that high-performing organizations use to roll out their AI implementations? Lets explore finding a special recipe that all organizations can strive for.
- [The real benefits of adding an AI chatbot to your website.](https://www.trickywombat.ai/signals/benefits-of-ai-chatbot): Companies with strong knowledge bases achieve 75% AI resolution rates and 369% ROI. The 39% that fail almost always have an infrastructure problem, not a model problem.
- [Prompt engineering is only the first step to create great AI](https://www.trickywombat.ai/signals/prompt-engineering): The first five hours of prompt work produce a 35% accuracy gain. The next 60 add only 6%. The best organizations treat prompt engineering as only the first step.
- [Context engineering](https://www.trickywombat.ai/signals/context-engineering): A well engineered pipeline matters. The gap between relevant response and poor answers is not a model problem. A smaller AI with well-engineered context outperforms a much bigger model without it. Context engineering, the discipline of designing what an AI sees before it responds, explains the gap.
- [Your AI support bot isn't stupid](https://www.trickywombat.ai/signals/bot-isnt-stupid): Peer-reviewed research shows the same LLM swings from 50% to 87% accuracy based solely on retrieval pipeline design. Yet 95% of enterprise AI pilots deliver zero P&L impact. The model was never the bottleneck.
- [What is the ROI for AI customer service?](https://www.trickywombat.ai/signals/what-is-the-roi-for-ai-customer-service): Klarna's chatbot projected $40M in profit improvement. That didn't materialize, so they started rehiring human agents within a year.
- [The Real AI Chatbot Implementation Timeline](https://www.trickywombat.ai/signals/chat-implementation-timeline): Gartner found 85% of CS leaders will pilot AI chatbots in 2025 but only 5% have deployed one. The bottleneck isn't the platform, it's the knowledge infrastructure underneath it.
- [Good knowledge = good engineering](https://www.trickywombat.ai/signals/good-engineering-knowledge-pipeline): Most AI pilots fail not because the models are wrong but because engineering data is unclassified, ungoverned, and full of gaps. A problem that naive AI accelerates, not solves.
- [AI in legal practice is broken](https://www.trickywombat.ai/signals/ai-in-legal-practice-is-broken): 74% of lawyers cite AI accuracy as their top concern. Legal AI tools hallucinate 17-33% of the time, even on Westlaw and Lexis. Your data pipeline is the fix.
- [AI Slop is the New Spam](https://www.trickywombat.ai/signals/ai-slop-is-new-spam): AI slop is generic AI output that sounds confident but says nothing. 53% of consumers distrust AI search results. Learn why it happens and how to fix it.

## Technical Articles

- [AI Engineering Disciplines](https://www.trickywombat.ai/what-we-do/ai-engineering-disciplines): AI engineering is a seven-discipline stack that determines AI quality. Prompt engineering is only one. Here's what the other six are and why they matter more.
- [Query intent](https://www.trickywombat.ai/what-we-do/query-intent): Query intent classification cuts RAG hallucination from 40% to under 5% and reduces LLM API costs by up to 95%. Here's how the architecture works.
- [Prompt Rewriting](https://www.trickywombat.ai/what-we-do/prompt-rewriting): Over 60% of RAG errors start before the LLM runs. Query rewriting techniques improve retrieval quality by 15-45% across independent benchmarks.
- [Cognitive Resonance](https://www.trickywombat.ai/what-we-do/cognitive-resonance): Why matching words was never the same as matching meaning, and what the shift to concept-level retrieval changes for teams that depend on their own data
- [User Memory](https://www.trickywombat.ai/what-we-do/user-memory): RAND data shows 80% of AI projects fail from infrastructure gaps. LLM memory layers cut token costs by 90%, but extraction pipelines still hit only 30-70% accuracy.
- [Agglomerative clustering](https://www.trickywombat.ai/what-we-do/agglomerative-clustering): Agglomerative clustering groups related chunks between retrieval and generation, giving LLMs structured context instead of ranked fragments.

## Pages

- [Web Ingestion](https://www.trickywombat.ai/what-we-do/web-ingestion): How Tricky Wombat ingests websites: URL mapping, JavaScript rendering, sitemaps, and Watch freshness that keeps your knowledge current.
- [Enterprise Search](https://www.trickywombat.ai/enterprise-search): Knowledge lives in every department. Engineering docs, sales playbooks, HR policies, product specs. Your people need answers that cross those boundaries. Connected Search finds them.
- [Team Knowledge](https://www.trickywombat.ai/search-small-teams)
- [Technical Discovery](https://www.trickywombat.ai/technical-discovery)
- [Services](https://www.trickywombat.ai/services)
- [Pricing](https://www.trickywombat.ai/price)
- [What We Do](https://www.trickywombat.ai/what-we-do)
- [Content Creators](https://www.trickywombat.ai/creators)
- [AI model credits](https://www.trickywombat.ai/ai-model-credits): AI model tiers (Standard, Advanced, Premium) and credits per query by plan — Starter, Silver, Gold, and Enterprise.
- [Process Mining](https://www.trickywombat.ai/process-mining): Surface how work runs across SOPs, tickets, and tribal knowledge. Tricky Wombat Process Mining helps enterprises see process gaps and automate what should be automatic.
- [Teachers](https://www.trickywombat.ai/teachers): Turn your Kajabi, Circle, and course library into a searchable knowledge base. Students get answers from your lessons, modules, and materials, with links back to the source. Built for the full course, not one lesson at a time.
- [Thought Leaders](https://www.trickywombat.ai/thought-leaders): Turn your books, Substacks, TED talks, podcasts, and guest articles into one searchable knowledge base. Answers cite your words across formats, not a generic chatbot paraphrase.
- [YouTubers](https://www.trickywombat.ai/youtubers): Turn your YouTube channel into a searchable knowledge base. Aggregate ideas across playlists, map entity relationships, and mine processes from your tutorials. Built for the full channel, not one video at a time.