We find Better
answers for you

Every answer your AI gives depends on the data behind it.

Your chat assistant loses a customer every time it gives a mediocre answer. Your enterprise search burns hours when employees can't find what they already know exists. Your audience disengages the moment your content feels generic instead of personal.

These are different problems with the same root cause: the information feeding your AI determines the quality of every response it produces.

Tricky Wombat turns that information into the engine that drives quality, accuracy, and relevance across every answer.

AI Search with Keyboard
AI Circuit with Magnifying Glass
Dashboard components

We build AI at every scale

From Large Enterprises

Team meeting with AI enhanced table

Search for Small Teams

Knowledge lives in your shared drives, chat threads, meeting notes, and scattered docs. Your team is small enough to know the answer exists . But doesn't always know where to find it. Small Team Search finds it.

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Buildings connected by data streams

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.

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Two engineers at desk

Technical Discovery

Technical knowledge lives across repos, wikis, Confluence, and Slack. API specs, architecture decisions, runbooks, incident history. Your engineers need precise answers that connect those sources. Technical Discovery finds them.

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Context First

Answer quality is determined before the model generates a single token.

Every major AI model can reason, summarize, and generate. Swap one for another and output quality shifts by single-digit percentages. Change what the model sees before it reasons, and the result changes entirely. Tricky Wombat engineers the full context pipeline: from query intent classification to retrieval, assembly, generation, and evaluation. The model is interchangeable. The pipeline is the product.

  • Your question is classified before a single document is retrieved
  • Context is assembled for this question, from this user, against this data
  • Every answer is scored, cited, and fed back into the pipeline
See it with your data
1

Classify

Query intent

2

Retrieve

Hybrid search + rerank

3

Assemble

Scoped context

4

Generate

With guardrails

5

Score

Evaluate + feedback

The model is step 4. The pipeline is the product.