Your team's knowledge, one question away

Tricky Wombat works with companies of any size that need better answers from their data. The platform performs especially well for companies in the 500 to 2,000 employee range, where data fragmentation is real but the big enterprise vendors treat you as an afterthought. Most AI search vendors optimize for Fortune 500 accounts spending mid-seven figures annually. Companies below that threshold get generic configurations, deprioritized support, and a spot as customer 4,312 on a renewal list.

Tech-forward larger organizations that want a more hands-on, context-first approach are a strong fit too. So are small but nimble teams inside large companies that need to solve a specific search problem without waiting for an enterprise-wide procurement cycle.

The difference is the relationship. TW builds the context pipeline around your specific data structures, query patterns, and organizational needs regardless of headcount. The people who built the product are the same people configuring your system. That level of direct engagement is the point, whether you are a 500-person company, a 5,000-person organization, or a 20-person team inside one.

Tricky Wombat's infrastructure is built on services that meet enterprise security standards at every layer of the stack.

Your documents and source data are stored in AWS S3 with server-side AES-256 encryption at rest and TLS encryption in transit. Metadata and application state live in DynamoDB, which provides the same encryption standards within AWS's SOC 2 Type II, ISO 27001, and HIPAA-eligible environment. Vector embeddings used for semantic search are stored in Pinecone, which is SOC 2 Type II certified, GDPR-ready, and HIPAA compliant, with AES-256 encryption at rest and TLS 1.2 in transit. The application layer runs on Vercel, which provides automatic HTTPS, DDoS protection, and SOC 2 Type II compliance.

No component of the pipeline trains on your data. Your information is used to answer your queries and nothing else. Access controls follow the permissions your organization already has in place, so users only see results from documents they are authorized to access.

The short version: your data is encrypted everywhere it sits and everywhere it moves. The services that store it are independently audited. And none of it is used for anything other than finding better answers for your team.

Large vendors offer long lists of connectors (most of which are irrelevant to any given customer), big implementation teams, and brand recognition that makes procurement comfortable. Their architecture is designed to solve a generic problem at scale. Your implementation gets the same configuration as every other customer.

That tradeoff has a cost. A connector that links to a data source is not the same as a connector that understands how to ingest, chunk, and prepare that data for accurate retrieval. Most companies use a fraction of the connectors available to them, and the ones they do use still produce shallow indexing, silent truncation of large files, and missed industry-specific context. When a platform is built to serve thousands of companies simultaneously, it optimizes for breadth over depth. The pattern is consistent: scaling the business does not scale the answer quality.

Tricky Wombat builds the context pipeline around your specific data structures, query patterns, and organizational needs. The platform runs on AWS with Vercel, the same enterprise-grade infrastructure that large vendors use, and the same infrastructure that scales instantly and meets the standards your organization requires.

The difference is what sits on top of that infrastructure. The people who built the product are the same people configuring your system. Implementation decisions happen in days, not weeks. When you identify a capability gap, that conversation goes directly to the people who can act on it. You are not a ticket in a queue. You are a partner with direct access to the team that built the system.

We connect to standard enterprise data sources including cloud storage, document management systems, internal wikis, and communication platforms. The platform is built on AWS with Vercel, uses Apache Tika to connect to over 1,000 different file types for text and metadata extraction (including PDF, Microsoft Office: DOC, XLS, PPT), HTML, Image files (PNG. JPG, TIFF, GIF), OCR, Audio (MP3, WAV), Video (MP4, Quicktime) (see Apache Tika Supported Document Formats for a full list) and scales with your data volume.

Connection is the easy part. Where most vendors fail is what happens after connection. Silent truncation of large files without signaling errors. Images and diagrams stripped instead of understood. Stale indexes that serve answers from last week's crawl while your data changed yesterday.

Tricky Wombat’s data layer monitors connected sources continuously. When a document changes, the pipeline re-processes it. When a new document appears, it enters the index without waiting for a scheduled crawl.

The ingestion system handles the cases other platforms handle badly or not at all: a 40 MB regulatory filing where the answer sits on page 800, handwritten details on a scanned document, or a footnote that references another manual entirely.

Your context layer reflects your organization right now, not the last time a crawl ran.

Most enterprise AI search evaluations run on demo data with scripted queries. That tells you what the product looks like. It does not tell you whether it works against your actual information.

Tricky Wombat runs a pilot against your real data, your actual queries, and your team's workflow before you commit to anything. The question worth asking during an evaluation is not "how many companies use this?" The question is "how well does this work against my data, my queries, and my team's actual workflow?"

We are confident enough in that answer to let you test it. If you are evaluating us alongside vendors with bigger websites and longer customer lists, you should.

Start a conversation, describe the problem, and we will tell you whether the fit is worth both sides' time.

Most enterprise AI search vendors require a sales-led evaluation, a scoping process, a multi-week implementation, and months of interaction data before the system starts producing useful relevance. A fully integrated deployment with a large vendor can take anywhere from 30 days to several months depending on data complexity, permission mapping, and integration depth. Some platforms need dedicated developer time for configuration, and the ML models behind them do not improve until they have accumulated enough usage data to learn from.

Tricky Wombat works differently. Connect your data sources and start asking questions. The context pipeline begins working immediately because it does not depend on months of behavioral data to produce relevant answers. The architecture is built to understand your documents, rank them correctly, and assemble focused context from the first query.

A typical engagement starts with a direct conversation about your data and your problem. From there, Tricky Wombat connects to your sources, ingests and indexes your documents, and gives your team a working system they can test against real queries.

Every customer gets white-glove onboarding. The people who built the platform are the same people running your setup, answering your questions, and tuning the pipeline to your data. The same way a Four Seasons remembers how you take your coffee, we learn your data structures, your query patterns, and your team's actual workflow. That level of attention stays with you. There is no implementation team handoff, no multi-week scoping phase, and no waiting for the system to "learn" before it becomes useful.