AI SEARCH FOR TECHNICAL TEAMS
The spec exists. It's buried in a wiki your team forgot.
Tricky Wombat connects your engineering team's repos, wikis, Confluence spaces, Slack threads, and internal tools into a single AI search layer built for technical precision. An engineer asks a question. The system returns a cited answer drawn from across your technical knowledge base. One search bar. Every technical source. Sourced answers in seconds.
WHY TECHNICAL SEARCH FAILS
Your engineering team documents everything. Finding it is the problem.
Engineering teams produce more documentation per person than any other department. Architecture decision records, API specs, runbooks, incident postmortems, deployment guides, onboarding docs, design reviews, Slack threads with critical context. The information exists.
It lives in six or more tools. The ADR is in Confluence. The implementation is in GitHub. The decision context is in a Slack thread from nine months ago. The person who wrote the runbook transferred to another team. Keyword search returns forty results, none of which answer the actual question. So engineers do what they have always done: interrupt the person most likely to know.
Tricky Wombat connects every technical knowledge source into a single retrieval layer. When an engineer asks "What auth provider does the payments service use?", the system pulls from the architecture doc, the deployment config, and the integration spec in one response. The engineer who wrote the original doc does not need to be online, on the team, or even at the company.
ENTERPRISE-GRADE INFRASTRUCTURE
HOW IT WORKS
Answer quality is determined before the model generates a single token
Technical search vendors pitch model intelligence as the differentiator. Pick the most capable LLM. Plug in your repos. Trust the output. That approach fails for technical queries because the model is the last 5% of answer quality. The other 95% is determined by what reaches the model: how the query is classified, how sources are selected, how technical context is assembled, and whether the result is verified before your engineer sees it.
Tricky Wombat controls every stage of that pipeline. Each step is an independent engineering problem, and we treat it like one.
Classify the query
A factual lookup ("What auth provider does service X use?") needs different retrieval than a synthesis across twenty documents ("How has our API versioning strategy evolved over the last year?"). The system classifies the query type and routes it to the retrieval strategy most likely to return a precise technical answer.
Retrieve with technical precision
More documents in the context window make answers worse, not better. Hybrid search and reranking tuned for technical content (code, specs, ADRs, postmortems) return fewer, higher-quality results. The goal is the right three documents, not the first forty keyword matches.
Assemble scoped context
Retrieved documents are compressed, deduplicated, and scoped to the query's technical domain. Stale drafts and redundant passages are stripped. The model receives what it needs for this specific question and nothing else.
Generate against engineering standards
Guardrails are set before the model runs: cite sources, stay within the evidence, flag uncertainty. No hallucinated API signatures. No invented configuration values. The pipeline defines what a good technical answer looks like. The model follows.
Score the result and improve the pipeline
Each answer is scored for faithfulness, relevance, and completeness. Results that fall short are caught before your engineer sees them. Scoring data feeds back into retrieval tuning, context assembly, and ranking. The system improves with use, not just with more data.
HOW TECHNICAL KNOWLEDGE CONNECTS
Technical documents are not independent files. Your search should not treat them that way.
An architecture decision record references the RFC that proposed it. The RFC references the incident that prompted the design change. The incident postmortem references the runbook that failed during response. The runbook references the deployment configuration that drifted from its documented state.
Most search tools flatten this structure into a keyword index. They treat every document as independent text. The relationships between documents, the ones your engineering team built through months of design work and incident response, disappear.
Tricky Wombat's retrieval layer maps those relationships. Related technical content stays grouped so a question about a service's authentication model surfaces the architecture decision, the implementation PR description, the integration test rationale, and the security review notes together, even when they were authored by different engineers in different tools. The result is answers drawn from the right cluster of your technical knowledge, not a keyword-matched list limited to one repo or one wiki space.
Every answer cited5s answers
Cited answers in five seconds. Every response links to the source document, commit, or wiki page it drew from.
Engineering time recovered2.4hrs/day
Engineers spend 2.4 hours per day searching for technical information across systems. Technical Discovery gives that time back to building.
Live in1Week
Connect your technical knowledge sources and start searching across your engineering org.
Enterprise Security
Your data is protected at every layer
Every component in Tricky Wombat's stack is independently audited. Documents use AWS which is SOC 2 Type II certified and HIPAA-eligible. Vector embeddings live in SOC 2 Type II certified and GDPR-ready vector stores. The application layer runs on Vercel with automatic HTTPS and DDoS protection. Every service in the stack is one your engineering team has already vetted.
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Encryption at
Every Layer
AES-256 encryption at rest across S3, DynamoDB, and Pinecone. TLS 1.2 in transit. Automatic HTTPS via Vercel.
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Vendor
Audited Infrastructure
Every service in the stack is independently SOC 2 Type II certified. DynamoDB is ISO 27001 and HIPAA-eligible. Pinecone is GDPR-ready.
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Zero Training on
Your Data
No component of the pipeline trains on your data by default. Your information is used to answer your queries and nothing else.
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Vanta-Managed
Compliance
SOC 2 Type II and ISO 27001 certification in progress, managed through Vanta's continuous compliance monitoring platform.
SOC 2 in Progress
In progress via Vanta
ISO 27001
In progress via Vanta
AES-256 Encrypted
Data encrypted at rest
TLS 1.2 in Transit
Data encrypted in motion
Zero Data Retention
Your data stays yours
Built on AWS
Enterprise-grade infrastructure
TECHNICAL DATA SOURCE COVERAGE
Other search tools connect to apps. Tricky Wombat reads the files inside them.
Technical search vendors measure coverage by the number of API connectors in their catalog. Tricky Wombat takes a different approach. The platform connects to your Git repos, wikis, Confluence spaces, Notion workspaces, Slack channels, and internal documentation systems through direct integrations. For the files inside those systems, Apache Tika provides parsing and text extraction across more than 1,000 formats: Markdown, YAML, JSON, XML, PDFs, spreadsheets, code files, log outputs, Jupyter notebooks, and hundreds of specialized technical formats most search tools silently skip. The result is search coverage that goes deeper than a connector count and reaches into the actual content your engineers produce.
What teams are finding
“Finding what I'd said on a given topic across my books, talks, and interviews used to mean hours of manual searching. Now my team asks a question and gets a sourced answer pulled from everything I've published. Content I forgot I created surfaces exactly when it's relevant.”
Chip Conley
Founder of MEA & Joie de Vivre Hotels, Strategic Advisor to Airbnb
“My practice has decades of financial planning content across client engagements, mentorship sessions, and published frameworks. Tricky Wombat connects all of it. When I need to pull together ideas from across my body of work, the search finds the relevant pieces and brings them together in one answer.”
Ron Nakamoto
Founder of True Wealth Mentorship, Certified Financial Planner, Financial Coach
QUERY INTELLIGENCE
A lookup query and a synthesis query need different retrieval strategies. Most search tools treat them identically.
A factual question ("What is the SLA for the payments service?") and a synthesis question ("What were the common root causes across our last five P1 incidents?") require fundamentally different retrieval approaches. Most search tools treat them the same way and fail at one or both.
Tricky Wombat classifies the query before retrieval begins. A factual question gets precision retrieval from the most current authoritative source. A synthesis question pulls from multiple documents across repos, wikis, and incident logs, then assembles a coherent technical summary. When a query is vague ("How does auth work?"), automated prompt rewriting refines it behind the scenes to target the specific service, version, or context the engineer most likely needs.
Your engineering team does not need to learn query syntax or remember which wiki space holds the answer. The system adapts to how engineers actually ask questions.
Every technical team searches differently. Build AI search that works across all of them
Your engineering organization has teams with their own repos, wikis, tooling, and terminology. The answers your engineers need cross team boundaries daily.
But technical search tools are built to index text generically, not to understand how engineering knowledge flows between teams.
Tricky Wombat builds a context pipeline across your engineering knowledge, so every answer reflects how your teams collaborate, what your documentation says, and what your engineers are really asking.
HOVER TO EXPLORE
Infrastructure answers without interrupting the architect
Infrastructure answers without interrupting the architect
- Infrastructure decisions, deployment configurations, and service dependency maps live across Confluence, GitHub, Terraform repos, and internal wikis. No single engineer knows where everything is.
- When the platform team needs to understand why a service was configured a certain way, they interrupt the person who set it up. If that person left the team, the answer takes days instead of minutes.
- Technical Discovery surfaces infrastructure decisions, configs, and dependency documentation together so your platform team builds on documented knowledge instead of tribal memory.
Find what happened last time this broke
Find what happened last time this broke
- Incident postmortems, runbooks, and on-call procedures are created during or after high-pressure events. They are written once, filed, and rarely found again when the next incident hits.
- During an outage, the SRE team needs to know whether this failure mode has happened before, what the root cause was, and what the runbook says. Keyword search returns noise. Slack search returns fragments.
- Technical Discovery connects postmortems, runbooks, and incident timelines so your SRE team finds the relevant operational history in seconds, not after the incident is already over.
Audit-ready answers from engineering and legal
Audit-ready answers from engineering and legal
- Security audit findings, vulnerability assessments, and compliance evidence live across engineering systems, legal repositories, and GRC platforms. Pulling together the documentation for a single audit question can take hours.
- Security teams need to trace a control from policy to implementation to evidence. That trail crosses engineering wikis, code repos, and compliance platforms that do not talk to each other.
- Technical Discovery connects security and compliance documentation across systems so your security team answers audit questions with sourced evidence instead of manual document hunts.
Understand why a decision was made 18 months ago
Understand why a decision was made 18 months ago
- Architecture decision records, RFCs, product specs, and roadmap rationale are the institutional memory of your engineering organization. They document not just what was built, but why.
- When a team revisits a design decision from a year ago, the original context matters. Why was option B chosen over option A? What constraints existed? What has changed since? That context is scattered across docs, PRs, and Slack threads.
- Technical Discovery connects ADRs, RFCs, product specs, and discussion threads so your architecture decisions are understood in full context, not reimagined from incomplete information.
Everything You Want to Know
Frequently Asked Questions
Every technical search evaluation raises the same questions.
- Will this actually work against our specific technical documentation?
- How is this different from the search tools we already have?
- Does the architecture produce accurate results for precise technical queries?
These are the questions engineering leaders ask most, and the answers reflect how we think about the problem: retrieval infrastructure determines answer quality, not the model, not the connector count, not the vendor's logo.
Each answer below is written the way we would answer it in a first technical conversation.
Tricky Wombat works best for engineering organizations at companies with 100 to 5,000 employees where technical knowledge fragmentation is a daily operational drag. At this scale, documentation lives across multiple repos, wiki platforms, communication tools, and the heads of engineers who have moved teams or left the company. The big enterprise search vendors optimize for Fortune 500 deployments. Companies below that threshold get a generic index configuration and deprioritized support.
Tech-forward larger organizations that want a context-first approach are a strong fit too. So are engineering teams inside large companies that need to solve a specific technical search problem without waiting for an enterprise-wide procurement cycle.
The difference is the relationship. Tricky Wombat builds the retrieval pipeline around your specific documentation structures, query patterns, and engineering workflows. The people who built the product are the same people configuring your system.
The platform uses Apache Tika for text extraction across more than 1,000 file formats. That includes Markdown, YAML, JSON, XML, code files, Jupyter notebooks, PDFs, spreadsheets, and hundreds of specialized formats that most search tools skip silently.
Beyond format support, the ingestion pipeline understands technical document structure. An API spec is not the same as a meeting transcript, and the system does not treat them interchangeably. Technical content is chunked and indexed in ways that preserve the relationships between sections, parameters, and references. When an engineer searches for a specific API endpoint, the result includes the relevant specification section, not a keyword match from a paragraph that happens to mention the endpoint name.
Elasticsearch and Algolia are search engines. They index text and return keyword matches. Building a useful technical search experience on top of them requires your engineering team to design the ingestion pipeline, tune the ranking, handle query understanding, manage index freshness, and maintain the infrastructure. Most teams underestimate this investment. They get a working prototype and then spend months trying to make it produce useful results for the queries engineers actually ask.
Tricky Wombat is the complete retrieval pipeline, not a component your team builds on. Query classification, hybrid search, reranking, context assembly, answer generation, and quality scoring are all handled by the platform. Your team connects data sources and starts asking questions. The infrastructure investment that makes search useful is the product, not a project you assign to your platform team.
Yes. The platform connects directly to Git repositories, Confluence, Notion, Slack, Google Drive, and standard cloud storage systems. For CI/CD and infrastructure tooling, the platform ingests documentation, configuration files, and output artifacts through the same pipeline.
The key distinction is what happens after connection. Most search vendors connect to a source and index whatever text they find. Tricky Wombat's pipeline parses technical content with format awareness, chunks documents to preserve structural relationships, and indexes them for semantic retrieval. A Terraform module, a Kubernetes manifest, and a Confluence page about the same service are connected in the retrieval layer even though they live in different systems. Your context layer reflects your engineering organization right now, not the last time a scheduled crawl ran.
Answer accuracy for technical queries depends on two things: whether the right source documents reach the model, and whether the model stays within those documents when generating the response. Tricky Wombat controls both.
The retrieval pipeline uses hybrid search with reranking to surface the most relevant technical content, not the most keyword-dense content. Context assembly strips noise and scopes the model's input to the query's technical domain. Generation runs against defined quality rules: cite sources, stay within evidence, flag uncertainty. Every answer is scored for faithfulness, relevance, and completeness before your engineer sees it. Results that fall short are caught and flagged. The system does not guess when it does not have a confident answer. It tells you.
Most engineering teams are running technical searches within five to seven business days. Connect your documentation sources (repos, wikis, Slack, cloud storage) and the context pipeline begins working immediately. It does not depend on months of behavioral data to produce relevant answers.
A typical engagement starts with a direct conversation about your engineering team's documentation landscape and search pain points. From there, Tricky Wombat connects to your sources, ingests and indexes your technical content, and gives your team a working system to test against real queries.
Every customer gets direct onboarding from the people who built the platform. There is no implementation team handoff, no multi-week scoping phase, and no waiting for the system to learn before it starts producing useful results.
Run a search against your technical docs
Point it at a repo, a wiki, or a Confluence space. Ask a technical question. See what comes back. One call. No commitment required.
Book a 20-minute fit callTechnical discovery across your engineering knowledge.
Plans start at $25/seat.