An AI-driven enterprise search solution that connects every data source you already have

Every enterprise search project begins with the same ambition: make our information findable. And every enterprise search project runs into the same wall: the information lives in too many places, in too many formats, behind too many systems. Product data sits in an ERP. Knowledge base articles live in a wiki. Policy documents exist as PDFs on a shared drive. Customer data is locked inside the CRM. Internal procedures are scattered across intranet pages that nobody has updated since 2021. The search problem isn't building a search engine — that technology has existed for decades. The problem is connecting the search engine to all the places where the information actually lives, and keeping those connections current as systems change.

Traditional enterprise search platforms attack this problem with connectors — pre-built integrations that crawl each system, extract content, transform it into a common schema, and index it in a central search cluster. On paper, it sounds clean. In practice, each connector requires configuration, authentication, field mapping, and ongoing maintenance. The SharePoint connector needs API credentials and handles permissions differently from the Confluence connector. The database connector requires a SQL query that defines which rows become searchable documents. The file system connector needs to know which directories to scan and which file types to read. Before a single search query is served, an engineering team has spent months building, testing, and deploying an indexing pipeline that touches every data source in the organization. And when a source changes its schema, moves to a new platform, or adds a new content type, the pipeline breaks and the search results degrade until someone fixes it.

The maintenance burden is where traditional enterprise search becomes genuinely expensive. Indexing pipelines don't maintain themselves. When the product team migrates from one catalog platform to another, the search connector needs to be rebuilt. When HR launches a new internal wiki, someone needs to add a new connector and configure it. When the data warehouse adds a new table, the SQL query needs updating. Each change is manageable in isolation, but enterprises don't change one thing at a time — they change everything, constantly, and the search infrastructure team is always playing catch-up. The result is a search experience that's perpetually incomplete, perpetually stale in some areas, and perpetually surprising in its gaps. Employees learn not to trust it. They bypass the search bar and ask a colleague instead. The search investment delivers diminishing returns.

Asyntai's AI-driven enterprise search solution approaches the data connection problem differently. Instead of building and maintaining dozens of specialized connectors, it provides four generalized data channels that cover the full spectrum of enterprise information — and each channel is designed to be connected in minutes, not months. The Real-Time Data Feed handles structured data: any URL that returns JSON, CSV, or an API response becomes a live data source. Auto-crawl handles web-based content: point it at a domain and it reads every accessible page. Document upload handles files: PDFs, Word documents, spreadsheets, and internal guides that exist as downloadable files. Custom Tools handle live system queries: connect the AI to your APIs so it can query CRM, ERP, order management, or any other system in real time during a conversation.

The Real-Time Data Feed is the channel that changes how enterprises think about making structured data searchable. Traditionally, making a product catalog searchable means building an indexing pipeline: extract data from the source system, transform it into a search-engine-friendly schema, load it into the index, and schedule regular re-indexing to keep it current. With Asyntai, you provide a URL that returns your data. That's it. The AI reads the feed, understands the structure, and makes every item searchable through natural language. When a visitor or employee asks a question about a product, the AI searches the feed — not by keyword matching, but by understanding what the person is looking for and reasoning about which items match. A product catalog, a resource directory, an employee directory, a course catalog, a parts inventory — any structured dataset that can be served as JSON or CSV becomes a searchable, conversational data source.

The feed's capacity scales with enterprise needs. The Standard plan supports up to 200,000 characters of feed data, sufficient for catalogs of a few hundred items with full descriptions. The Pro plan extends this to 10,000,000 characters — roughly 25,000 items with complete metadata, descriptions, prices, and image URLs. For organizations with larger datasets, multiple feeds can cover different data domains. And because the AI reads the feed live on each relevant query, changes to the source data reflect immediately. A price update, a stock change, a new item added to the catalog — the next query sees the current data, not yesterday's index. This eliminates the stale-data problem that plagues traditional enterprise search, where the gap between when data changes and when the search index reflects that change creates a trust deficit that undermines the entire search experience.

Auto-crawl covers the vast category of enterprise information that lives on web pages — corporate websites, intranet portals, knowledge bases, help centers, HR policy pages, department wikis, FAQ collections, and any other content that exists as HTML accessible through a URL. The AI crawls every page it can reach from the starting domain, reads the content, and makes it searchable. There's no sitemap to configure, no page-by-page selection, no content-type filtering. If a page exists and is accessible, the AI can find and use the information on it. Updates follow a regular crawl schedule, typically daily, so changes to your web-based content propagate within 24 hours.

What makes auto-crawl particularly valuable for enterprise search is that it captures the long tail of organizational knowledge — the information that lives on pages nobody remembers exist. Every organization has an intranet full of pages that were created for a specific purpose, served their audience, and then disappeared from collective memory. The benefits enrollment page from last year. The disaster recovery procedures document. The vendor onboarding checklist that the procurement team put together. These pages still exist, still contain valuable information, but they're effectively invisible because nobody remembers the URL and the intranet's own search is too weak to surface them. Auto-crawl makes this invisible knowledge findable again, because the AI reads every page and can answer questions from any of them.

Document upload handles the information that never made it to a web page — PDFs that live on shared drives, Word documents that circulate via email, training manuals that exist as downloadable files, compliance documents that were created by legal and distributed as attachments. This content is often the most authoritative information in an organization, yet it's the hardest to search because it exists as files, not as indexed web content. Traditional enterprise search needs a file system crawler and a content extraction pipeline to make these documents searchable. Asyntai needs a file upload. Drop the PDF into the knowledge base, and the AI reads it, understands it, and can answer questions from it. When someone asks "what's our maximum liability under the vendor agreement with Acme Corp," the AI finds the relevant clause in the uploaded contract and quotes it — a query that would return zero results in a keyword search because nobody would think to search for the exact legal phrasing.

Custom Tools represent the most powerful data connection channel because they provide live access to enterprise systems during a conversation. The other three channels — feed, crawl, upload — involve the AI reading and understanding data that's been provided to it. Custom Tools flip the model: the AI reaches out to your systems in real time, queries them with specific parameters, and uses the response to answer the current question. This is the difference between searching indexed data and querying live systems. An employee asks "how many open support tickets does Acme Corp have?" The AI doesn't search a cached database — it calls your ticketing system's API, retrieves the current count, and responds. The answer is always current because it came from the live system, not from an index that might be hours or days behind.

Custom Tools work by defining an API endpoint, the parameters the AI should send, and a plain-language description of what the tool does. The AI reads the description, understands when to use the tool based on the conversation, and constructs the API call with the right parameters extracted from the user's question. You can connect as many Custom Tools as your workflows require — one for the CRM, one for the order management system, one for inventory, one for the HR database. Each tool gives the AI access to a different system, and the AI decides which tools to use based on what the person is asking. A single conversation might involve the AI searching the knowledge base (from auto-crawl), looking up a product in the data feed, and then calling a Custom Tool to check inventory — all without the person knowing or caring which data source provided each piece of information.

The combination of these four channels solves the fundamental enterprise search problem — unified access to distributed data — without the infrastructure burden of traditional solutions. There are no indexing pipelines to build. No schema mappings to maintain. No connectors to configure and monitor. No search cluster to scale. The AI handles relevance, ranking, and result presentation without manual tuning. When a new data source needs to be added, it takes minutes: paste a feed URL, point the crawler at a new domain, upload a document, or connect a new Custom Tool. When a data source changes, the AI adapts: feeds are read live, crawled content refreshes daily, uploaded documents can be replaced, and Custom Tool endpoints can be updated without downtime.

The search experience itself is fundamentally different from what traditional enterprise search delivers. Traditional platforms return a ranked list of links. The user clicks through each link, reads the page, decides if it answers their question, and either finds what they need or goes back to try a different result. It's the same paradigm as web search engines from 2005, transplanted into the enterprise. Asyntai replaces this paradigm with direct answers in natural language. The AI reads the question, searches across all connected data sources, and responds with the answer — not a list of documents that might contain the answer, but the answer itself, with enough context to be useful and enough specificity to be trustworthy.

When the search results involve products, resources, or other structured items, the AI renders them as Dynamic Product Cards — visual cards that display the item's image, name, key details, and an action button, right inside the conversation. Multiple results appear as a swipeable carousel. This visual presentation works for any type of structured data in the Real-Time Data Feed, not just physical products. A course catalog becomes browsable cards with course name, instructor, schedule, and an "Enroll" button. An internal resource directory becomes cards with resource name, description, and a "View Details" button. A parts inventory becomes cards with part number, specifications, stock level, and an "Order" button. The visual format turns enterprise search from a text-based question-and-answer exchange into a browsable, visual discovery experience.

Multi-site isolation ensures that each department, brand, or division gets its own search scope. An enterprise with multiple business units can deploy separate Asyntai instances — each with its own data sources, its own knowledge base, its own Custom Tools — so that the sales team's search doesn't return results from the engineering team's internal documentation, and the customer-facing widget for Brand A doesn't show products from Brand B. Each deployment is completely isolated: separate data, separate conversations, separate analytics. This solves the permissions and scope problem that makes many enterprise search deployments chaotic — where a single search bar returns results from across the organization and the user has to figure out which ones are relevant to their context.

The multilingual capability deserves attention because it eliminates one of the most expensive aspects of enterprise search at scale. Organizations operating across multiple countries traditionally need separate search indexes for each language — a French index for the French knowledge base, a German index for the German documentation, a Japanese index for the Japanese product catalog. Each index needs its own language-specific tokenization, stemming, and relevance tuning. Maintaining parallel search infrastructure across a dozen languages is a significant cost multiplier. Asyntai serves all 36 supported languages from a single deployment. The AI understands questions in any supported language, searches the same data sources regardless of the query language, and responds in the language the person used. A Japanese employee can search an English-language knowledge base and get answers in Japanese. No translated indexes required. No language-specific configuration. One deployment, every language.

The cost comparison between traditional enterprise search and Asyntai's AI-driven approach reveals the economic logic of the shift. A mid-size traditional deployment involves infrastructure costs (cloud compute for the search cluster, storage for the index), licensing fees (per-document or per-query pricing from the platform vendor), and engineering costs (the team that builds, maintains, and tunes the search infrastructure). Annual costs in the six-figure range are common, and that's before accounting for the opportunity cost of the engineering time spent on search infrastructure instead of core product development. Asyntai's pricing starts at $139/month for the Standard plan and $449/month for Pro. Even at the Pro tier with maximum data feed capacity and Custom Tools, the annual cost is under $4,000 — a fraction of what traditional enterprise search platforms cost. The trade-off is customization depth: traditional platforms offer granular control over ranking algorithms, indexing behavior, and query processing. Asyntai trades that control for simplicity, speed of deployment, and zero maintenance overhead.

The organizations that benefit most from an AI-driven enterprise search solution share a pattern: their information is distributed across multiple systems, their users need answers rather than document links, and they don't have (or don't want to allocate) a dedicated search infrastructure team. This describes most mid-market and growing enterprises. They've outgrown the phase where everyone knows where everything is, but they haven't reached the scale where a dedicated search engineering team is justified. They need their product data, knowledge base, internal documentation, and CRM accessible through a single search experience — and they need it deployed this quarter, not next year. Asyntai's four data channels — Real-Time Data Feed, auto-crawl, document upload, and Custom Tools — give them exactly that: unified, conversational, AI-driven search across every data source that matters, live in days instead of months, maintained by no one instead of a team.

The shift from keyword search to AI-driven search is happening across the enterprise software landscape, and the organizations that move first gain a compounding advantage. Every day that employees spend manually searching for information — navigating to the right system, constructing the right query, scanning through results, clicking into documents, extracting the answer — is a day where that time could have been spent on the work itself. Every customer inquiry that bounces through a help center's keyword search and ends up in a support ticket is a resolution that could have been instant. Every product discovery session that stalls because the search bar didn't understand the question is a potential conversion that evaporated. AI-driven enterprise search doesn't just make search better — it makes every workflow that depends on finding information faster, because the information finds the person instead of the other way around.