Monthly retainer
Predictable low-touch coverage, re-indexing and tuning as content changes.
Enterprise Knowledge Platform for AI Agents
Build a governed knowledge layer across your organization's documents, portals, applications, and databases. Azati combines agentic retrieval, hybrid search, metadata enrichment, and grounded generation to give AI agents reliable access to enterprise knowledge.
Turn scattered enterprise knowledge, from scanned manuals and technical documents to portals, databases, and internal applications, into a governed retrieval layer that AI agents can use, with answers grounded in your source content and traceable to the original evidence.
How it worksEnterprise knowledge retrieval pilot
Fixed-scope pilot for validating retrieval quality, source grounding, and agent-readiness against a representative enterprise dataset before a broader deployment
2–4 weeks fixed scope
Rapid evaluation pilot on public web content or sample dataset to prove retrieval accuracy and response grounding before enterprise rollout
Custom Enterprise Infrastructure
Full portal, intranet, and application deployment with RBAC/AD integration, air-gapped hosting options, multi-modal OCR ingestion, and custom vector search core
See it running right now
Azati's own website runs on the same search core. You can test multilingual queries and see answers grounded in the site's existing content, with citations to the source pages.
Two structural differences: AI-accelerated delivery as the core operating model, not a layer bolted on top; and smaller, specialist-led teams with wider ownership, fewer handoffs, faster decisions.
Built for enterprise knowledge that conventional search can’t handle
Azati builds the retrieval and knowledge layer that makes this information usable by people, AI assistants, and purpose-built agents, without requiring you to move everything into a new system. Proven on patent and biomedical terminology, the densest, least forgiving content there is. If it holds up there, it holds up on your contracts, manuals, and SOPs.
- 50M+ patent and biological-sequence records processed
- 99.5% uptime SLA on managed deployments
- 98% tagging confidence in production bioinformatics search
- 27 ms average query response in a bioinformatics deployment
- 20+ years Document intelligence and search engineering
- 6 regulated industries Banking and finance, insurance, energy, oil & gas, life sciences, IP/biotech, and logistics
Measure your search accuracy
We don't quote one accuracy number for AI search in general. Search accuracy depends on the corpus, domain, query types, and quality of the underlying content, so it should be measured against your data. We'll show you the evaluation approach during the scoping call.
Schedule the scoping callThe problem worth solving
Content investment, without content findability
The strongest fit is an organization with a large or complex body of valuable content that conventional search struggles to surface, particularly when that content is distributed across websites, portals, intranets, documents, or internal knowledge bases.
Organizations accumulate case studies, SOPs, technical documentation, product specs, and internal knowledge for years, and then rely on keyword search that returns whatever page happens to contain the exact word typed in. Same root cause everywhere: search that ranks by statistical word-match and leaves the human to filter, instead of search that understands what's actually being asked.
The cost shows up in different places depending on where the content lives:
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On the website
"I searched for our GDPR case study and got a 2019 blog post instead.” Relevant content exists, but conventional search fails to surface it. Prospects may leave, while sales teams repeat information that is already published.
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On the intranet
A German-speaking employee may struggle to find relevant information when the source content is in English. An operator on a night shift may also need information buried in a scanned manual or document full of screenshots. In both cases, the problem is not necessarily missing knowledge. It is finding and interpreting the available content.
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In the app or portal
"We built a knowledge base. Support still says nobody finds the right article.” A knowledge base only reduces support effort when users can reliably locate the information they need.
The enterprise knowledge layer behind grounded AI agents
Retrieval is the foundation. Azati separates knowledge retrieval from the language model, so the system can be evaluated, controlled, and adapted independently of the model generating the answer.
The pipeline prepares enterprise content, interprets each request, selects the appropriate retrieval strategy, combines semantic and exact-match signals, reranks the evidence, and generates an answer only from the retrieved sources. Confidence thresholds provide an explicit fallback when the available evidence is insufficient.
Understand the request
Agentic intent analysis identifies the user's intent, domain, and relevant parameters, then creates a retrieval plan.
#1
Prepare the query
Terminology is normalized, domain-specific acronyms are expanded, and the query is enriched before retrieval. The resulting search plan can remain visible and editable.
#2
Retrieve from enterprise knowledge
Hybrid retrieval combines BM25 and vector search across structured and unstructured content, allowing the system to match both exact terminology and concepts expressed differently.
#3
Rerank and interpret evidence
Candidate sources are reranked for contextual relevance. The system can summarize the retrieved evidence and apply configured confidence thresholds.
#4
Ground the result
The final answer is tied to the underlying source material with citations. When evidence is insufficient, the system can return a controlled fallback instead of fabricating an answer.
#5
One enterprise knowledge layer, multiple AI applications
The retrieval layer does not have to end with a search box. The same governed enterprise knowledge can support purpose-built AI applications and agents.
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Knowledge search
Give employees, customers, or partners direct access to grounded answers across complex documentation and knowledge repositories.
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AI assistants
Add a source-grounded assistant to an existing portal, application, intranet, or field workflow without creating a separate knowledge silo.
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Purpose-built AI agents
Build agents that retrieve enterprise knowledge as part of a larger task: reviewing technical documentation, assisting support teams, navigating procedures, preparing research, or guiding users through domain-specific workflows.
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Agentic RAG
Combine retrieval, query planning, reranking, source evaluation, and LLM reasoning so agents work from controlled enterprise knowledge rather than relying only on their general training.
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Enterprise workflows
Connect retrieval to the systems and rules around the knowledge: permissions, escalation paths, approval steps, APIs, and existing business processes.
Inside the platform
Where this fits
This isn't a life-sciences tool that happens to work elsewhere. The underlying search core was proven on patent and genomic data because that's the hardest version of the problem, not because that's the only problem it solves.
Not every organization needs this. The ones that benefit share a shape: content that's valuable but scattered, hard to search by keyword, or too sensitive to hand to a public cloud AI tool. If your situation doesn't fit, we'll say so on the call.
Enterprise knowledge environments- Corporate websites and technical knowledge portals
- Customer and partner knowledge bases
- Intranets and internal knowledge repositories
- Engineering and technical documentation
- Scientific and research repositories
- Enterprise applications and operational systems
- Legacy document archives
- Structured databases combined with unstructured content
- AI surfaces: search, assistants, agents, embedded application experiences, and domain-specific workflows
Prospects who need to self-qualify before a sales call, or international visitors arriving in a language your content isn't written in. AI search returns the right case study or spec sheet, cited, on the first try. In the visitor's own language, without a translation project.
Fit signal: rich content, complex product lines, or an audience that leaves rather than calls when search fails.
Tech requirements & limitations
Said plainly, upfront, because it saves a scoping call from becoming a surprise later.
What the knowledge intelligence solution ingests
PDFs (text and scanned), web pages, DOCX/XLSX, images and screenshots (via vision/OCR, not skipped), and connectors to your document stores for the intranet/portal tier. Public website content is crawled directly; internal sources require an ingestion path you control.
What it needs from you
Access to the content (crawl access, an export, or a connector), a decision on hosting (see Configurability), and a decision on identity and permissions for portal/intranet deployments. Preserving your existing access rights (AD/LDAP, RBAC) so a shift worker can't retrieve an executive-only document is real engineering work, not a checkbox; it's scoped explicitly, not assumed included.
This is enterprise knowledge infrastructure, not a generic chatbot
The platform provides the retrieval, grounding, permissions, evaluation, and guardrails that AI applications and agents need to work with an organization's own knowledge. Search is one application of that infrastructure; it is not the entire product.
Scale
The website package targets up to ~5,000 public pages. Larger content estates, multiple domains, multi-tenant setups, or full internal knowledge bases are scoped as an extended engagement on the same platform.
Configurability
The platform is one core engine; what changes per client is configuration, not the core.
Guardrails
Off-topic refusal (it won't answer questions unrelated to your content), an allow/deny list to keep sensitive pages out of the answer surface even if they exist in the index, and prompt-injection defenses so visitors, including competitors probing your site, can't trick the system into surfacing what it shouldn't.
Confidence threshold and honest fallback
Below the delivery threshold, the system says "I didn't find a confident answer" instead of guessing, and can route the question to the right internal team by your rules, with a pre-drafted message ready to send, turning a dead end into a handled request instead of a lost visitor or a frustrated employee.
Hosting
Azati EU infrastructure, your own cloud tenant (AWS/Azure/GCP), or on-premise/air-gapped deployment for organizations that cannot send data to any external service. This is a genuine deployment option we've delivered elsewhere, not a roadmap item.
Proven in production. A secure on-premises deployment (vLLM, Open WebUI, RAG) for a confidential-search environment, with data kept within the client's controlled infrastructure. 60% faster information retrieval and a 75% reduction in search time for a confidential-search client.
Model choice
External LLM APIs by default for fastest time-to-value, with a fully self-hosted, open-source model option (zero data egress) for environments where no query or document content can leave the perimeter, ever. Self-hosted model options are available when content must remain within client-controlled infrastructure rather than being sent to a third-party AI provider.
Brand and language
Widget UI matched to your visual identity; multilingual query understanding so a question in any language finds English (or any-language) source content and answers back in the language it was asked.
Deliverable
What you receive depends on the surface, but the core is constant.
- Ingestion and indexing of the agreed content
- The four-stage query-understanding and retrieval pipeline described above
- Grounded, cited answers with a confidence-based honest-fallback path
- Configurable guardrails
- An admin view showing what was ingested, what was parsed, and what failed (so you can see the system's honesty, not just trust it)
- An analytics dashboard covering top queries, zero-result queries, and click-through
Maintenance
Azati's delivery model is build-and-operate, not build-and-hand-off. The engineers who scope the work are the same engineers who run it in production, which is also why the Maintenance options below default to Azati staying involved rather than a cold handover.
Search quality degrades as your content grows and the world changes. We say this upfront rather than let you find out after support ends. Content re-indexing runs on a webhook from your CMS or document store by default, so newly published content is searchable within hours, not weeks.
Included production support (three months on the website package) covers initial drift as real users hit edge cases. After that, three paths, all supported, none forced:
Managed engagement
Azati operates the full system under an SLA.
In-house handover
A runbook, monitoring setup, and update procedures, if your team wants to run it themselves.
Why agentic knowledge intelligence outperforms conventional search
Framed as business outcomes, not engineering trivia, because the buyer of this isn't the engineer evaluating the algorithm. It's the executive who owns the cost of people not finding answers.
Ingestion and document parsing
Index-time (continuous, before any query runs)
Content becomes searchable before anyone asks a question. PDFs (text and scanned), web pages, DOCX/XLSX, and images are parsed, OCR'd where needed, chunked, and indexed, including documents that are scans with a stamp on them, not clean text. This is the stage most off-the-shelf search tools quietly assume away, and the stage most enterprise content actually breaks on.
Query-time (per search): Query → Understand and prepare → Search core → Refine and explain → Answer
Fewer lost prospects, fewer repeated sales calls
Semantic retrieval finds the right case study even when the visitor's words don't match your page's words. The FinOps page gets found by someone searching "reducing cloud spend."
International reach without a translation project
Any language in, matching language out, against a single content estate. No parallel indices, no localization backlog blocking search from working today.
Answers your legal and compliance team can defend
Every claim is anchored to a source you own and can click into. If it's not confident, it says so. It is designed to answer from retrieved source content and return an explicit fallback when the available evidence does not meet the configured confidence threshold.
Sensitive content stays out of the wrong hands
Off-topic refusal, allow/deny lists, and prompt-injection defenses mean the search surface can't be walked into revealing what it shouldn't, by a competitor or by an employee without the right access.
A dead end becomes a handled request
When there's no confident answer, the system doesn't just say "not found". It routes the question to the right team with a drafted message, on your rules. That's the difference between a lost visitor and a captured lead, or a frustrated employee and a logged request.
You can see why it answered the way it did
Every query is traceable through the pipeline: what was retrieved, how it was ranked, what the final answer was grounded in. That's what makes this defensible in a regulated environment, not just a good demo.
Architectural boundaries and scope realism
Knowing the edge is worth more than a capability list, and it's what tells you whether to call us or someone else.
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Not a generic chatbot or standalone copilot
Azati's core offering is the enterprise knowledge and retrieval layer behind AI applications. It can power direct search and cited answers, but it can also serve as the knowledge foundation for purpose-built assistants and agents.
The difference matters: the system is engineered around your content, retrieval requirements, permissions, deployment constraints, and business workflow rather than around an open-ended chat experience.
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Not automatically the right fit for content that's already clean and centralized in one system your team searches well
If you're fully inside Microsoft 365 with a well-organized SharePoint and no data-residency constraint, a general copilot may already cover you. Azati will tell you that on the call rather than sell past it.
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Not zero-maintenance forever
Content changes, the world changes, search quality drifts. Azati says what happens after support ends instead of leaving it implicit.
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Not a replacement for a full permissions overhaul
Azati preserves and respects the access model you already have. We don't redesign your identity and access management as part of this engagement.
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Not scaled to unlimited content by default
Base packages target defined content volumes. Very large or multi-tenant estates are a separately scoped engagement on the same platform.
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Not a substitute for content strategy
If the underlying content doesn't exist or is wrong, search can only surface it faster. It can't invent what isn't there.
Get a technical fit assessment
In a 30-minute scoping call, an Azati engineering lead will look at your content, search requirements, deployment constraints, and expected scale. You'll get a practical view of whether AI search fits the problem, and what would need to be validated before implementation.
Discuss your search requirementsWhy Azati for knowledge intelligence
We don't describe search systems we could build. We run one. Azati's own website uses the same search core described on this page, alongside search and document-intelligence systems developed for regulated and data-intensive environments.
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The value is not simply adding an LLM to an existing search box
Azati combines content preparation, hybrid retrieval, reranking, grounded answer generation, citations, confidence controls, and deployment options into one search pipeline.
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That matters when the content is too complex
Distributed, sensitive, or poorly structured for conventional keyword search, and when the organization needs to see where an answer came from.
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The approach has been proven in a working proof-of-concept on the WIPO patent corpus
It combines hybrid full-text search, BM25, vector semantic retrieval, and re-ranking across every field of a patent, not just the claims.
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The same core pipeline (BM25 retrieval, AI query enrichment, AI-generated summaries) runs live in production today for sequence search
We're specific about which parts are shipped and which are proven in POC, because a patent office and a CIO ask the same question: show me the number, not the adjective.
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Patent and genomic data is a deliberately extreme proving ground
Ambiguous identifiers, inconsistent naming, dense cross-references, and zero tolerance for a missed match. Most enterprise content is easier than this, not harder. A search core Azati tuned for sequence IDs and patent claims handles SOPs, contracts, and product manuals without needing to be re-architected for "easier" data. It's already been stress-tested past that bar.
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The lineage of this search core traces back to engineering work our team did on SequenceBase
This sequence-search platform went on to power patent-expert workflows at Clarivate, with Azati as the technical delivery partner.
The same discipline, across industries
| Case | Result |
|---|---|
| Engineering-document intelligence, nine-year partnership with digatex.com | 70% faster delivery, turning dense technical archives into structured, searchable data |
| Bioinformatics semantic search | 27ms per query, 98% tagging confidence with custom word2vec model Retraining on new datasets in ~3 minutes |
| Services marketplace, NLP-powered local search | +272% search-driven activity |
| Recruitment platform, fuzzy skill matching | +27% relevant candidates surfaced |
| Inventory search, 600k+ SKUs | Sub-second similar-item fallback when no exact match exists |
| Sports-data platform, 5M+ records | 92% semantic search accuracy >70% reduction in manual oversight |
| Search stack we run | Elasticsearch · Solr · pgvector · vector DBs · word2vec · LLM embeddings · LangGraph · Llama · OpenAI · Redis · CUDA · BLAST+ |
Azati's knowledge intelligence search stack
Elasticsearch
Solr
pgvector
LangGraph
Llama
OpenAI
Redis
CUDA
Zoom in on Azati’s search intelligence expertise
BLAST Optimization for Short DNA and RNA Sequences
⚡ Pain Points We Tackled
A biotechnology company struggled to obtain meaningful BLAST results for short DNA/RNA primers under 20 bases. Missing matches and incomplete alignments slowed genomic analysis and required researchers to manually tune search parameters.
Our Approach
Azati customized the BLAST algorithm for short-sequence research by adjusting sensitivity thresholds, introducing dynamic parameter tuning, and improving filtering logic. The solution automated configuration based on query length while reducing false negatives in primer-based searches.
Applied Methods and Practices
- Optimized BLAST for short sequences:Azati modified BLAST parameters and core algorithm logic to improve sensitivity for sequences under 20 bases, helping surface relevant alignments that standard configurations could miss.
- Dynamic parameter adjustment:Implemented automatic parameter tuning based on query sequence length, removing the need for researchers to manually configure BLAST for different types of inputs.
- Improved filtering and noise reduction:Enhanced filtering logic to reduce false negatives while maintaining useful search results, helping researchers identify biologically relevant matches without manually reviewing large volumes of irrelevant output.
- Core algorithm integration:Embedded the optimizations into the BLAST codebase to create a robust solution that could be used as part of the client's ongoing genomic research workflows.
Solution Features
- 3x more relevant short-sequence matches:The optimized configuration retrieved three times more relevant matches for DNA/RNA sequences shorter than 20 base pairs.
- 55% fewer false negatives:Enhanced sensitivity and filtering reduced false negatives in primer-based searches, improving the reliability of sequence analysis.
- 40% faster analysis workflow:Automated parameter adjustment reduced manual configuration and contributed to a 40% faster end-to-end sequence analysis workflow.
- More reliable primer research:Researchers gained more consistent access to relevant short-sequence alignments, supporting primer-based genomic analysis and related biotechnology research.
- Automated sequence analysis:Dynamic configuration reduced the need for researchers to manually tune algorithm parameters for different query lengths.
Embedded AI Search for Oil & Gas Engineering Reviews
⚡ Pain Points We Tackled
Engineering teams accumulated valuable knowledge across capital projects, but finding comparable historical observations required manual searches through project records, reviews, and technical documentation. The client needed faster knowledge retrieval without introducing a separate tool or disrupting established engineering workflows.
Our Approach
Azati embedded AI-powered semantic search directly into the client's existing capital project platform. The solution combines domain-restricted retrieval, LLM orchestration, and contextual recommendations to help experts find relevant historical observations within their existing review workflow.
Applied Methods and Practices
- AI-assisted engineering search:Azati implemented semantic search that analyzes engineering recommendations, retrieves similar historical observations within the relevant discipline, and ranks them by relevance. Results connect back to the source projects, phases, activities, and remediation history.
- Embedded AI workflow integration:Experts can launch the search directly from an engineering observation and review ranked recommendations within the existing interface. This preserves established review and approval workflows while reducing context switching.
- Enterprise AI orchestration:Azati introduced an orchestration layer to manage AI requests, LLM processing, response persistence, lifecycle handling, timeouts, and controlled execution. Domain restrictions and workload controls support predictable enterprise AI operations.
- Contextual engineering decision support:Rather than returning generic search results, the platform presents ranked observations together with project stage, engineering phase, priority, remediation status, and other context needed to assess relevance.
Solution Features
- ~92% faster engineering knowledge retrieval:AI-assisted search reduced the time required to find comparable observations from approximately one hour to as little as five minutes, accelerating engineering review.
- 50,000+ historical observations searchable:Previously distributed engineering knowledge became accessible through a unified AI-assisted search experience, supporting reuse of organizational expertise across projects.
- 85% less manual search effort:Faster access to relevant historical observations reduced repetitive research and allowed reviewers to spend more time evaluating engineering decisions.
- AI adoption without workflow disruption:The capability was embedded into the existing engineering interface and delivered in four weeks, with no end-user retraining required.
- Governed enterprise AI foundation:Centralized orchestration, request tracking, response persistence, timeout handling, retention policies, and domain-restricted retrieval provided controlled AI operations within the existing enterprise environment.
Semantic Search Engine for Bioinformatics Data
⚡ Pain Points We Tackled
A bioinformatics company needed to improve the accuracy and speed of searches across large, complex scientific datasets. Inconsistent blood sample descriptions, manual tagging, synonyms, and disease-name variations made conventional search slow and unreliable, with some queries taking several minutes.
Our Approach
Azati developed a machine learning-powered semantic search engine that automatically tags blood samples, interprets unstructured queries, recognizes synonyms and terminology variations, and retrieves relevant results in milliseconds. A custom Word2Vec model, Redis caching, and RESTful microservices provided a scalable foundation that could adapt to new datasets.
Applied Methods and Practices
- Automated semantic matching:Azati developed an intelligent matching module that analyzes blood sample descriptions and automatically assigns relevant tags with around 98% confidence. This allowed the search engine to work with inconsistent or incomplete descriptions without relying on manual tagging.
- Natural language query analysis:A dedicated query analysis module converts unstructured searches into structured entities such as sample type, disease, and geography. This gives the search engine the context needed to match user queries more accurately against the underlying dataset.
- Life sciences terminology modeling:Azati trained a custom Word2Vec model on hundreds of thousands of life sciences documents to identify synonyms and semantic relationships. The approach enabled the system to recognize different expressions of the same disease, test, or scientific concept.
- High-performance search infrastructure:Redis caching was used to store preprocessed sample data in memory, while optimized search algorithms reduced query response times from several minutes to approximately 27 milliseconds.
- Scalable microservices architecture:The search and matching components were implemented as RESTful microservices deployed in the cloud. This architecture allowed individual modules to be maintained and scaled independently as datasets and processing requirements grew.
Solution Features
- Millisecond-level scientific search:Average query response time was reduced to approximately 27 milliseconds, replacing searches that could previously take several minutes.
- More consistent sample classification:Automatic tagging reached around 98% confidence, reducing reliance on manual tag selection and improving the consistency of search results.
- Faster adaptation to new datasets:Neural networks could be retrained on new datasets in approximately three minutes, allowing the search engine to adapt as the scientific data expanded.
- Better access to complex scientific data:Semantic matching enabled researchers to find relevant samples despite inconsistent descriptions, synonyms, and terminology variations.
- Scalable foundation for bioinformatics search:The combination of machine learning, caching, and cloud-based microservices provided an architecture that could support growing scientific datasets without requiring a fundamental redesign.
AI-Powered Recruitment Search Platform
⚡ Pain Points We Tackled
A New Jersey staffing agency needed to reduce the manual effort involved in sourcing and evaluating candidates across multiple websites. Resume data was fragmented and inconsistent, while incomplete profiles and complex technical skills made it difficult to identify suitable candidates efficiently.
Our Approach
Azati developed a cloud-based recruitment search platform that automatically collects and consolidates candidate data, structures incomplete resumes, predicts relevant skills, and enables fast search across enriched candidate profiles. A network of microservices supported large-scale data collection and machine learning-based candidate classification.
Applied Methods and Practices
- Automated candidate data collection:Azati built a network of dedicated web scrapers to collect resume and candidate information from multiple recruitment websites. Asynchronous processing and containerized services allowed the platform to continuously process large volumes of web data.
- Candidate data consolidation:Raw resume data was normalized and structured into comprehensive candidate profiles. Information from partial or duplicate resumes could be merged, giving recruiters a more complete view of each candidate.
- Machine learning-based skill prediction:Machine learning models classified candidates by expertise and predicted missing competencies based on known technologies, programming languages, and frameworks. This enriched incomplete profiles and expanded the pool of candidates matching specific requirements.
- Recruitment search and matching:A dedicated Search API allowed recruiters to search and filter candidates using skills, experience, predicted competencies, and other profile attributes. Enriched candidate data made it possible to identify relevant profiles more quickly.
- Scalable cloud architecture:The platform used containerized microservices and asynchronous processing to handle growing data volumes. A React-based interface provided recruiters with access to enriched candidate information and search functionality.
Solution Features
- 127% more relevant candidates identified:Machine learning-based classification, skill prediction, and enriched candidate profiles increased the pool of relevant candidates identified by recruiters.
- Candidate classification in seconds:Average classification and tagging time was reduced to approximately four seconds per candidate, accelerating the initial evaluation stage.
- 17K webpages processed daily:The automated collection infrastructure enabled the platform to process approximately 17,000 webpages per day across recruitment data sources.
- More complete candidate profiles:Consolidating partial and duplicate resume data gave recruiters a more comprehensive view of candidate experience, skills, and competencies.
- More efficient recruitment operations:Automated sourcing, profile enrichment, classification, and search reduced manual effort across the recruitment workflow and supported faster candidate identification.
AI-Powered Auto Parts Inventory Search Engine
⚡ Pain Points We Tackled
A Brazilian auto parts retailer operated an online catalog with more than 500,000 parts and 100,000 accessories, but its legacy search struggled with the scale and complexity of the inventory. Product data was distributed across spreadsheets, databases, files, and APIs, while customers needed to find specific parts even when their queries did not exactly match catalog entries.
Our Approach
Azati developed an AI-powered inventory search engine that unified fragmented catalog data, enriched parts with searchable attributes, and matched user queries against product characteristics. When an exact match was unavailable, the engine could identify similar parts and accessories, while in-memory processing enabled search results in under one second.
Applied Methods and Practices
- Unified fragmented inventory data:Azati built custom data connectors and processing pipelines for XLSX, TXT, CSV, MySQL, and API sources. The different catalog formats were normalized into a unified structure suitable for indexing and fast search.
- Intelligent product tagging:The system enriched auto parts with unique attributes and tags, giving the search engine additional product characteristics to use when interpreting customer queries and identifying relevant inventory.
- Similarity-based product search:A pairwise comparison algorithm matched queries against product attributes rather than relying solely on exact catalog terms. If the requested part could not be found directly, the engine could return similar products based on their characteristics.
- Real-time search experience:A React-based interface provided live filtering, autocomplete, and continuously updated results without page reloads. This allowed customers to refine searches while typing and receive immediate feedback.
- In-memory search architecture:Search processing was moved into memory to avoid database read/write bottlenecks. Memcached supported fast access to the indexed data and helped maintain sub-second response times across a large inventory.
- Modular platform architecture:The solution separated data processing from the user interface, making the search engine easier to maintain and allowing search logic and frontend functionality to evolve independently.
Solution Features
- Sub-second inventory search:Average search response time was reduced to ≤1 second, enabling near-instant product discovery across a large automotive inventory.
- More relevant product discovery:Attribute-based tagging and similarity matching allowed the engine to handle complex or incomplete queries and surface relevant alternatives when an exact product match was unavailable.
- 15K inventory objects analyzed:The prototype was developed and validated against 15,000 inventory objects, providing a representative foundation for the larger catalog.
- 300K product attributes generated:The sample dataset was enriched with approximately 300,000 attributes, giving the search engine a more detailed basis for product matching.
- Scalable catalog search:A unified data-processing layer and modular architecture provided a foundation for handling fragmented inventory sources and expanding product data without relying on the legacy search approach.
Integrated AI Search Platform for Sports Data Management
⚡ Pain Points We Tackled
An international sport organization needed to centralize athlete and event data streaming in from live feeds, official results APIs, media outlets, CSVs, XML, HTML pages, and historical archives – with inconsistent naming conventions and no consistent identifiers linking the same athlete or event across sources. Every update, correction, and conflict was reviewed manually, a process that couldn't scale as feed volume grew, leaving administrators no automated way to catch anomalies before they reached a report or partner feed.
Our Approach
Azati built a four-layer platform: distributed ETL pipelines on Apache Spark for batch and streaming ingestion, a containerized microservices layer for entity linking and deduplication, an AI-assisted enrichment stage using NLP and embeddings for semantic search, and a governance hub with full versioning, conflict comparison, and audit trails.
Applied Methods and Practices
- Distributed ETL at Scale: batch and streaming pipelines normalizing multi-format data (JSON, CSV, XML, HTML) at terabyte scale.
- Entity Resolution & Deduplication: modular microservices (Docker/Kubernetes on AWS) linking records to a single athlete or event identity.
- NLP & Embedding-Based Enrichment: resolving naming ambiguities and powering natural-language semantic search via Elasticsearch.
- Governance & Versioning: full audit trails, approval workflows, and side-by-side conflict comparison for every profile change.
Solution Features
- Unified Athlete & Event Identity: 5M+ records normalized and cross-linked across every source system feeding the platform.
- Automated Anomaly Detection: event-driven alerts replace manual, periodic data-quality checks.
- Natural-Language Semantic Search: analysts query historical results conversationally instead of hand-building filters.
- Elastic, Event-Ready Infrastructure: dynamic cloud scaling absorbs traffic spikes around major competitions.
Frequently asked questions
Knowledge intelligence is the combination of enterprise knowledge, intelligent retrieval, contextual interpretation, and AI reasoning that allows people and AI systems to find, understand, and use an organization's own information.
Azati's Knowledge Intelligence platform provides this layer for enterprise websites, portals, applications, intranets, and AI agents.
Azati's knowledge intelligence platform is the enterprise knowledge and retrieval layer behind AI applications. It can provide direct search and cited answers, but it can also supply governed retrieval to purpose-built assistants and AI agents.
Unlike a generic copilot, it is engineered around the organization's own content, retrieval logic, permissions, domain terminology, deployment requirements, and source-grounding rules.
The search pipeline can support multilingual queries and answers, including cases where the query language differs from the source-content language. Supported languages and expected accuracy should be validated against the target corpus.
Grounded AI search can reduce unsupported answers by generating responses from retrieved source content and applying confidence thresholds. Azati's approach can return an explicit fallback when the available evidence is insufficient rather than forcing an answer.
AI search accuracy depends on the content corpus, domain, query types, retrieval configuration, and evaluation methodology, so there is no meaningful universal accuracy percentage. Azati evaluates search against the client's actual content and use cases rather than presenting a single generic accuracy number.
Organizations with substantial, valuable, or sensitive knowledge distributed across websites, portals, intranets, document repositories, applications, or databases, particularly where conventional search, fragmented knowledge bases, or generic AI tools cannot provide sufficiently relevant, grounded, or permission-aware retrieval.
Azati EU infrastructure (GDPR-native), your own cloud tenant, or fully on-premise/air-gapped for organizations that can't send data to any external service, including, where required, a self-hosted open-source model configuration with zero data egress to any third-party AI provider.
Azati can deliver a fixed-scope website in approximately 2–4 weeks. Enterprise portal, intranet, and application deployments usually require individual scoping because integrations, permissions, content volume, and infrastructure requirements vary.
That's the portal/intranet tier, scoped separately because it usually involves identity-aware search and non-text content (scans, drawings). Same platform, different, honestly-priced configuration.
Configuration, prompts, guardrail rules, brand-matched UI, and analytics belong to you. The underlying search platform is Azati IP, licensed for the engagement term. Source-code delivery is available as a separate arrangement.
Three options, no forced lock-in: a monthly retainer, a fully managed engagement on an SLA, or an in-house handover with a runbook and monitoring setup.
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