Facing a data-related challenge? Explore some of the most common issues and discover how we can help you tackle them, gain deeper insights from your data, and find practical solutions.
Enterprise data, ready for AI
Q-Sensei provides the AI Data Engine as a managed service for building AI Data Applications on enterprise data. Together with our customers, we make AI more precise, secure, and cost-efficient through connected data, greater control, and contextual reasoning.Learn more
How do we stop poor-quality data from undermining AI results?
The challenge
I need reliable data to build AI applications that perform consistently in production. But our data pipelines often contain duplicate, incomplete, inconsistent, or poorly formatted records. Without effective validation and preparation, these problems can undermine analytical results and lead to inaccurate or unreliable AI responses.
How Q-Sensei helps
The AI Data Engine ingests and aggregates data from connected sources, then supports cleansing, enrichment, labeling, and structured analysis. You can examine data quality across sources and processing stages, applying validation rules and integrations suited to your requirements.
You get a more consistent and reliable data foundation for analytics and AI. This reduces the effort spent correcting data problems and gives your AI applications better information to work with.
Review the records behind a quality finding
Incoming records and updates
Account C04Contact field present
Ready to examine
Account C09Contact field empty
Missing value
Account C04, second entryMatching identifier
Possible duplicate
Check whether the two entries for account C04 refer to the same record.
Data typically used in this investigation
Operational databases and business records
These contain structured information from enterprise applications and transactional systems. They help you identify incomplete, inconsistent, or duplicate records that can affect AI results.
Documents and knowledge repositories
These contain policies, reports, procedures, and other unstructured business information. They help you assess whether important knowledge is complete, current, and consistently prepared for AI.
Application logs and event data
These record system activity, processing events, and data pipeline behavior. They help you investigate ingestion problems, failed transformations, and irregularities in data processing.
Data pipeline and integration records
These describe data movement, transformations, and validation steps across connected systems. They help you examine where data quality issues originate and how they affect downstream information.
Data validation and quality records
These contain validation results, quality indicators, and exception reports. They help you assess data reliability and identify recurring problems across sources and processing stages.
How do we keep data and machine learning features consistent across teams and AI workflows?
The challenge
I need different teams and AI workflows to work with consistent data and feature definitions. Today, the same business information or machine learning features may be calculated, transformed, and maintained differently across projects. This creates conflicting results and makes it harder to reproduce, compare, and trust model outputs.
How Q-Sensei helps
The AI Data Engine integrates source records, prepared datasets, and derived features within a shared data foundation. You can make consistent data definitions and transformations available across connected workflows and applications.
Your teams can work from a more consistent view of enterprise data instead of maintaining disconnected versions for individual projects. This makes results easier to compare, reuse, and maintain across AI workflows.
Compare workflows against a shared feature definition
Shared feature · net_revenue_v2
Billed amount minus refundsCalendar monthEUR
Same definition and version to compare across workflows
Training datasetFeature version v2 · monthly EUR values
Inference workflowFeature version v2 · monthly EUR values
A shared definition provides a reference for checking consistency across teams and workflows.
Data typically used in this investigation
Operational databases and business records
These provide the source information used to calculate and derive business and machine learning features. They help you compare how the same underlying information is represented across teams and applications.
Feature datasets and analytical tables
These contain prepared features, calculated metrics, and derived values used by analytical models. They help you identify differences in feature definitions and transformations.
Data pipelines and transformation records
These describe how source information is processed and converted into reusable datasets. They help you trace inconsistencies back to specific processing steps.
Machine learning training and inference data
These datasets show the information used during model development and real-time predictions. They help you examine whether features remain consistent between training and production workflows.
Data catalogs and metadata repositories
These document data structures, definitions, and relationships across enterprise systems. They help you establish shared definitions and make data easier to understand and reuse.
How do we protect sensitive data and meet privacy and compliance requirements in AI workflows?
The challenge
I need to use enterprise data for AI without exposing sensitive information or losing control over how it is accessed and processed. Different users, teams, and applications require different levels of access, while policies must govern how data and intelligence can be combined and used. These requirements become harder to maintain as information moves through ingestion, processing, reasoning, and delivery.
How Q-Sensei helps
The AI Data Engine applies granular access permissions based on users, teams, roles, and responsibilities. You can define governance policies for how data and intelligence are accessed, processed, combined, and used. These controls extend across the intelligence lifecycle, from data ingestion and processing through reasoning, delivery, and use.
You retain control over who can access which data and capabilities, and how enterprise intelligence can be used throughout your AI workflows. This helps you protect sensitive information, apply your governance requirements consistently, and build AI applications with security and accountability at their core.
Give each identity an appropriate scope
Support applicationDefined application identity and permissions
Authorized scope
Customer service records
Approved guidance
Restricted financial recordsOutside this identity’s scope
Review this application’s permissions and processing records against the access policy.
Data typically used in this investigation
Identity and access management systems
These contain user identities, groups, roles, and access permissions. They help you define and maintain granular access rights across data and AI capabilities.
Data classification and metadata repositories
These describe data sensitivity, ownership, and handling requirements. They help you apply appropriate governance policies to different types of enterprise information.
Business policies and compliance documentation
These define internal rules and external requirements for handling and using information. They provide the context for governing how data and intelligence can be accessed and processed.
Application and access logs
These record user activity, access events, and application operations. They help you investigate how information and capabilities are accessed and used.
Audit and processing records
These document relevant data handling, transformations, and workflow activity. They help you trace how information moves through governed AI workflows and support compliance reviews.
How do we trace the origin and transformation of data behind unexpected AI results?
The challenge
When an AI application produces an unexpected or biased result, I need to understand which information contributed to it. Without clear records of where data came from, how it was transformed, and where it was used, tracing the issue back through complex pipelines can be difficult and time-consuming.
How Q-Sensei helps
The AI Data Engine supports exploration of data lineage and provenance across connected sources and preparation steps. You can examine source information, transformations, and available processing records to understand how data entered the analytical and AI workflows.
You get a clearer view of the information behind analytical results and AI responses. This makes it easier to investigate unexpected outcomes, assess data reliability, and explain how relevant information was prepared and used.
Trace an unexpected result through available records
Result to investigateResponse R12 · Order A17
Prepared recordOrder A17 · Customer C04
Transformation recordDate normalization · source field retained
Source entryOrder spreadsheet · row 17
Evidence gapAn intermediate processing record is unavailable
Follow captured source and transformation references. Missing lineage remains a gap to investigate.
Data typically used in this investigation
Source databases and business records
These contain the original information used in analytical and AI workflows. They help you trace results back to the underlying business data.
Data pipeline and transformation logs
These record ingestion, processing, and transformation activities. They help you identify how source information changed before reaching an AI application.
Data catalogs and metadata repositories
These describe data structures, ownership, and relationships between datasets. They help you understand where information originates and how it relates to other enterprise data.
Application and model activity logs
These record relevant processing events and AI application activity. They help you investigate which information and operations contributed to an unexpected result.
Audit and provenance records
These document data access, processing history, and available source references. They help you reconstruct the information flow behind analytical findings and AI-generated responses.
How do we make structured and multimodal enterprise data usable for AI?
The challenge
I need AI applications to work with more than structured database records. Relevant knowledge is also stored in documents, PDFs, images, diagrams, and audio and video recordings. Preparing these different information types and making their content accessible in a consistent way is a major challenge when building AI applications.
How Q-Sensei helps
The AI Data Engine ingests and prepares structured and unstructured information from connected enterprise sources. It can also make existing transcripts and metadata from audio and video recordings available alongside other enterprise data, so that this information can be searched, analyzed, and related to business context.
You can make knowledge from different information sources accessible through a shared data foundation. This gives your AI applications a broader view of enterprise information without requiring every source to be treated as a separate data silo.
Connect source formats with business meaning
Source fields
cust_ref
C04
order_no
A17
date
30/10/2026
Order spreadsheet
Prepared record
Customer
C04
Order
A17
Date
2026-10-30
Source reference retained
Business recordsStructured fields and identifiers
Documents and visualsContent, structure and metadata
Audio and videoExisting transcripts and metadata
Retain source references while preparing structured and multimodal information, including existing audio and video transcripts and metadata.
Data typically used in this investigation
Databases and structured business records
These provide tabular information from enterprise applications and transactional systems. They give AI applications access to structured business facts and relationships.
Documents, text files, and knowledge repositories
These contain reports, contracts, policies, and other written information. They make enterprise knowledge available for contextual retrieval and analysis.
Images, diagrams, and scanned documents
These contain visual information such as technical drawings, charts, and digitized records. They help you make relevant visual content available alongside structured and textual information.
Audio and video transcripts
These make spoken content, discussions, and recorded events searchable alongside other enterprise information. They help your AI applications access knowledge contained in meetings, interviews, and recordings.
Audio and video metadata
Information such as timestamps, participants, titles, and recording details helps you identify, filter, and relate recordings to relevant business context.
How do we scale vector storage and retrieval without creating memory and latency bottlenecks?
The challenge
As our AI applications and knowledge bases grow, storing and retrieving vector embeddings can create significant memory, performance, and infrastructure demands. I need to scale retrieval without allowing increasing data volumes to result in unacceptable latency or operational complexity.
How Q-Sensei helps
Q-Sensei takes a different architectural approach to scaling data storage and retrieval. By combining multidimensional indexing with neural graph-based reasoning, it identifies relevant information across large volumes of enterprise data and processes it in context. This architecture is designed for scale and real-time performance, with less processing overhead.
Your AI applications can retrieve more relevant information with greater precision and lower processing costs. This gives you a more efficient way to handle growing enterprise data and complex queries without relying solely on increasingly large vector databases and similarity searches.
Connect applications to a shared data foundation
Multi-Dimensional Analytical IndexShared data foundation for complex queries
Neural Graph ProcessorRelationships and contextual reasoning
Standard APIs and configured integrations
AI application workload
Memory useRetrieval latencyQuery volume
Observe these measures for the relevant workload.
Measure memory use and retrieval latency at the query volume your application needs.
Data typically used in this investigation
Document and knowledge repositories
These contain the source material used to prepare enterprise knowledge for retrieval. They help you assess the volume and structure of information that AI applications need to access.
Vector and embedding datasets
These contain numerical representations of documents and other information used in similarity-based retrieval. They help you investigate retrieval performance and the scale of vector data.
Application and search logs
These record retrieval requests, response times, and search activity. They help you identify latency patterns and understand how retrieval workloads change as usage grows.
Infrastructure monitoring data
These provide metrics on memory, CPU, storage, and network utilization. They help you examine resource bottlenecks that affect data access and retrieval.
AI application usage records
These describe query volumes, request patterns, and application activity. They help you understand retrieval demand and plan for growing workloads.
How do we combine real-time data streams with large volumes of historical information?
The challenge
I need AI applications to consider current events alongside historical information. Real-time streams provide immediate context, while historical data reveals trends and patterns. Keeping both available and analyzing them together is difficult when streaming and batch processing operate in separate systems.
How Q-Sensei helps
The AI Data Engine can integrate event streams with historical records from connected sources. You can analyze incoming information in relation to stored data, using time-based comparisons and other analytical operations to examine developments across different periods.
You get a more complete picture of current events and their historical context. This makes it easier to investigate changes, recognize patterns, and provide AI applications with information that reflects both recent activity and longer-term developments.
Bring recent events and historical records together
Customer C04Shared entity
Recent eventOrder A17 · received November 4 at 10:02 UTC
Historical recordsOrders for Customer C04 · October 1–31
Examine the event alongside the relevant earlier records.
Use shared entities and timestamps to examine current activity in historical context.
Data typically used in this investigation
Streaming platforms and event brokers
These deliver continuously generated events from applications and connected systems. They help you examine current activity and make recent information available for analysis.
Operational databases and transaction records
These store business events and transactions over time. They provide historical context for interpreting incoming events and identifying changes.
Application and infrastructure logs
These record system activity and operational events as they occur. They help you correlate real-time behavior with historical patterns and incidents.
IoT and device telemetry
These provide ongoing measurements and events from connected equipment and environments. They help you analyze current operating conditions against historical readings.
Historical analytical datasets
These contain aggregated records, trends, and measurements from previous periods. They help you compare current events with longer-term developments and patterns.
How do we control the growing compute and storage costs of AI data processing and analytics?
The challenge
As our data volumes and AI workloads increase, the costs of processing, storing, embedding, and analyzing information can grow quickly. I need to get meaningful results from large and complex datasets without sending unnecessary information through expensive model and compute operations.
How Q-Sensei helps
The Neural Graph Processor reasons across the full body of available data and its relationships, helping Q-Sensei’s purpose-built models, third-party LLMs, and your own models deliver more precise, explainable results with less processing overhead. By handling relationship analysis and structured reasoning in a dedicated computational layer, it reduces unnecessary model processing and helps lower compute, energy, and operating costs.
You can get more intelligence from your existing data while making more efficient use of AI resources. This helps you scale analytical and AI workloads without letting processing costs grow at the same rate as data volumes and complexity.
Examine the work behind resource consumption
Data processingInspect preparation and analytical work for the selected workload.
Model usageReview model requests and available token and compute records.
Storage lifecycleExamine retained data, usage and lifecycle requirements.
Compare resource consumption with the application’s requirements and reviewed outcomes.
Compare processing, model usage and storage to see where the workload consumes resources.
Data typically used in this investigation
Cloud infrastructure and usage metrics
These record compute, storage, and network resource consumption. They help you understand infrastructure costs and how they change with data and AI workloads.
AI model and API usage records
These describe model requests, token consumption, and associated usage. They help you identify which AI operations contribute most to processing costs.
Data pipeline and processing logs
These record ingestion, transformation, and analytical processing activity. They help you examine processing volumes and identify resource-intensive stages.
Storage and data lifecycle records
These describe data volumes, retention periods, and storage utilization. They help you assess storage growth and identify opportunities to manage data more efficiently.
Query and analytics performance metrics
These record query activity, execution times, and resource consumption. They help you understand the cost and performance implications of analytical workloads.
How do we preserve meaning and context when splitting documents for RAG?
The challenge
I need to prepare documents for Retrieval-Augmented Generation without losing the context that makes their content meaningful. Chunks that are too small can separate important details from their surrounding information, while chunks that are too large can introduce irrelevant content. Finding the right balance is difficult, especially across complex documents and interconnected enterprise knowledge.
How Q-Sensei helps
The Multi-Dimensional Analytical Index organizes enterprise data, while the Neural Graph Processor reasons across relevant information, relationships, and business context. Rather than relying only on isolated document chunks or matching terms, it can consider connections between entities and evaluate relevant conditions to provide AI applications with richer context for retrieval and reasoning.
Your RAG applications can draw on a broader understanding of enterprise knowledge, not just a collection of matching text passages. This helps you improve the relevance and precision of generated answers while making their supporting evidence easier to trace.
Keep a passage connected to its business context
Which delivery terms apply to Order A17?
Selected passageDelivery within five business daysAgreement v3 · section 4
Associated contextPriority delivery conditionsProduct P12 · region B · applicable agreement version
Document structure, entities and applicable conditions help preserve meaning when selecting context for RAG.
Data typically used in this investigation
Documents and knowledge repositories
These contain the original content used in RAG applications. They help you examine how document structure and content affect the context available for retrieval.
Document metadata
This includes titles, authors, dates, categories, and other descriptive information. It helps you retain useful context and narrow retrieval to relevant documents.
Business glossaries and taxonomies
These define terminology, classifications, and domain-specific concepts. They help your AI applications interpret specialized language and relationships consistently.
Entity and relationship records
These describe connections between people, organizations, products, processes, and other business objects. They help preserve relationships that may not be apparent from individual text passages.
Document structure and section information
These describe headings, sections, tables, and other organizational elements. They help you retain the context surrounding individual passages when preparing documents for retrieval.
How do we keep AI applications' enterprise knowledge current as business data changes?
The challenge
I need AI applications to reflect current enterprise information without repeatedly rebuilding datasets or retraining the underlying models. Business records, documents, and operational information change continuously. If those changes do not reach the knowledge available to AI applications, their responses can quickly become outdated.
How Q-Sensei helps
The AI Data Engine continuously ingests, cleans, enriches, and labels connected enterprise data. As new data arrives or existing data changes, those changes are automatically reflected in the AI Data Engine—keeping data current, consistent, and controlled across supported sources and integrations.
You can rely on an evolving data foundation rather than repeatedly preparing static datasets for your AI applications. This helps you keep enterprise knowledge aligned with changing business information while reducing manual data management effort.
Follow a source change into available context
Source revisionPolicy v3 · updated November 4 at 10:02 UTC
Connected preparationIngested record retains revision v3 and source reference.
Retrieval contextInspect the policy version available to the application.
Check source revision, processing record and retrieved version together.
Trace the policy revision from the connected source to the version the application retrieves.
Data typically used in this investigation
Operational databases and business applications
These contain changing business records, transactions, and operational information. They help keep AI knowledge aligned with current enterprise data.
Document and knowledge repositories
These store policies, procedures, reports, and other business content that changes over time. They help ensure AI applications can access updated enterprise knowledge.
Content management and collaboration systems
These contain working documents, shared files, and team knowledge. They help make newly created or revised information available for AI applications.
Event streams and change records
These capture updates and events as they occur across connected systems. They help reflect recent business activity in the AI Data Engine.
Reference and master data systems
These maintain shared business entities, identifiers, and classifications. They help keep related information consistent as enterprise records change.
How do we prevent irrelevant retrieval results from adding noise to AI prompts?
The challenge
I need AI applications to retrieve information that actually helps answer a question. But conventional retrieval can return text that matches keywords or vector similarity without reflecting the full context of the request. Irrelevant results then consume prompt space, increase processing costs, and can lead to inaccurate or misleading answers.
How Q-Sensei helps
The Multi-Dimensional Analytical Index supports complex queries across large datasets, combining filtering, grouping, correlation, and analytical operations within a single query context. The Neural Graph Processor builds on this foundation by reasoning across relevant data, entities, relationships, and business conditions to determine what matters to a question—not simply what matches it.
You can address complex questions with a combination of analytical depth and contextual reasoning. This helps your AI applications retrieve more relevant information, account for relationships across enterprise data, and deliver more precise, meaningful responses.
Inspect relevance before assembling a prompt
Which delivery terms apply to Order A17?
Retrieved candidatesDocuments with similar delivery wording
Relevant conditionsCustomer C04 · Product P12 · region B
Evidence to reviewAgreement v3 §4 · applicable conditions
Match the customer, product and region to the agreement before selecting a passage.
Data typically used in this investigation
Enterprise documents and knowledge repositories
These contain the information retrieved to answer user questions. They provide the source content for assessing retrieval relevance and completeness.
Business databases and structured records
These contain facts, attributes, and relationships relevant to enterprise questions. They help refine retrieval with precise filters and business context.
Entity and relationship data
These describe connections between business objects and concepts. They help distinguish information that is relevant to the question from results that only share similar terms.
Search and retrieval logs
These record queries, retrieved items, and search activity. They help you investigate irrelevant results and identify recurring retrieval problems.
Prompt and application activity records
These contain prompts and available context passed through AI workflows. They help you understand how retrieved information affects the quality and relevance of generated responses.
How do we detect data drift and changing business patterns before they undermine AI performance?
The challenge
I need to know when the data used by our AI applications no longer reflects current conditions. Customer behavior, operational processes, and business patterns change over time. Without ongoing analysis, data drift and concept drift can go unnoticed until they affect model performance or the quality of AI-generated results.
How Q-Sensei helps
The AI Data Engine supports multidimensional analysis, time-based comparisons, and pattern analysis across connected historical and current data. You can examine changes in data distributions, trends, and business relationships to investigate potential drift and changing conditions.
You gain a clearer view of how enterprise data and business patterns evolve over time. This helps you recognize changes that may affect AI performance and make better-informed decisions about data preparation and model evaluation.
Compare input patterns across matched periods
October 21–27 · 1,000 ordersOctober 28–November 3 · 1,000 orders
Share of orders by order-size group · common scale 0–100%
Small
Earlier 50% · later 30%
Medium
Earlier 30% · later 45%
Large
Earlier 20% · later 25%
The mix of order sizes shifted between the two weeks. Compare evaluation results from the same periods to check for a change in accuracy. Sample data.
Data typically used in this investigation
Historical and current business datasets
These contain business records and measurements from different periods. They help you compare current data distributions with historical baselines.
Model input and feature data
These describe the information supplied to AI models during training and operation. They help you investigate changes in incoming data that may affect model behavior.
Business performance and KPI records
These track metrics such as customer activity, sales, and operational performance. They help you identify changing business patterns that may influence AI applications.
Application and model performance logs
These record application behavior, model outputs, and performance indicators. They help you examine whether changes in data coincide with changes in AI performance.
External market and reference data
These provide information about market conditions, industry developments, and external events. They help you distinguish internal data changes from broader shifts in the business environment.
How do we understand what happened inside an AI application, from prompts and retrieved context to generated results?
The challenge
When an AI application behaves unexpectedly, standard application logs often do not tell me enough. I need to understand the prompt, the information retrieved, the context passed to the model, and the resulting output. Without this level of visibility, investigating failures, inconsistent answers, and unexpected costs becomes difficult.
How Q-Sensei helps
Q-Sensei makes available operational data alongside enterprise information, including relevant application logs, traces, and activity records from connected systems. You can examine this information in context to investigate AI application behavior, performance, and processing activity.
You get a more complete picture of how your AI applications operate and where problems may arise. This makes it easier to investigate unexpected behavior, understand performance and costs, and improve the reliability of your applications.
Follow the captured evidence for a request
Captured requestRequest R12 · November 4, 10:02 UTC
Prompt P17Available in the connected prompt log
Retrieved context D04Document reference and revision recorded
Model activityUsage record available · internal reasoning not captured
Generated result R12Response linked to the captured request
Follow the request through the connected logs, including the prompt where logging is enabled.
Data typically used in this investigation
Prompt and response logs
These record user inputs and AI-generated outputs where logging is enabled. They help you examine how an application responds to different requests.
Retrieval and context records
These describe the information retrieved and made available to the model. They help you investigate whether the application received relevant supporting context.
Application logs and distributed traces
These record application events, execution steps, and service interactions. They help you trace processing behavior and investigate failures across connected components.
Model and API usage metrics
These capture request volumes, latency, token usage, and related performance information. They help you understand response times, resource consumption, and operating costs.
Infrastructure and service monitoring data
These provide information about resource utilization and the health of supporting systems. They help you assess whether infrastructure conditions contributed to application behavior.
How do we measure AI accuracy and reliability when ground-truth evaluation data is limited?
The challenge
I need to know whether an AI application produces accurate, relevant, and reliable results. But creating and maintaining representative evaluation datasets takes time, and reliable ground-truth answers are not always available. Without meaningful ways to compare outputs against supporting evidence and expected results, measuring quality and tracking improvements is difficult.
How Q-Sensei helps
Q-Sensei links AI outputs to retrieved information, source records, and available data provenance. You can examine the evidence behind generated results and use connected enterprise information to support evaluation workflows and comparisons.
You gain a more transparent basis for assessing AI-generated results, even when a complete set of ground-truth answers is unavailable. This helps you investigate inaccuracies, assess the relevance of supporting evidence, and build more reliable evaluation practices.
Separate supporting evidence from accuracy evaluation
Available supportResponse R12 cites Policy v3 §4Source reference and version available for inspection
Evaluation statusReference answer unavailableBusiness conditions: review pendingOverall accuracy: not established
Check the cited policy against the response. A reference answer or expert review would help assess its accuracy.
Data typically used in this investigation
AI-generated responses and prompt records
These capture the questions submitted to an application and its resulting answers. They provide the material you need to examine and compare application behavior.
Retrieved documents and source records
These contain the evidence used to support generated responses. They help you assess whether answers are grounded in relevant and reliable information.
Evaluation datasets and test cases
These contain representative questions, expected results, and defined evaluation criteria. They help you measure accuracy and consistency across application versions.
Business rules and reference information
These define accepted business facts, terminology, and expected outcomes. They help you assess whether generated responses align with enterprise requirements.
User feedback and review records
These capture ratings, corrections, and expert assessments of AI outputs. They help you identify recurring quality issues and improve evaluation criteria.
How do we evaluate and improve new AI applications when no historical usage data is available?
The challenge
When I launch a new AI application or feature, I have little or no interaction history to understand how people will use it. I cannot rely on established usage patterns to assess performance, identify weaknesses, or optimize the experience. I need a way to evaluate the application before enough real-world usage data has accumulated.
How Q-Sensei helps
The AI Data Engine provides a foundation for data preparation, contextual retrieval, and analytical evaluation from the outset. You can use connected enterprise knowledge and relevant business data to examine application responses, test questions, and assess results against available evidence and defined requirements.
You can start evaluating an AI application before substantial usage history exists. This gives you a basis for identifying gaps, refining its data and context, and improving the application as real-world interaction data becomes available.
Start with defined questions and review criteria
Record matchTest: Order A17. Criterion: cite the matching source record. Review pending.
Missing termsTest: no applicable agreement. Criterion: identify the evidence gap. Review pending.
Restricted recordTest: unauthorized request. Criterion: respect the defined access policy. Review pending.
Connected enterprise knowledge and defined test cases support early evaluation. Review gaps and outcomes before expanding use.
Data typically used in this investigation
Enterprise knowledge repositories
These contain the documents, policies, and information the new application is expected to use. They help you test whether it can retrieve relevant knowledge before real-world usage begins.
Business databases and reference records
These provide structured facts and business information relevant to the application's purpose. They help you assess whether responses reflect accurate enterprise information.
Defined test questions and evaluation scenarios
These describe representative tasks and questions the application should handle. They help you evaluate behavior against expected requirements without relying on historical usage.
Business rules and acceptance criteria
These define the requirements and expected outcomes for the application. They help you assess whether responses meet the intended business objectives.
Early user feedback and review records
These capture initial assessments, corrections, and observations from users or subject-matter experts. They help you refine evaluation criteria as real-world experience accumulates.
Q-Sensei provides the AI Data Engine as a managed service to help you turn enterprise data into trusted insights and intelligent applications. Together, we make AI more precise, secure, and cost-efficient through connected data, greater control, and contextual reasoning.
