Enterprises generate vast amounts of documents, reports, policies, manuals, emails, and records every day. Most of this knowledge exists, but it is hard to find, hard to trust, and difficult to use for real decisions.
At ThirdEye Data, we help enterprises convert fragmented documents and content into secure, searchable, and decision-ready knowledge systems that work inside existing environments and processes.
We have seen that most enterprises do not have a “lack of data” problem. They have a knowledge access and usability problem.
Based on our experience building document and knowledge intelligence systems, these are the real challenges we address.
Critical information is scattered across SharePoint, file systems, document management platforms, email archives, data lakes, and legacy systems. Employees spend excessive time searching, asking colleagues, or recreating information that already exists.
We build systems that make enterprise knowledge discoverable, contextual, and available at the moment decisions are made.
Invoices, contracts, policies, SOPs, technical documents, and compliance records are still reviewed manually in many organizations. This slows operations, increases errors, and creates dependency on a few individuals who “know where things are.”
Our solutions automate document understanding while keeping validation and control in human hands.
Legacy keyword search tools fail to understand intent, context, or meaning. Users receive too many irrelevant results or miss critical information entirely. Over time, teams stop trusting internal search systems.
We design semantic, role-aware search systems that return precise, relevant answers instead of long result lists.
Policies and regulations change frequently. Teams struggle to interpret what applies to their role, geography, or situation. This creates compliance risk and inconsistent execution.
We enable policy-aware knowledge systems that help teams understand and apply rules correctly without slowing down operations.
Even when information is found, it often sits outside workflows. Teams must manually translate knowledge into actions, approvals, or updates across systems.
We embed knowledge intelligence directly into workflows so information can drive action.
Our Enterprise Knowledge Intelligence solutions are designed to create measurable value across core enterprise functions by improving how information is accessed, interpreted, and used.
By making relevant documents and knowledge available instantly, teams can make decisions based on facts rather than assumptions or incomplete information.
Automating document classification, retrieval, and interpretation reduces the manual effort required to manage and search enterprise content.
Policy-aware search and document understanding reduce the risk of missed rules, outdated information, or inconsistent interpretation.
Our Enterprise Knowledge Intelligence solutions are built as connected components, not isolated features. Each component solves a specific problem, and together they form a reliable knowledge system.
Our document intelligence solutions are designed to read and understand documents at scale.
They handle:
We design these systems to work with real-world documents, including variations, inconsistencies, and incomplete data. Human review points are built in where accuracy is critical.
We build enterprise-grade internal search platforms that go beyond keyword matching.
These systems:
Search results are precise, contextual, and trustworthy, which drives adoption across teams.
We build applications that allow users to ask natural language questions over enterprise documents.
These applications:
They are especially effective for technical documentation, policies, contracts, and operational manuals.
These systems help teams interpret and apply policies correctly.
They:
The focus is on clarity, consistency, and risk reduction.
We extend knowledge intelligence into action.
This includes:
Knowledge becomes an active part of business operations.
Developed a Generative AI-based document analytics platform to extract pertinent entities from a variety of file formats, such as .pdf, .xls, and .doc, originating from multiple sources.
Implemented a cognitive computing application leveraging IBM Watson services on the IBM Bluemix cloud infrastructure to identify subject matter experts for a BFSI customer.
Our approach is designed for enterprises that need reliable, governed, and scalable knowledge systems.
We use open-source technologies where flexibility, customization, or fine-grained control is required.
Typical use cases include:
RAG orchestration
Embedding and retrieval pipelines
Custom document parsing logic
Integration layers
All open-source components are production-hardened and governed.
For most enterprise deployments, Microsoft and Azure form the foundation.
We prioritize:
Azure OpenAI
Azure AI Search
Azure AI Foundry
Microsoft Fabric
Power Platform and Copilot Studio
Entra ID and Zero Trust architectures
This ensures strong security, governance, and seamless integration with existing enterprise ecosystems.
Many enterprises operate in mixed environments.
We design hybrid knowledge intelligence systems that:
Work across cloud and on-prem data
Respect data residency requirements
Avoid unnecessary re-platforming
Integrate cleanly with legacy systems
This allows gradual modernization without disruption.
Talk to us to design and implement enterprise-grade knowledge intelligence systems that fit your data, your workflows, and your compliance requirements.
Knowledge Retrieval & Orchestration
LangChain
Semantic Kernel
LlamaIndex
RAG & Search Components
Custom RAG pipelines
Hybrid retrieval (semantic + keyword)
Re-ranking and relevance tuning
Vector Databases & Search
FAISS
Milvus
Weaviate
OpenSearch
Elasticsearch (vector + keyword)
Backend & Integration
Python, FastAPI
REST and event-driven APIs
Microservices architecture
Deployment & Operations
Docker
Kubernetes
CI/CD pipelines
Microsoft & Azure
Azure OpenAI
Azure AI Search
Azure AI Foundry
Microsoft Fabric
Power Platform
Copilot Studio
Azure Functions
Logic Apps, App Services
Microsoft Entra ID
Key Vault
Azure Monitor
AWS
Amazon Bedrock
Amazon OpenSearch
Lambda and Step Functions
Google Cloud Platform
Vertex AI
Gemini models
BigQuery integrations
Third-Party Platforms
Databricks (Vector Search, MLflow)
Snowflake (Cortex, external functions)
Specialized document and OCR tools
Most internal search systems assume that employees already know what they are looking for and where it might exist. They rely heavily on keywords and return long lists of documents that still require manual reading, interpretation, and cross-checking. In practice, this slows teams down and increases the risk of missing critical information.
Enterprise Knowledge Intelligence works differently. It is designed around how decisions are actually made inside organizations. Instead of returning documents, it interprets questions in business context, retrieves relevant information across multiple sources, and explains the answer using enterprise language and terminology. More importantly, it preserves traceability. Users can always see where an answer came from and validate it against source documents. This makes the system usable not just for discovery, but for real operational and compliance-sensitive decisions.
This is not solved at the prompt level. In enterprise environments, hallucinations are a system design problem, not a model problem. We address this by ensuring that language models never operate in isolation.
Every answer is grounded in retrieved enterprise data, and generation is constrained strictly to that context. If the system cannot find enough reliable information to answer a question, it is designed to say so clearly rather than guess. We also include confidence thresholds, source attribution, and fallback logic for sensitive use cases. This approach reduces risk and, more importantly, builds trust with users over time because the system behaves predictably.
Yes, and that is the normal starting point for most enterprises we work with. Internal knowledge is usually spread across PDFs, Word files, scanned documents, emails, and legacy repositories. We design systems that can handle inconsistent formats, overlapping content, and outdated material.
We apply structured ingestion, intelligent chunking, metadata enrichment, and retrieval strategies that work even when the data is not clean. The system improves over time as usage patterns and feedback refine relevance and accuracy.
Access control is enforced exactly as it exists today in your systems. The AI does not bypass permissions or combine restricted data across roles.
We integrate with enterprise identity systems and respect document-level and system-level access rules. Users only see what they are already authorized to see. All interactions are logged for audit and review, which is essential for regulated environments.
We design these systems for continuous change, not static knowledge. The system supports incremental updates, version awareness, and re-indexing strategies that do not require full rebuilds.
When documents are updated or replaced, the system adjusts retrieval behavior accordingly. This ensures users are not unknowingly relying on outdated or superseded information, which is a common issue with simpler implementations.
Yes. In most cases, embedding is the preferred approach. Users should not have to switch tools just to access knowledge.
We commonly integrate knowledge intelligence into internal portals, business applications, dashboards, and workflow systems. This improves adoption because intelligence is available where work already happens.
Enterprise Knowledge Intelligence systems typically work across multiple source types at the same time. This includes document repositories, databases, data warehouses, internal applications, and selected SaaS tools.
We design retrieval strategies that respect source boundaries while still providing unified answers. Data movement is minimized unless there is a clear governance or performance reason to do otherwise.
No. While we have deep expertise with Microsoft and Snowflake ecosystems, we design systems to remain flexible.
Architectures are built so components can evolve independently. This allows enterprises to adapt to future platform changes without reworking the entire system. Vendor choice is driven by enterprise constraints, not our preferences.
