Employees lose hours digging through complex contracts, financial filings, and policy documents. GrandView builds private AI assistants using IBM watsonx.ai and watsonx.data. Your teams ask questions in plain English and receive answers evaluated against source text, with available page citations and approved controls.
Deploying business AI does not require sending sensitive files to public cloud models or building custom models from scratch. Working with IBM and Red Hat, we build private search assistants configured to retrieve approved content and include available source references.
We deploy Retrieval-Augmented Generation (RAG) that searches internal files at query time. The architecture can keep documents in an approved environment and prevent their use for external model training.
watsonx.data indexes files with role-based permissions. If an employee lacks access to an executive contract or confidential schedule, retrieval is configured to exclude that content from answers.
Responses can include links to retrieved source passages. Staff can verify contract clauses, financial numbers, and policy guidelines directly against available references.
The result is not another experimental chatbot. It is a verifiable, permission-governed search assistant deployed securely inside your operational perimeter.
Discuss your search priorities →High-value information lives inside 80-page contracts, annual reports, and operational guidelines. Finding simple answers requires manual reading and cross-referencing.
Legal, finance, and operational teams lose valuable time manually reviewing vendor contracts, credit agreements, and policy PDFs just to find specific terms, expiration dates, or liability limits.
Semantic search extracts relevant paragraphs and clause comparisons across thousands of documents in seconds, directing your analysts straight to the exact page.
Without an approved internal tool, staff turn to public generative AI tools to summarize text. This creates immediate risks of exposing trade secrets, customer details, or confidential financial numbers to third parties.
Deploy private foundation models on internal infrastructure or dedicated virtual clusters, ensuring prompts and internal documents never leave your security perimeter.
General-purpose AI tools try to be helpful even when they do not know the answer. In business operations, an invented figure or distorted contract clause can cause expensive legal or commercial errors.
watsonx.governance continuously scores response faithfulness against retrieved passages, refusing to answer or alerting users when supporting evidence is lacking.
Vital information sits across SharePoint, network folders, Box, and enterprise applications. No single system can answer cross-departmental questions that require connecting multiple documents.
Index structured data from databases and unstructured records from repositories into watsonx.data, enabling unified queries across enterprise boundaries.
Because unverified answers, security risks, and fragmented storage reinforce each other, GrandView assesses your document structure, access policies, and search workflows before recommending a deployment architecture.
| CAPABILITY | TRADITIONAL KEYWORD SEARCH | PUBLIC WEB AI TOOLS | GRANDVIEW PRIVATE GOVERNED RAG |
|---|---|---|---|
| 01 Query Understanding | Primarily matches keywords; misses synonyms and underlying semantic intent. | General natural language understanding without enterprise context or precision. | Semantic retrieval tailored to enterprise vocabulary and domain taxonomy. |
| 02 Answer Format | Long lists of document links requiring manual reading and skimming. | Synthesized text without verifiable page or section citations. | Direct answers with paragraph and page citations to internal files. |
| 03 Data Confidentiality | Data stays behind firewalls but remains difficult to find or navigate. | Prompts and uploads risk third-party exposure or external model training. | Private deployment with approved retention and data-handling controls. |
| 04 Access Control | Basic folder-level permissions; cannot restrict access at the granular document passage level. | No enterprise role-based access controls; all uploaded data is exposed to the session. | Role-based filtering evaluates indexed permissions before returning document passages. |
| 05 Accuracy & Factuality | Relies entirely on manual human review and verification of each individual document. | High risk of hallucinated facts, distorted numbers, and invented contract clauses. | watsonx.governance evaluates faithfulness against reference text and flags low confidence. |
| 06 Structured Data Synergy | Disconnected from financial systems, transactional databases, ERP records, and BI tools. | Cannot securely connect to internal ERP, planning models, or general ledger records. | Connects narrative explanations directly to IBM Cognos Analytics and Planning Analytics. |
| 07 Automated Workflows | Static search; no connection to operational tasks, approval queues, or downstream systems. | Isolated in a web chat window; staff must copy and paste data into other systems manually. | Directly triggers business rules in IBM ODM and automated tasks in Business Automation Workflow. |
Private AI search solves different operational problems for each department head, while maintaining unified technical control.
Provide an approved AI solution that runs in your secure environment
Employees turn to unsanctioned public SaaS chatbots to summarize confidential text, creating security blind spots, risking IP exposure, and violating privacy policies.
Staff receive a fast, approved AI assistant running on internal infrastructure like Red Hat OpenShift, eliminating shadow AI and ensuring data never leaves your network.
Reconcile variances and explain numbers across financial reports
Finance analysts lose days manually cross-referencing narrative commentary in SEC 10-K filings, credit agreements, and vendor invoices against BI spreadsheets.
Private RAG connects narrative commentary directly with certified numbers from IBM Cognos Analytics and Planning Analytics, explaining variances with cited sources.
Accelerate contract review and due diligence with verified citations
Attorneys and compliance staff spend dozens of hours reading 80-page agreements line-by-line to compare indemnity caps, termination clauses, and renewal dates.
Legal teams query agreement repositories in natural language and receive synthesized clause comparisons supported by direct paragraph and page citations.
Give operational staff faster access to standard procedures
Frontline personnel submit hundreds of routine support tickets because standard operating procedures and technical manuals are buried in deep SharePoint folders.
Employees receive instant, verified procedural answers with direct links to internal manuals and guidelines, reducing resolution times and routine ticket volume.
The pilot proves search accuracy and permission filtering on an initial document set before expanding across other enterprise departments.
Explore our RAG architecture ↓The solution combines document parsing, vector retrieval, and foundation model inference within a secure perimeter.
Documents such as contracts, PDFs, and reports are parsed into semantic passages while preserving metadata, section headers, and user access permissions in IBM watsonx.data.
Granular passage metadata ensures employees only search files they are officially permitted to access.
When a user asks a question, the system converts it into a semantic query, filters results by the user's access rights, and retrieves only the most relevant verified excerpts.
Semantic search locates conceptual answers even when queries use different wording than the source text.
IBM watsonx.ai uses approved models, including IBM Granite, to summarize retrieved passages. Responses can include citations to the available source pages.
Staff click citations to inspect the source paragraph immediately, ending reliance on blind AI trust.
GrandView delivers an end-to-end governed search system configured for your environment, not an isolated proof of concept.
ENTERPRISE RAG ARCHITECTURE DELIVERABLE
A secure, partitioned vector store on watsonx.data containing your parsed and permission-tagged enterprise files. Document text is stored in open table formats with role-level isolation.
Configured on your hybrid cloud, Red Hat OpenShift, or private dedicated environment.
ENTERPRISE RAG ASSISTANT INTERFACE
A clean, secure web interface or API connector allowing authorized staff to query company files in plain language. Answers include clickable citations directly linking to the supporting text passage.
Integrates with corporate single sign-on (SSO) and respects Active Directory / LDAP access rights.
ENTERPRISE WORKFLOW INTEGRATIONS
Pre-built hooks that connect search results into your reporting dashboards (Cognos 12) or automated workflows (CP4BA). Grounded narrative explanations enrich certified numbers without manual copy-pasting.
Allows automated rules to evaluate contract terms directly against transaction records.Deploy your private search pilot in weeks Test semantic search on an agreed document set with role permissions, verified citations, and an evaluation report before full production rollout.
SCHEDULE A RAG PILOTThe three-week pilot begins after readiness approval. Production scope and timing are defined only after the pilot results are reviewed.
Confirm one use case, approved documents, access roles, target environment, test questions, data owner, and acceptance criteria before scheduling the pilot.
Configure ingestion, permissions, retrieval, and answer generation for the agreed corpus, then evaluate relevance, faithfulness, citations, and access filtering.
Review evaluation results, security controls, data handling, operating ownership, expected usage, integration needs, and production support requirements.
Expand approved sources, user groups, integrations, monitoring, and support through production waves sized after the decision gate.
Bring your document search challenges, contract backlogs, or internal data security questions to a direct conversation with our practice leads.
IBM watsonx.ai & watsonx.data Enterprise RAG Expertise
