FOR BUSINESSFind answers. Keep moving.
Connect scattered material to projects and people. Find the answer, its source and its owner.
Pilot measure: time to find informationMEMOVA FOR BUSINESS
Enterprise knowledge.
Built for your next move.
Connect company knowledge and operational data, from permission-aware preparation to model training and local deployment.
Documentsproduct-brief.md v.04
ConversationsDecisions · commitments
Business systemsProjects · customers · records
Authorize · protect · prepare
Your company knowledgeMEMOVA CONTEXT LAYERRelationsSources
Answers with evidenceSearch · answers · insightsSource-linked & versioned
Ready-to-use workReports · plans · handoversBuilt from authorized knowledge
Concept illustration · synthetic data · not a live enterprise system
Permission before useKnowledge with provenanceDeployment by design
KNOWLEDGE THAT WORKS FOR YOU
Not another place to store files.
A better way to put knowledge to work.
Reuse information, team experience and engineering analysis. Improve work and production workflows with inspectable data.
Reuse expertise. Improve operations.
Connect procedures, operating records and model analysis to support troubleshooting and quality decisions, with less repeated preparation.
Pilot measure: response time, rework and qualityUnlock data without losing control.
Provide the necessary data by person, purpose and time. Authorize retrieval, sharing and training separately.
Pilot measure: access and revocation tests01 / WHAT WE BUILD
From data foundations to useful applications.
Start with a defined business problem. Bring together the knowledge, data and model services it needs.
01
Enterprise knowledge bases
Organize documents, meetings, customers and projects into traceable, updatable context for your teams and enterprise agents.
- Map business objects, relationships and knowledge structure
- Preserve original sources, versions and citations
- Retrieve within permissions and maintain knowledge over time
02
Local model training & deployment
Select, train or fine-tune models for business and engineering tasks, then deploy validated algorithms in private environments, on site servers or at the edge.
- Train and evaluate prediction, recognition and anomaly-detection models
- Dedicated model adaptation, local inference and workflow integration
- Validate quality and latency; hand over monitoring and rollback
03
Data collection & authorization
Define who owns the data, who can use it, for which purpose and when that access ends, before collection begins.
- Source registration, permission scope and purpose records
- Separate personal, company and third-party data
- Design revocation, expiry and deletion rules
04
Privacy processing & data engineering
Prepare documents, images, sensor readings and operating records for retrieval, analysis or authorized training, with provenance and processing history.
- Parsing, transcription / OCR, deduplication and normalization
- Sensitive-data detection, redaction and human review
- Annotation, quality sampling and traceable delivery
02 / THE DATA LIFECYCLE
Permissions travel with the data.
Make sources, purpose, permissions and revisions inspectable from collection through application.
01
Collect
Start with authorized sources
Inventory documents, business systems, site sensors and multimodal material. Record each source, owner, purpose and permission scope. Start with one defined business or engineering problem.
Output: source inventory & permission scope02
Protect
Make privacy part of processing
Identify and redact sensitive content against agreed rules. Separate identity mappings from processed data. Review high-risk samples and define retention and deletion scope.
Output: processing rules & review records03
Prepare
Make data usable and inspectable
Parse, transcribe, deduplicate and normalize data. Add labels and business entities while retaining source evidence, processing versions and quality records.
Output: versioned, source-linked datasets04
Organize
Connect knowledge to your business
Organize knowledge around people, customers, projects, events and constraints. Keep indexes rebuildable, with permissions, sources and revisions attached. Model output does not automatically replace approved records.
Output: knowledge structure & retrieval evidence05
Apply
Send the right context to the right model
Supply the necessary evidence for an authorized task: Q&A, reporting, business assistance or separately approved data applications. Retrieval, external sharing and model training require distinct decisions.
Output: application pilot, evaluation & audit planTHE MEMOVA CONTEXT LAYER
Knowledge that lasts beyond a model.
Your knowledge base holds business material, relationships and history. Models handle specific tasks. Changing a model should not mean rebuilding all that context.
- Sources & records
- Original material, owners, revisions and citations
- Knowledge & retrieval
- Business entities, stable links and permission filtering
- Models & applications
- Necessary evidence, task routing and reviewed feedback
INDUSTRIAL & ENGINEERING AI
From operational data to productive work.
Knowledge bases retain expertise. Algorithms process operational signals. Connect collection, training and local deployment in an engineering workflow that can be tested and improved.
Operational feedback returns to the data and model
Start with the operation, not a model.
Connect authorized sensor readings, images, operating logs and engineering records around a quality, equipment or efficiency question. Define access, ownership and training rights.
Source inventory · interfaces · purpose permissionsPilot before scaling. Outcomes require site validation; control-system write-back needs separate assessment and authorization, without replacing safety interlocks or human fallback.
03 / YOUR DEPLOYMENT
Fit AI to your data boundaries.
Choose a deployment based on the workload, data sensitivity and operational requirements.
Process within your environment
Evaluate storage, retrieval and inference on company networks, site servers or edge devices, matching compute, integration and maintenance needs. Verify offline behavior across models, dependencies, logs and every processing component.
- Company-controlled devices and network
- Quality and latency tests on target hardware
- Staged release, rollback and operations handover
Encryption, key ownership, external processors, model licenses and acceptance criteria are agreed in the project plan.
04 / APPLICATIONS
Context for the work that matters.
Keep project knowledge through every handover
INPUT
Meetings, project files, customer records and business rules
PROCESS
Connect sources, decisions, owners and actions by project; distinguish confirmed information from conclusions awaiting review.
OUTPUT
Source-linked briefs, handover material and internal Q&A
INDUSTRY PERSPECTIVE
How enterprise knowledge becomes useful.
Lessons from public industry practices. These are not Memova customer projects; the applications above are proposed Memova scenarios.
Connect data to operations
Skywise brings together work orders, parts and fleet data for operations and maintenance. The lesson: organize business relationships before adding AI applications.
Airbus sourceDesign retrieval with evaluation
Professional teams retrieve evidence from internal knowledge and evaluate it against real tasks. The lesson: answers need sources, and performance needs testing.
OpenAI sourceOne entry point, source permissions
Cross-system search respects source permissions while records remain in their original systems. The lesson: easier discovery does not mean universal access.
GitLab handbook
WORKING TOGETHER
Agree the scope. Prove the value.
Does a knowledge base require a custom-trained model?
No. Start with authorized data preparation and RAG so an existing model can work with traceable evidence. Fine-tuning is a separate scope with its own data rights, objectives, evaluation and cost.
Are local training and local deployment the same?
No. Training or fine-tuning changes a model using authorized data; deployment runs a validated model on target hardware. Knowledge retrieval, task-specific algorithms and language-model adaptation are evaluated separately, without assuming foundation-model training from scratch. Agree the training location, compute, data egress and release criteria separately.
Does personal data automatically enter the company knowledge base?
Personal use is not company authorization. Define ownership and purpose separately for personal, company and third-party material, and connect shared content only within an explicit scope.
How are encryption, redaction and offline operation verified?
Specify storage and transport controls, key responsibilities, authorized roles, external processors, redaction rules and backup scope in the project plan, then verify against business examples. Redaction can miss content; offline operation requires validating the entire chain, not just a local model.
How do we start an enterprise project?
Choose one workflow, its sources and permission scope, then agree pilot deliverables and acceptance criteria. Knowledge-base, dedicated model and multimodal data services are scoped per project; features and security controls follow the agreed solution.
LET'S BUILD YOUR CONTEXT LAYER
Start with one real business workflow.
Tell us about your sources, business goal and deployment requirements. We will define a pilot and its deliverables together.