AI Discovery
Identify high-value AI opportunities before committing to a large build.
- Use-case mapping
- Data and workflow assessment
- Feasibility and risk review
Release 1.8 · AI Solutions & AI Productization
Turn AI ideas into working business systems.
Practical AI engineering for teams that need more than a prototype—from discovery and architecture to agents, enterprise knowledge, automation and productized AI platforms.
AI delivery architecture
Business problemUse case, users, workflow and measurable outcome
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Data + knowledgeDocuments, systems, APIs, structured and unstructured data
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Models + intelligenceModels, retrieval, tools, prompts and evaluation
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Agent / workflowHuman controls, permissions, actions and automation
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Production outcomeObservable, secure and maintainable business capability
01 · AI Solution Portfolio
AI capabilities packaged around real business use cases.
Each engagement can be scoped independently or combined into a production AI program. Final architecture and model choices depend on the business requirement.
AI Application Engineering
Embed AI capabilities inside web, SaaS, mobile and enterprise applications.
- AI-enabled product features
- Model/API integration
- Evaluation and observability
AI Agents & Copilots
Task-oriented assistants that can retrieve context, use approved tools and operate within defined permissions.
- Tool use and workflows
- Human-in-the-loop controls
- Guardrails and auditability
Enterprise RAG
Make internal knowledge searchable and usable through grounded retrieval experiences.
- Document ingestion
- Retrieval and citations
- Access-aware knowledge search
Document AI
Convert documents into structured information and workflow-ready signals.
- Extraction and classification
- Validation workflows
- Human review queues
Intelligent Automation
Connect AI reasoning with business processes where repetitive work can be assisted or automated.
- Workflow orchestration
- System/API actions
- Exception handling
AI Modernization
Introduce AI into existing products, operations and software estates without treating AI as a separate island.
- Legacy workflow augmentation
- AI-ready architecture
- Incremental modernization
02 · AI Systems
Build AI that can work inside the business.
AI systems become useful when intelligence is connected to trusted context, business tools, permissions and measurable workflows.
Agents & Copilots
- Role-specific assistants for employees and customers
- Tool calling and controlled actions
- Context retrieval and task memory
- Approval gates for sensitive actions
RAG & Enterprise Search
- Private knowledge bases
- Grounded answers with source context
- Permission-aware retrieval
- Evaluation sets for answer quality
AI Automation
- Document-to-workflow processing
- Classification, routing and summarization
- Human review for exceptions
- Workflow metrics and traceability
Predictive & Decision Support
- Forecasting and anomaly detection
- Business intelligence augmentation
- Decision-support interfaces
- Model monitoring and review
03 · AI Productization
Move from an AI experiment to a repeatable product.
For companies with a validated use case, Zkandavel can help shape AI into a maintainable product capability rather than a one-off demo.
1. ValidateUse case + outcome
2. PrototypeFast proof of value
3. EngineerProduction architecture
4. EvaluateQuality + safety
5. DeployCloud + integration
6. OperateObserve + improve
AI Feature Factory
Turn validated AI use cases into product features that can be released incrementally.
AI Platform Engineering
Build reusable foundations for models, retrieval, evaluation, prompts, tools and governance.
Custom AI SaaS
Design and engineer a complete AI-enabled SaaS product around a defined business problem.
04 · AI Lab
Concept demos that make the architecture visible.
These are solution concepts for discovery and discussion—not claims of a live public AI service. A production implementation is scoped against the customer's data, systems and controls.
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Enterprise Knowledge Copilot
Ask questions over approved business documents and return grounded context.
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Document Intelligence
Extract structured fields from incoming documents and route exceptions for review.
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AI Operations Agent
Combine business context, approved tools and workflow steps under human control.
05 · Industry Use Cases
Start with the workflow, then choose the AI pattern.
Healthcare
Document intelligence, knowledge search, operational assistants and workflow support.
Financial Services
Document processing, knowledge copilots, decision support and controlled automation.
Retail & E-Commerce
Customer assistants, catalog intelligence, operations automation and analytics.
SaaS & Technology
AI product features, developer copilots, support intelligence and embedded agents.
Education
Knowledge assistants, content workflows, support automation and search experiences.
Telecom & Infrastructure
Operations knowledge, incident assistance, document workflows and decision support.
06 · Responsible AI
AI that is secure, observable and accountable.
Governance is part of the engineering lifecycle. Specific controls depend on the data, users, systems and risk profile of each implementation.
Secure
Access controls, secrets management and protected system boundaries.
Private
Design data flows deliberately around customer-controlled information and environments.
Observable
Monitor requests, latency, quality signals, costs and workflow outcomes.
Evaluated
Use representative evaluation sets and review outputs before expanding usage.
Auditable
Maintain traceability for important actions, sources and approvals where required.
Human-in-the-loop
Keep people in control of sensitive or consequential workflow decisions.
Have an AI use case, product idea or workflow?
Bring the business problem first. We can map the use case, data, architecture and delivery path before a larger implementation is defined.