AI & AUTOMATION
Connect your business data, tools, and approvals with practical AI. We build assistants and automated workflows that help teams find answers, process information, and complete everyday work.
Discuss an AI & Automation Use Case
KNAYK LABS builds AI assistants, retrieval-augmented generation (RAG) systems, and automated workflows connected to business data, applications, tools, and human approvals.
Four connected capabilities. Shaped around the work your business needs to do.
See how AI can support customers, simplify operations, and help teams make informed decisions.
CustomersCustomers
Questions, new enquiries, and setup tasks can leave customers waiting for the right person.
OperationsOperations
Documents and requests often move through disconnected tools and repeated data entry.
Insights & qualityInsights & quality
Reports, signals, and test results can be difficult to review consistently as information grows.
A WORKFLOW IN PRACTICE
An example of how AI-assisted document processing could work. The exact rules and integrations depend on your business.
An invoice arrives through an agreed upload or inbox, together with the purchase-order data available to your team.
AI extracts supplier, amount, and line-item details. Validation checks compare them with the source document and business rules.
A reviewer sees extracted fields alongside their source. Missing details, mismatches, and uncertain results stay in a review queue.
After approval, the integration creates or updates a draft record in the finance system and records the outcome. Payment authorization stays within your existing controls.
Evaluation and controls belong in the design. Their implementation is defined around the workflow’s risks and agreed scope.
Check the work against representative examples and make supporting information easy to inspect.
Evaluate answer quality, extraction errors, and failed actions separately. Where retrieval is used, show source references so a reviewer can inspect the underlying information. A citation helps review; it does not guarantee correctness.
Define what the system can access and which decisions need a person before action.
Agree permissions, credential handling, and the actions automation may take. For sensitive or irreversible decisions, show the proposal and relevant context to an authorized reviewer, who can approve, reject, or correct it.
Give uncertain results and operational failures a clear path to review and recovery.
When evidence is missing or validation fails, ask for clarification, abstain, or route the task for review. Plan appropriate logs and monitoring, accounting for privacy and retention. Re-evaluate when models, prompts, data, or business rules change.
Bring a process that creates repetitive work or slows decisions. We can define a focused assessment before deciding what to build.
WORKFLOW ASSESSMENT
Map the process, review available data and integrations, and identify where AI adds value compared with simpler automation.
FOCUSED PROOF OF CONCEPT
Where appropriate, test one agreed workflow using representative examples and a limited set of integrations.
Deliverables, access requirements, timing, and fees are agreed before work begins. A proof of concept is a validation step; production deployment requires a separate readiness review.
KNAYK can support the journey from discovery and AI strategy through architecture, proof of concept, implementation, integration, deployment, monitoring and optimization.
Understand the workflow, available data, users, and operational constraints.
Test a bounded approach against representative examples and review its limits.
Connect approved data and tools, implement controls, and test the end-to-end workflow.
Review readiness, agree operational ownership, and learn from monitored performance.
Start with representative examples of the work, the desired outcome, and the rules people currently follow. We assess availability, quality, permissions, and whether sensitive data can be minimized or replaced with suitable test data.
We first review available APIs, authentication, vendor restrictions, and data exchange options. Access approvals or additional licenses may be needed. The integration approach is agreed before implementation.
Model selection depends on task quality, latency, cost, deployment constraints, and data handling requirements. A focused evaluation can compare suitable options; there is no single model assumed for every workflow.
This depends on the selected provider, deployment configuration, and contractual terms. Data usage, retention, and access requirements need to be reviewed and agreed before business information is sent to a service.
Define acceptance criteria around the actual task, test representative cases, and inspect failure modes. Use validation and human approval where needed. AI outputs can be incorrect, so the workflow needs an explicit exception path.
Scope, data preparation, integration complexity, evaluation, and deployment needs affect build cost. Ongoing costs can include model usage, infrastructure, monitoring, and support. These are assessed for the proposed approach rather than quoted as a universal price.
Monitoring, maintenance, model or prompt changes, and further improvements can be scoped as ongoing work. Support coverage, escalation, operational ownership, and re-evaluation responsibilities are agreed explicitly.
Contact us with the workflow you want to improve so we can discuss what relevant material is available to share. The invoice example and workflow animation on this page explain an approach; they are not client case studies or measured performance results.
Tell us about the workflow, problem or opportunity. We'll help assess where AI and automation can create practical value.
Discuss Your Use Case