Knowledge assistants
Connect approved documents and structured information to a retrieval-based experience, with access boundaries and source references where appropriate.
SERVICES / AI & AGENTIC ENGINEERING
AI that understands your context and supports useful work.
Build knowledge assistants, intelligent applications and controlled agent workflows around the information and systems your business already uses.
PLOVETEK LABSEngineering intelligence. Building what’s next.
THE BUSINESS CONTEXT
A clear purpose behind the technology.
An AI feature is useful when it helps someone find a reliable answer, make a better-informed decision or complete a specific piece of work. We begin with that job, then design the knowledge, interfaces and controls needed to support it.
Discuss your priorities ↗CHALLENGES WE CAN HELP ADDRESS
- 01
Company knowledge is difficult to find and easy to misinterpret.
- 02
Teams repeatedly read documents, extract information and prepare the same kinds of responses.
- 03
An AI prototype works in demonstrations but lacks quality evaluation and safe failure behavior.
WHAT WE CAN BUILD
Practical solutions. Connected foundations.
Start with a focused capability or combine these approaches into an engagement around your requirements.
Intelligent product features
Bring classification, extraction, summarization and generation into an existing application, with clear inputs, outputs and review behavior.
Agents with useful boundaries
Connect an agent to permitted tools and workflows. Define what it may do, what requires approval and what should be escalated.
ILLUSTRATIVE USE CASE
An internal operations knowledge assistant
A practical example to discuss during discovery. This illustrates a possible solution; it is not a claim about an existing client engagement.
- 01
Find the right context
Identify the user’s question and retrieve information from the sources that user is permitted to access.
- 02
Prepare a grounded answer
Use the retrieved context to produce an answer with useful references, or ask for clarification when the information is insufficient.
- 03
Evaluate and improve
Review representative queries, inspect failure cases and update the knowledge or prompting based on agreed quality criteria.
TYPICAL ENGAGEMENT OUTPUTS
Useful work.
A reviewable result.
The exact scope is agreed during discovery. These outputs provide a starting point for defining the engagement.
- Use-case definition and scope boundaries
- Knowledge and integration design
- Working application and tool interfaces
- Evaluation examples and acceptance criteria
- Monitoring and operational handover
QUESTIONS THAT SHAPE THE DESIGN
Define the important
constraints early.
We make the key assumptions visible before implementation, so the architecture and delivery plan reflect the work ahead.
- Which information is approved and who can access it?
- How will answer quality and task completion be assessed?
- Which actions need human review or explicit permission?
- What should happen when context, confidence or an integration is missing?
COMMON QUESTIONS
Clarity for
your next step.
Can we add AI to an existing product?
Yes. A focused feature can be integrated into your current application. We first review the product workflow, available data and integration points to define a suitable scope.
How do you approach model selection?
We compare the requirements for quality, latency, cost, hosting and integration. The architecture can provide a model interface so a future change does not require rewriting the full application.
Does an agent act without approval?
Its permissions should be explicit. Routine actions can be scoped for automation, while important decisions and consequential actions should follow agreed approval policies.
EXPLORE MORE
Connected capabilities.
LET’S BUILD SOMETHING THAT MATTERS
Your next chapter.
Engineered together.
Tell us where you want to go. We’ll help you work out what to build and how to get there.