PBYTEAI LAB
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AI systems built around real operations.

We connect company knowledge, operational data and everyday workflows to useful software — from technical support copilots to production intelligence and AI-assisted solution design.

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Applied AI, explored through working concepts.

Built around your data

Knowledge with context.

Designed for real workflows

Useful where work happens.

Private when required

An appropriate data boundary.

Human in the loop

People own the decisions.

Explore the concepts

Three operational questions.
Three practical starting points.

Concept 01 · PByte / AI Lab

Industrial Service Copilot

The right knowledge. At the point of service.

The answer is often there — scattered across manuals, past tickets and the memory of your most experienced engineer.

Bring technical documents, machine context and service history into one source-backed workspace. Help a technician find a useful starting point, then keep an engineer in control.

Inside the concept Web application design

01 / 03
Inspect the alarmExplore the affected asset with its event history and source-backed investigation notes.

Illustrative interface concepts, not released products. Fictional data; diagrams and charts are illustrative, not validated engineering results.

Interactive case · fictional data

Line 02 has stopped. Pick up the case, compare the evidence and prepare the engineer’s handover.

Try the case
Try the interactive sample Local demo · fictional data
Service desk / Knowledge assistantConcept · Demo data

Technician’s question

Machine reports feeder position error E142. What should I check?
Source-backed concept answer3 procedures · 7 similar incidents

Start with the context,
not an assumption.

The mock evidence groups possible causes into calibration drift, sensor contamination, a communication timeout or an incorrect setup. A qualified engineer needs to determine which, if any, applies.

Suggested review checklist

  1. Confirm machine identity, feeder ID and recorded position.
  2. Have an authorised technician review sensor condition.
  3. Review whether a manufacturer-approved calibration diagnostic applies.
  4. Read the last maintenance record and setup changes.
  5. Compare recent alarms with earlier service incidents.

Illustrative content only — not equipment instructions. Follow approved documentation and authorised service procedures.

The workflow

  1. 1Ask in context
  2. 2Retrieve relevant evidence
  3. 3Review with an engineer
  4. 4Capture the resolution

Potential value: faster diagnosis, less dependence on a single expert and service knowledge that stays with the team.

  • RAG
  • Document Intelligence
  • Service History
  • Structured Data
  • Semantic Search
  • Human Approval
How this concept works

A production implementation would retrieve permission-scoped passages and related service records, attach citations and route uncertain or consequential decisions to an authorised engineer. This demo uses prewritten, fictional evidence; it does not diagnose equipment.

Concept 02 · PByte / AI Lab

Factory Intelligence

See the line. Understand the story.

Production dashboards show what happened. Finding why still means piecing together events, shift notes and maintenance records.

Connect production signals with operational context. Ask a focused question, trace the supporting events and give your team a clearer next step.

Inside the concept Web application design

01 / 03
See the production flowLocate the interrupted stage and understand which downstream stations are affected.

Illustrative interface concepts, not released products. Fictional data; diagrams and charts are illustrative, not validated engineering results.

Try the interactive sample Local demo · fictional data
Operations desk / Morning shiftConcept · Demo data

Production overview

A shared view of the floor.

Fictional plant · fixed shift snapshot, not a live feed

Line 01 OEE
82.4%
Within demo baseline
Line 02 OEE
69.1%
Needs review
Line 03 OEE
87.7%
Within demo baseline

Line 02 · process sequence

  1. 01PrinterRunning304 units/h0 alerts
  2. 02SPIRunning302 units/h0 alerts
  3. 03PlacementAttention248 units/h2 alerts
  4. 04ReflowRunning246 units/h0 alerts
  5. 05AOIRunning245 units/h0 alerts
  6. 06X-RayIdle0 units/h0 alerts

Ask about the operation

Investigation brief

The interruption started at placement.

Between 09:42 and 11:10, feeder-related interruptions on the placement station coincided with lower Line 2 performance. Downstream stations then spent 17 minutes waiting for boards.

412 unitsEstimated lost planned output · demo
  1. 0109:42 · Feeder error
  2. 02Placement interrupted
  3. 0317 min · Downstream starvation
  4. 04Line 02 OEE · 69.1%

EvidencePlacement event log · MES output plan · shift context

Suggested next step

Investigate feeder positions 24 and 31, and compare their alarm history with yesterday. Confirm the cause with the shift engineer.

We build the intelligence layer above the machines.Production events · MES · maintenance · operational context

The workflow

  1. 1Connect production events
  2. 2Correlate the timeline
  3. 3Explain the evidence
  4. 4Prioritise investigation

Potential value: quicker shift reviews, clearer bottleneck investigations and better-informed maintenance priorities.

  • Time-Series Data
  • Event Correlation
  • MES Integration
  • Root Cause Analysis
  • Operational Analytics
  • AI Copilot
How this concept works

A real system would normalise machine and MES events, align timestamps and combine them with product, shift and maintenance context. Correlation suggests where to investigate; it is not proof of causation. Values below are a fixed fictional snapshot, not live telemetry.

Concept 03 · PByte / AI Lab

AI Solution Configurator

From a rough requirement to a reasoned starting point.

An RFQ arrives as a document. Turning it into a solution means extracting constraints, finding gaps and making assumptions visible.

Structure the brief, explore a possible architecture and prepare an engineer-reviewed proposal draft — with the open questions still in view.

Inside the concept Web application design

01 / 03
Read the requirementTrace extracted values back to the RFQ and keep missing requirements visible.

Illustrative interface concepts, not released products. Fictional data; diagrams and charts are illustrative, not validated engineering results.

Try the interactive sample Local demo · fictional data
Solution desk / Requirement to draftConcept · Demo data

01 / The requirement

Start with the brief.

Use the labels in this sample. Extraction runs only in this browser; nothing is uploaded or sent to an AI service.

Local field extraction + illustrative reference architecture. Not a capacity calculator.

02 / Ready for review

A clearer brief.
A better first conversation.

Analyze the sample to see structured requirements, a possible process architecture and the questions an engineer still needs to answer.

  • Requirements, not assumptions
  • Reasoning for each process stage
  • A draft that stays in your control

The workflow

  1. 1Read the requirement
  2. 2Structure the constraints
  3. 3Review a concept
  4. 4Draft the proposal

Potential value: a more consistent first response, fewer missed requirements and more engineering time spent on the decisions that matter.

  • Document Extraction
  • Product Knowledge
  • Constraint Matching
  • Recommendation Engine
  • Proposal Automation
  • Human Review
How this concept works

A production implementation could extract requirements from documents and match them to a maintained capability catalogue. This browser demo only reads labelled text fields and shows a generic reference architecture. It does not use an AI model, calculate capacity, select a vendor or validate an engineering solution.

Beyond the examples

The building blocks of useful AI.

Different workflows need different tools. These capabilities can be combined around the job that actually needs doing.

01

Enterprise Knowledge AI

Find the answer. Keep the source.

Search company knowledge with citations, permission-aware retrieval and an explicit route to human review.

02

Workflow Agents

Move the work forward.

Triage requests, gather context, classify documents and prepare reports or drafts. Require approval before consequential actions.

03

Document Intelligence

Give unstructured work a structure.

Extract and organise information from RFQs, service forms, invoices, specifications and inspection reports.

04

Private AI

Choose the right operating boundary.

Evaluate cloud, private-cloud or on-premises options against your data, model, security and operational requirements.

The system behind the experience

From company data to useful AI.

Connect the right sources. Add retrieval, models and rules. Put the result into a tool your people can actually use.

Company data flows through data integration, retrieval, models and rules into the PByte application layer, serving employees, technicians, managers and customers. Permissions, audit logs, citations, human approval and monitoring span the system.
Illustrative architecture. The integration and deployment model depend on the workflow.
Read the architecture and safeguards
  1. Company data: documents, databases, APIs, machine and production data, service history and business systems.
  2. Data & integration layer: connect, normalise and apply access boundaries.
  3. AI layer: retrieval, models and rules grounded in the permitted context.
  4. PByte application layer: tools integrated into the actual workflow.
  5. People: employees, technicians, managers and customers.

Across every layer: authentication, permissions, audit logs, source citations, human approval and monitoring.

How we work

Start focused. Learn from the work.

  1. 01

    Identify the workflow

    Start with a specific bottleneck and the people who own it.

  2. 02

    Connect the right data

    Understand sources, quality, access and operational context.

  3. 03

    Build a focused prototype

    Test one useful path with your team and representative data.

  4. 04

    Measure and harden

    Evaluate usefulness, failure modes, security and reliability.

  5. 05

    Integrate into operations

    Fit the tool to daily work, with monitoring and ownership.

Useful means accountable

AI needs guardrails.

A good AI system should show where information came from, make uncertainty visible and involve people when a decision matters.

Source-backed answers
Make the supporting evidence inspectable.
Role-based permissions
Respect who can see and do what.
Human approval
Keep consequential actions under human control.
Auditability
Make decisions and changes traceable.

Start with a real question

Have a workflow
that should be smarter?

Show us the process, the data and the bottleneck.
We’ll explore where AI could genuinely help.

Start a conversationinfo@pbyte.bg