Repetitive work
The team spends too much time on pattern-based tasks.
AI FOR BUSINESS / ZIBATIS
AI services for business—from a real problem to an operational system
If part of your business is slow, manual, fragmented or dependent on individuals, we first examine the problem and workflow. Then we determine where AI, automation, an agent or custom software can create real value.
You do not need to know which technology you need.
BUSINESSAI SYSTEMCONTROLLED / MEASURABLE
01SIGNALCustomer, CRM, documents and events
02UNDERSTANDRetrieve, interpret and evaluate
03DECIDERules, risk and authority
04ACTSend, update, create or escalate
05MEASURETime, quality, error and outcome
PROBLEM SELECTOR
Which part of your business needs to improve?
Find the problem before choosing the tool
A business AI project starts where there is real friction, a clear owner and a measurable outcome.
Scattered information
Knowledge is distributed across people, files and systems.
Broken follow-up
Leads, customers or tasks get lost between stages.
Reports without decisions
Information exists, but the next action is unclear.
Dependency on people
Operations rely on one or two people's knowledge.
Growth without scale
More customers directly require more headcount.
Slow decisions
Collecting and interpreting information takes too long.
Repeated answers
Customers or teams ask the same questions repeatedly.
An AI agent is not the answer to every problem
The solution should match the problem. Sometimes it is an agent; sometimes automation, process redesign—or no AI at all.
Repetitive, rule-based work→Automation
Answers from known sources→Knowledge System / RAG
Decision plus tool use→AI Agent
Disconnected systems→Integration
Fragmented operations→Operational System
New AI-native product→AI Product
Unclear problem→Diagnosis
Broken process→Process Redesign
Choosing technology before understanding the problem usually adds one more tool to the organization.
What can we build?
These are not off-the-shelf products. Each is architected around your data, risk and operating workflow.
AI Agents
Agents with defined context, tools, permissions and boundaries.
EXAMPLEA sales agent that completes, qualifies and routes a lead.
Explore capability ↗AI Automation
Event → decision → action, with control and recovery.
EXAMPLEAutomated follow-up for requests with explicit conditions.
Explore capability ↗Knowledge Systems
Turn scattered knowledge into a source people and AI can use.
EXAMPLEOrganizational answers cited to approved sources.
Explore capability ↗AI Integration
Connect AI to CRM, APIs, databases, websites and existing tools.
EXAMPLERead customer status and record the outcome in CRM.
Explore capability ↗Intelligent Business Systems
Operational systems for workflow, reporting and decisions.
EXAMPLEA command center connecting signals to decisions and tasks.
Explore capability ↗AI-powered Products
Products or web apps where AI is part of the core experience.
EXAMPLEA specialist tool with structured input, analysis and human review.
Explore capability ↗What does AI actually do in a business?
A useful use case is defined by a verb and an observable outcome—not a technology label.
01Qualifies a lead
+
- TRIGGER
- Form or conversation
- INPUT
- Customer data and sales rules
- AI ROLE
- Finds missing information and proposes priority
- ACTION
- Updates CRM and routes ownership
- HUMAN ROLE
- Pricing and sensitive commitments stay human
- MEASURE
- Qualified lead rate
02Finds approved knowledge
+
- TRIGGER
- A question from team or customer
- INPUT
- Versioned documents
- AI ROLE
- Retrieves relevant sources and drafts the answer
- ACTION
- Returns an answer with citations
- HUMAN ROLE
- Ambiguity goes to the knowledge owner
- MEASURE
- Source accuracy and answer acceptance
03Turns reports into action
+
- TRIGGER
- A KPI change or event
- INPUT
- Operational data
- AI ROLE
- Explains deviation and prioritizes hypotheses
- ACTION
- Creates an alert and proposed task
- HUMAN ROLE
- Final management decision stays human
- MEASURE
- Time from detection to action
04Escalates exceptions
+
- TRIGGER
- An error or low confidence
- INPUT
- Workflow context and history
- AI ROLE
- Classifies the exception and its risk
- ACTION
- Routes a summarized case to the right person
- HUMAN ROLE
- A person makes the final decision
- MEASURE
- Handoff speed and quality
Where is intelligent work valuable in your business?
These seven questions start discovery. Instead of a fabricated percentage, the outcome should explain why a pilot is ready—or what must be prepared first.
- 01
What consumes the most team time?
- 02
Is the work repeatable?
- 03
Is the required data available?
- 04
Can a good output be recognized?
- 05
What is the cost of an AI mistake?
- 06
Must another system be involved?
- 07
Where must a person make the final decision?
GOOD PILOT CANDIDATE
The problem is bounded, measurable and testable.
NEEDS PROCESS DESIGN
The process must become clearer before AI.
NEEDS DATA / KNOWLEDGE
The use case fits, but context is not ready.
HIGH-RISK / HUMAN-CONTROLLED
AI can assist, but final decisions must not be autonomous.
LOW FIT
AI is probably not the best investment right now.
From problem to a measurable system
The Zibatis method exposes risk early and keeps scaling dependent on evidence.
- 01Opportunity Map
DIAGNOSE
Problem, workflow, users, data, bottleneck and risk
- 02System Blueprint
ARCHITECT
AI role, tools, permissions, human handoff and KPI
- 03Working Pilot
PILOT
The smallest real use case in a bounded environment
- 04Evidence
MEASURE
Accuracy, error, latency, adoption, quality and outcome
- 05Operational System
SCALE
Only validated parts enter operations
AI project examples
We label maturity clearly. A prototype is not presented as production, and unmeasured outcomes are not invented.
AI Brand Operations
- Problem
- Data, content, SEO and brand operations lived in separate reports.
- System
- A layer turning scattered signals into insights, decisions, tasks and evidence.
- Current evidence
- The current pilot tests monitoring, prioritization and decision architecture.
NIAAVA Clinic OS
- Problem
- Patient, appointment, session, payment and reporting needed one shared data model.
- System
- An operational reception and finance prototype built on one source of truth.
- Current evidence
- Current work focuses on data integrity, financial accuracy and recovery.
ZCalorie
- Problem
- Post-bariatric food tracking required realistic units, broad food data and a simple experience.
- System
- A nutrition companion for water, protein, meals, weight and trends.
- Current evidence
- The pilot evaluates the food database, selectable units, data isolation and mobile UX.
The difference is not the model; it is system design
Business First
Start with the outcome, not the model.
Controlled by Design
Knowledge, access, permission and human review are designed from day one.
Evidence Driven
No project scales just because AI feels interesting.
Built to Integrate
The system must work with existing operations and tools.
UX Matters
An intelligent system must remain understandable and controllable.
Measurable
Every pilot needs an explicit success definition.
When AI may act, boundaries matter
Control is not only a security feature; it is part of the system's value and trust architecture.
What does AI know, and which source is authoritative?
Which data and tools can it access, and at what level?
What may it do or prepare for approval?
When must it stop and let a person decide?
LoggingEvaluationVersioningPermissionMonitoringFallback
Access level, data type, processing location and logging are defined for each project's architecture. Sensitive projects include data classification, PII, retention, human approval and external providers in discovery.
How is the cost of an AI project determined?
Price is not determined by a technology name. Workflow scope, data readiness, integration complexity, risk and operating requirements shape the real cost.
01Workflow count02Integration complexity03Data readiness04Knowledge volume05Permission level06UI / application07Evaluation complexity08Monitoring09Infrastructure10Risk11Support
After diagnosis, we define a bounded pilot and a decision rule for what happens next—not a fake pricing calculator.
Everything does not need to be ready to begin
I do not know which AI I need.+
That is expected; solution selection is part of diagnosis.
Our data is not ready.+
The first step may be knowledge preparation or a source of truth.
We already have a system.+
Replacement is not the default; integration may be the answer.
The project is small.+
A good pilot is deliberately small, bounded and measurable.
We are concerned about security.+
Data, access, logging and human approval are designed explicitly.
An agent can make mistakes.+
Yes. That is why evaluation, boundaries and handoff are mandatory.
Frequently asked questions
Which businesses are a good fit for AI?+
Businesses with repeatable problems, accessible data or knowledge, a clear owner and a measurable outcome. Company size alone is not the deciding factor.
How do we know whether AI fits our problem?+
We examine repeatability, data readiness, the ability to judge output quality, the cost of error and required integrations. An unclear process should be designed first.
How is an AI agent different from ChatGPT?+
An agent can use approved knowledge and tools within a defined role, maintain state, take bounded action and hand sensitive decisions to a person.
How is automation different from an AI agent?+
Automation suits fixed, predictable rules. An agent is useful when work needs contextual interpretation, unstructured information or multi-step decisions.
Can AI connect to our CRM and existing software?+
Yes, when an appropriate API or integration path exists. Access, allowed actions, logging, recovery and human approval must be designed.
Do we need to train a custom model?+
Usually not. Existing models combined with organizational knowledge, RAG, tools, rules and evaluation solve many use cases. Training needs evidence-based justification.
What is RAG and when is it useful?+
RAG retrieves relevant information from defined sources before answering. It is useful when responses must rely on current, approved organizational documents.
How long does an AI project take?+
Timing depends on data readiness, workflow count, integrations, risk and evaluation. We first define a bounded pilot.
How is project cost determined?+
Workflow count, integration complexity, data readiness, knowledge volume, authority, UI, evaluation, infrastructure and support determine cost.
When should AI not be used?+
When the problem is unclear, the base process is broken, required data is absent, output cannot be evaluated or the cost of error exceeds likely value.
What happens when AI is wrong?+
Error is assumed from the start. Evaluation, access limits, guardrails, logging, fallback and human handoff are designed to control it.
How is organizational data handled?+
Data type, source, ownership, access, processing location, retention and external providers are defined for the project architecture.
PROJECT INTAKE / AI SYSTEMS
Send your problem for review
You do not need to know which AI to build. Describe the problem, process or bottleneck; we will assess whether AI fits and what the smallest testable version might be.
We start with the problem, not the tool.