Artificial intelligence consultancy for executive leadership

From strategy to production.Accountable for the result.

We advise executive committees on artificial intelligence and turn their decisions into systems that work: strategy, agents, data and governance, delivered by one senior team against a business metric agreed on day one.

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Fig. 0. Illustrative case 4.2, annotated on a stock photograph.
Entry time per orderBefore A quarter of an hour **After About 3 minutes**
1 Order by email or PDF
2 Order agent
3 Normalized catalog
4 Team validation
5 ERP
Animation over a stock photograph of a distribution warehouse, illustrating case 4.2. A line runs through the system's five steps: the order arrives by email or PDF (1); the order agent reads it (2); it matches the items against the normalized catalog, which sits in the racks (3); the team validates the draft in the warehouse office, which is the human decision (4); the order enters the ERP (5). A dimension line measures entry time per order: before, a quarter of an hour; after, about 3 minutes. Rounded, indicative figures.

Focus: End-to-end AI consultancy
Areas: Strategy, agents, data and governance
For: Chief executives, operations, finance and technology
Timeline: First system in production, typically within 10 to 15 weeks
First step: A 30-minute diagnostic, free of charge

The problem

AI initiatives rarely fail because of the model. They fail at the handoffs.

A consultancy signs off the strategy, an integrator builds the pilot, Risk reviews it at the end and Operations inherits a system it did not design. Every handoff loses context, time and budget, and the signal never reaches the P&L.

AI initiative

No business case

The pilot works, but nobody set which metric it had to move or what moving it was worth.

Addressed in 2.1

Outside the operation

The agent lives in a demo, not inside the systems and processes where the work happens every day.

Addressed in 2.2

No usable data

The data exists, but not with the quality, permissions and traceability that production demands.

Addressed in 2.3

No control of risk

Nobody can answer to the risk committee or internal audit, or show compliance with the EU AI Act, so the project cannot scale.

Addressed in 2.4

P&L

Fig. 1. Where an AI initiative stalls. The signal never reaches the P&L.

Simplified diagram

What your executive committee asks us

Chief executive
Where does AI create value in our business, and in what order?
A prioritized portfolio of use cases, each with its business case, its metric and its place in a twelve-month sequence.
Operations
Which processes can run with agents without losing control?
Those that combine volume, clear rules and accessible data. We integrate the agents into your systems, with human review wherever the risk requires it.
Finance
What does it cost, when does it pay back and how do we measure it?
A fixed price per phase, a baseline measured before we start and a results report at the close of every phase.
Technology
Are our data, our systems and our security ready?
We tell you plainly in the diagnostic: where you start from, the target architecture and security controls built in by design.

Practice areas

One team across all four areas

Four areas, one system. We close the loop from start to finish.

Application development, cloud and cybersecurity are not separate service lines: they are part of every area, because no AI system works without them.

Steel conveyors in front of orderly tote shelving in a warehouse; AI-generated image. — Plate 2. The areas in an illustrative operation: decisions at the conveyor merge (2.2), data in the storage totes (2.3) and control at the signal tower (2.4).
AI-generated image; illustrative scene, not a client site.

Fig. 2. How we close the loop. The four practice areas on one system.

Simplified diagram

2.1Strategy and roadmap
What we solve
Where to invest in AI, in what order and with what expected return. We separate what moves the numbers from what only looks good in a demo.
What we deliver
- Opportunity map prioritized by value, feasibility and risk
- Business case for each initiative, with metric and baseline
- Twelve-month roadmap with a budget per phase
How it is measured
Return on investment, time to first result and the share of initiatives that reach production.
Who we decide with
Chief executive and chief financial officer
2.2Agents and automation
What we solve
AI systems that do real work inside your processes and applications, with people at the decision points that matter.
What we deliver
- Agents integrated into your systems: ERP, CRM and core platforms
- Custom applications and automations
- Continuous evaluation of quality and cost per task
How it is measured
Cycle time, cost per transaction, share of cases resolved without intervention and errors avoided.
Who we decide with
Chief operating officer and process owners
2.3Data and infrastructure
What we solve
Data that reaches the system with the quality, permissions and traceability production demands, on a platform your team can run.
What we deliver
- Data readiness assessment
- Data architecture and cloud platform
- Security, observability and cost control
How it is measured
Data coverage per use case, cost per query, availability and time to launch a new use case.
Who we decide with
Chief technology officer and IT leadership
2.4Governance, security and risk
What we solve
Every system auditable, secure and compliant by design, so your organization can answer for it before any committee.
What we deliver
- AI governance framework and risk classification
- Human oversight, decision logging and traceability
- AI-specific security testing and EU AI Act readiness
How it is measured
Incidents, audit findings and approval time for new use cases.
Who we decide with
Risk committee, compliance, security and the data protection officer

Technologies we commonly use, with no exclusivity to any vendor: Python, TypeScript, Rust, Go, Postgres, AWS, Google Cloud, Kubernetes, Anthropic and OpenAI models, LangGraph, Temporal, Vercel.

Method

Fixed price per phase. No lock-in.

Four phases, three decisions. You decide on evidence before investing more.

Every phase ends with a concrete deliverable and a leadership decision: continue, adjust or stop. The price of each phase is fixed before it starts, and if the decision is to stop, the project ends there.

Involvement of each practice area in each phase
Area	3.1 Diagnostic 2 to 3 weeks	3.2 Design and pilot 8 to 12 weeks	3.3 Scale-up 3 to 6 months	3.4 Operation Ongoing
2.1Strategy	Intensive involvement	Follow-up	Follow-up	Follow-up
2.2Agents	No activity	Intensive involvement	Intensive involvement	Follow-up
2.3Data	Follow-up	Intensive involvement	Intensive involvement	Follow-up
2.4Governance	Intensive involvement	Intensive involvement	Follow-up	Intensive involvement

3.1Diagnostic
2 to 3 weeks
- StrategyIntensive involvement
- AgentsNo activity
- DataFollow-up
- GovernanceIntensive involvement
Interviews with leadership and teams, a review of processes, data and systems, and use cases prioritized by value, feasibility and risk.
You receive
A business case with metric and baseline, a data and risk assessment, and a fixed proposal for the pilot.
Decision 1: invest in the pilot, or not.
3.2Design and pilot
8 to 12 weeks
- StrategyFollow-up
- AgentsIntensive involvement
- DataIntensive involvement
- GovernanceIntensive involvement
Architecture, data, agents and controls for one use case, in production with real users and a bounded scope.
You receive
A working system and a results report against the baseline.
Decision 2: scale, adjust or stop.
3.3Scale-up
3 to 6 months
- StrategyFollow-up
- AgentsIntensive involvement
- DataIntensive involvement
- GovernanceFollow-up
Extension to more processes, business units or markets, full integration and training for the teams who will work with the system.
You receive
The system across the planned operation, with its dashboard.
Decision 3: run it with us or transfer it to your team.
3.4Operation
Ongoing
- StrategyFollow-up
- AgentsFollow-up
- DataFollow-up
- GovernanceIntensive involvement
Monitoring, evaluation of quality and cost, improvements and periodic reviews with leadership.
You receive
A periodic report on results, cost and risk.
Periodic review with leadership; full transfer whenever you decide.

Fig. 3. Typical plan for a first use case. Indicative durations; fixed in writing in the proposal.

3.1Diagnostic

2 to 3 weeks

Interviews with leadership and teams, a review of processes, data and systems, and use cases prioritized by value, feasibility and risk.

You receive

A business case with metric and baseline, a data and risk assessment, and a fixed proposal for the pilot.

Decision 1: invest in the pilot, or not.

3.2Design and pilot

8 to 12 weeks

Architecture, data, agents and controls for one use case, in production with real users and a bounded scope.

You receive

A working system and a results report against the baseline.

Decision 2: scale, adjust or stop.

3.3Scale-up

3 to 6 months

Extension to more processes, business units or markets, full integration and training for the teams who will work with the system.

You receive

The system across the planned operation, with its dashboard.

Decision 3: run it with us or transfer it to your team.

3.4Operation

Ongoing

Monitoring, evaluation of quality and cost, improvements and periodic reviews with leadership.

You receive

A periodic report on results, cost and risk.

Periodic review with leadership; full transfer whenever you decide.

Illustrative cases

Representative composites; no client is identified

Three illustrative cases. This is what the work looks like in production.

Note

These cases are illustrative: representative composites of the kind of engagement we take on and how we approach it. They are anonymized and simplified, and they do not describe any specific client. Figures are rounded and indicative: they depend on each organization's starting point, scope and data quality, and they are not a promise of results.

Illustrative case 4.1

European insurer

Scope: Home insurance claims
Areas: 2.1 Strategy
2.2 Agents
2.3 Data
2.4 Governance
Time to production: About 15 weeks

Tabbed files, an open folder and a scanner on a desk; AI-generated image. — Plate 4.1. Claim files waiting to be opened: the claim notice (1) and the documents the agent extracts (3).
AI-generated image; illustrative scene, not a client site.

Challenge

The home claims team opened and classified every file by hand. In storm season the volume tripled and the time to first response to policyholders soared, with a direct cost in satisfaction and overtime.

What we did

We prioritized three claim types by volume and risk, and agreed a single metric with leadership: time to first response. We connected the claims system and the document archive, deployed an agent that opens the file, extracts the documentation and proposes a classification, and made human review mandatory above an amount threshold or on signs of fraud. Every decision is logged for internal audit.

Result

Results of illustrative case 4.1
Indicator	Before	After
Time to first response to the policyholder	About 2 days	Same day
Handling hours per simple claim	About 3 h	Around 1 h
Above-threshold decisions reviewed by a person	Not recorded	All, with a record

Illustrative case. A representative composite of engagements of this kind; it does not describe a specific client. Rounded, indicative figures that cannot be extrapolated without a diagnostic.

1Policyholder claim
2Intake agent
3Document extraction
4Human review above threshold
5Claims system

Fig. 4.1. Deployed system, simplified.

Illustrative case 4.2

Industrial distributor

Scope: B2B orders and catalog
Areas: 2.1 Strategy
2.2 Agents
2.3 Data
Time to production: About 10 weeks

Distribution warehouse with pallet racking, annotated with the system's five steps and a dimension line. — Plate 4.2. The warehouse from Fig. 0, with the deployed system: from the order (1) to the ERP (5), with team validation (4).
Stock photograph; not a client site.

Challenge

A distributor with around 40,000 SKUs received most orders by email and as PDFs, with ambiguous descriptions and customer codes that differed from its own. The office team spent much of the day keying them into the ERP, and wrong item codes caused returns and delays.

What we did

We started with what was really holding the project back: we normalized the catalog and each customer's code equivalences. On that basis we deployed an agent that reads every order, identifies the items and leaves a draft order in the ERP for the team to validate. When something is unclear, the agent flags it; it does not resolve it on its own.

Result

Results of illustrative case 4.2
Indicator	Before	After
Entry time per order	A quarter of an hour	About 3 minutes
Orders with a wrong item code	1 in 20	1 in 100
Share of the team's day spent keying orders	More than half	Less than a fifth

Illustrative case. A representative composite of engagements of this kind; it does not describe a specific client. Rounded, indicative figures that cannot be extrapolated without a diagnostic.

1Order by email or PDF
2Order agent
3Normalized catalog
4Team validation
5ERP

Fig. 4.2. Deployed system, simplified.

Illustrative case 4.3

Clinic group

Scope: Appointments and patient service
Areas: 2.2 Agents
2.3 Data
2.4 Governance
Time to production: About 14 weeks

Stone reception counter, glass doors and a clinic waiting room; AI-generated image. — Plate 4.3. Reception and waiting room of a clinic: where calls arrive (1), referral to a consultation (3) and the schedule that fills or is left with gaps (4).
AI-generated image; illustrative scene, not a client site.

Challenge

A network of private clinics missed a significant share of calls at peak hours and suffered a no-show rate that left gaps in the schedule. Any solution had to process health data, a special category under the GDPR, without compromising privacy or the relationship with the patient.

What we did

We started with governance: a data protection impact assessment with the data protection officer, a risk classification of the system and clear limits on what the agent can and cannot do. We then deployed an agent that handles appointments by phone and messaging, sends reminders and reschedules, with data hosted in the European Union. Any request with clinical content is referred to medical staff.

Result

Results of illustrative case 4.3
Indicator	Before	After
Unanswered calls at peak hours	Almost 1 in 3	Fewer than 1 in 10
Appointments lost to no-shows	1 in 8	1 in 15
Clinical requests referred to medical staff	Not recorded	All, with a record

Illustrative case. A representative composite of engagements of this kind; it does not describe a specific client. Rounded, indicative figures that cannot be extrapolated without a diagnostic.

1Call or message
2Appointments agent
3Clinical referral
4Clinic schedules

Fig. 4.3. Deployed system, simplified.

Data hosted in the European Union.

Firm

Principles written into every proposal

One senior team, from the first leadership meeting to the system in production.

Zyntexia Solutions is an end-to-end artificial intelligence consultancy. We bring strategic judgment and senior engineering into the same team: the people who define the strategy with your executive committee are the people accountable for the system working in production.

We speak the language of your executive committee and the language of your engineers. That fluency in both keeps the strategy from staying on paper and the technology from being built without a purpose.

How we work with you

Team: 3 to 6 senior people per project
Price: Fixed per phase, with the scope in writing
Reporting: Report to leadership at the close of every phase
Continuity: We run the system or transfer it to your team; you decide

Principles

5.1
The metric before the model.
Every initiative starts with a business metric agreed with leadership and a measured baseline. If it cannot be measured in production, we do not build it.
5.2
Whoever signs the proposal leads the project.
The senior consultant who presents the proposal leads it to the end. There is no team that sells and another that delivers, and critical work is never subcontracted.
5.3
Governance from day one.
Risk assessment, traceability and human oversight are part of the design, not a final review. We document what your risk committee, your auditors and the EU AI Act will ask for.
5.4
Technology independence.
We choose models, clouds and vendors for their fit with your case, and we justify every choice in writing. Your architecture is not tied to us or to a single vendor.
5.5
Your system and your know-how stay with you.
The code, the data and the documentation are yours. We do not use your data to train models or for other engagements, and we leave your organization able to run and improve the system.

Your organizationExecutive committeeSponsorship and a decision at the close of every phase
ZyntexiaEngagement partnerLeads the engagement end to end and answers to leadership
Your organizationProcess ownerDefines what a good result looks like in operations
ZyntexiaAgent and application engineeringDesigns and builds the system inside your processes
Your organizationTechnology and dataSystems, access and architecture
ZyntexiaData architecture and platformData, integration, cloud and observability
Your organizationRisk and complianceRisk criteria, data protection and approval
ZyntexiaAI governance and securityRisk classification, controls and traceability

Fig. 5. Typical project team: 3 to 6 senior people. Every role has a counterpart in your organization.

Contact

Reply within 24 business hours

Thirty minutes to find out whether it makes sense.

A conversation with a senior consultant to understand your situation, identify where AI can move your numbers and decide whether a next step makes sense. Free of charge and without commitment.

What happens next

6.1
Within 24 business hours
A senior consultant, not a salesperson, reads your message and proposes a date.
6.2
A 30-minute diagnostic
We pin down the problem, the metric that matters and the smallest scope worth taking to production. You leave with a clear opinion, even if we do not work together.
6.3
A proposal within a week
Fixed scope, timeline and price, in writing.

Prefer email? Write to us at info@zyntexia.tech.

From strategy to production.Accountable for the result.

AI initiatives rarely fail because of the model. They fail at the handoffs.

No business case

Outside the operation

No usable data

No control of risk

Where does AI create value in our business, and in what order?

Which processes can run with agents without losing control?

What does it cost, when does it pay back and how do we measure it?

Are our data, our systems and our security ready?

Four areas, one system. We close the loop from start to finish.

2.1Strategy and roadmap

2.2Agents and automation

2.3Data and infra­structure

2.4Governance, security and risk