Services

I work across three levels. They build on each other, but projects can start at the level that fits your situation.

Not sure where you fit? Ask AI Me

Level 1 — Process clarity & system design

Define structure and governance before introducing tools.

Use cases

Map how work actually flows today.
Identify where decisions are made.
Locate delay and error points.
Clarify ownership and data reliability.

Outcomes

Clear process and system boundaries.
Defined ownership and decision points.
Implementation-ready architecture blueprint.

Good fit

The current process is unclear or fragmented.
Automation attempts failed or stalled.
Governance and operating model need alignment.

Not a fit

No process owner can engage.
There is no operational problem to solve.

Level 2 — Workflow automation

Automate structured operational flows across teams and systems.

Use cases

Intake -> validation -> routing -> approval.
Reporting automation (weekly, monthly).
CRM / email / spreadsheet workflows.
Multi-step operational handoffs.

Outcomes

Faster turnaround.
Fewer errors.
Clear ownership and visibility.

Good fit

Teams repeat the same operational steps daily.
Manual handoffs slow down delivery.
Current workflow has a clear owner.

Not a fit

The process is undefined.
There is no measurable target outcome.

Level 3 — Applied AI systems

Implement document intelligence and retrieval-based assistants grounded in your data.

Use cases

Document intelligence: extract fields from invoices, offers, CMRs, and proposals.
Document intelligence: compare documents against a master file (Excel/database).
Document intelligence: flag exceptions for review.
Retrieval assistants: internal knowledge search with citations.
Retrieval assistants: AI-assisted triage of operational requests.

Outcomes

Less manual processing.
Faster answers.
Consistent decision logic.

Good fit

Data sources are known and maintained.
Requests are recurring and pattern-based.
Grounded, explainable behavior is required.

Not a fit

You want an unconstrained general chatbot.
Data ownership and quality are unknown.

How I work

How delivery works

Structured execution from workflow selection to scale.

1. Identify the highest ROI workflow.
2. Define system scope and boundaries.
3. Build and launch a working version (typically 2-6 weeks).
4. Improve and scale.

Outcomes

A clear implementation path.
A production-ready first release.
A roadmap for next workflows.

Good fit

A decision owner is available.
A priority workflow can be selected quickly.
The team supports staged rollout.

Not a fit

Scope is broad with no decision process.
No owner can validate outcomes.

When partners are involved

Specialist depth when projects require enterprise transformation, advanced engineering, or security alignment.

Use cases

Enterprise transformation and governance support for complex environments.
Advanced AI engineering capacity.
Automation execution capacity.
Cybersecurity and compliance alignment.

Outcomes

Specialized expertise without losing orchestration clarity.
Aligned delivery across business, architecture, and implementation.
Single-point coordination.

Good fit

Scope requires capabilities beyond a single team.
Security/compliance depth is mandatory.
Delivery must stay coordinated under one architecture lead.

Not a fit

The scope is small and self-contained.
No need for specialized depth.

Engagement models

Choose collaboration depth based on urgency, scope, and ownership.

Build Sprint (2-6 weeks): launch one working automation or AI system end-to-end.
Retainer: ongoing improvements and new workflows.
Advisory / Architecture: structured planning and system design before implementation.

Outcomes

A delivery mode with clear accountability.
Transparent scope and boundaries.
Continuity from design to operation.

Good fit

You want both speed and architecture discipline.
The roadmap extends beyond one workflow.
Leadership wants measurable checkpoints.

Not a fit

No timeline, owner, or decision path exists.
Delivery mode cannot be agreed upfront.