One person, both halves.
The person who diagnoses the workflow is the person who builds the system. Nothing is lost in a strategy-to-developer handoff.
Clear leverage for your business.
Turn messy work into clear business leverage.
Dispella identifies where AI can increase revenue, productivity, or competitive advantage. I then design, build, and put the system into operation — without a strategy-to-developer handoff, and with the finished asset owned by you.
Valuable work gets lost in the gaps.
Before choosing technology, the first job is to locate the work, decisions, and context that are creating drag.
Your team loses hours to research, reporting, and repeat decisions.
Important context is scattered across inboxes, folders, tools, and people.
AI experiments are easy. Reliable systems in real operation are not.
Diagnose first. Build what earns its place.
One accountable path from a frustrating workflow to a validated, governed system in real use.
Fit
Bring one costly or frustrating workflow. Determine whether AI can create enough leverage to justify a Sprint.
- Duration
- 30 minutes
- Commercial
- Free, led by the founder
- You leave with
- A fit decision and a clear next step
Sprint
Map the workflow, measure what is available, test a working prototype, and define the production recommendation.
- Duration
- Two weeks
- Commercial
- Fixed price, agreed before we start.
- You receive
- Workflow map, baseline or measurement plan, tested prototype, scope
Build
Build, deploy, evaluate, document, and roll out the validated system.
- Duration
- Four to eight weeks
- Commercial
- Scoped from the Sprint evidence
- You receive
- The governed system, evaluations, documentation, repository
After the build
Expand into more roles and workflows, or keep the system maintained. You never pay merely to retain access to what you own.
Accountability is part of the architecture.
The delivery model, ownership model, and permission model are designed together—not added after the system works.
You own the asset.
You receive a version-controlled repository containing the system logic, rules, workflows, evaluations, integration code, configuration, and documentation. Sensitive data, private records, credentials, and operational knowledge sources remain inside systems you control.
Governed by design.
Every system defines what AI may read, draft, or propose; what a human must approve; what remains prohibited; where important answers came from; and how actions are verified.
A proposal is not permission.
Every system separates what the AI can understand from what it is authorized to do, then verifies the result.
- sensepermitted sources
- reasonanalysis and draft
- gatepermission boundary
- proposeproposal only
- approvehuman authority
- verifychecked and recorded
The model is never the permission system.
A visible gate separates analysis from action. A proposal waits at the boundary until a person approves it; the result is then checked and returned to the record.
Built across research, knowledge, and content.
Different workflows. The same discipline: explicit sources, human boundaries, and systems shaped around the work.
View all six systemsClient system — in operation.
PR Media Intelligence
A media-intelligence pipeline for a PR agency. It monitors news and social sources, deduplicates material, runs AI sentiment and framing analysis with an audit trail, and produces recurring source-linked reports in the client’s language.
- RunsOn demand
- SourcesNews + social
- StoreDeduplicated
- AnalysisAnalyst in the loop
- Report cycles3 delivered
Boundary held at approve
Enterprise pilot — knowledge and archive systems.
Agency Knowledge & Archive Pilot
Two systems for a regional advertising group operating across eight markets: a cited HR and legal knowledge assistant that answers in the employee’s language, and a creative-archive distillation that turned about thirty years of work and more than a thousand pitch decks into searchable summaries with duplicate detection.
- Markets8
- Archive span~30 years
- Pitch decks1,000+
- Systems piloted2
- Runs onClient tenant
Boundary held at gate
Internal operating system — in operation.
NOMI Content Operations
A multi-role AI content operation for an e-commerce brand the founder co-owns. One topic moves through research, writing, translation in two languages, design, review, and publishing.
- Specialist roles8
- Briefs → articles6 → 6
- LanguagesMK / EN / SQ
- PublishingDraft-only
- SafeguardProvenance check
Boundary held at approve
The system remains yours when the model changes — or when our work together ends.
The system I build lives in your repository. Sensitive data stays in systems you control. The business logic is designed to be model-portable, so provider changes do not force a rebuild from zero. You keep using what you paid for whether or not we continue working together. Ongoing work is for improvement and expansion — not access.
- System logic and rulesVersion controlled
- Workflows and evaluationsDocumented
- Integration code and configurationTransferred
- Sensitive data and credentialsClient controlled
- Provider changeAdapt and retest
Bring your most frustrating workflow.
Thirty minutes. Leave with a clearer view of where AI can create leverage — and whether a Sprint is the right next step.