Before
Approvals, reporting, fulfillment, support, or finance depend on copying values between tools and hoping edge cases stay quiet.
Architecture and implementation for payment-grade operational systems
Fix the systems slowing the business.
Intervencija diagnoses brittle workflows, unreliable data, overloaded platforms, and legacy integrations — then ships the first production fix with architecture, implementation, and handover under one roof.
Start here
10-day System Audit Sprint
Map the workflow, data, architecture risk, ownership gaps, and cost of delay before anyone prescribes a platform, rebuild, or AI layer.
Output: decision map, risk register, architecture options, and the first production fix worth shipping.
Request auditConstraint map
Buy the removal of drag, not abstract architecture.
Intervention
Own the workflow boundary, data model, integration contract, production path, and decisions the team can defend later.
After
A working system slice with fewer manual loops, faster decisions, clearer ownership, and a handover path instead of permanent dependency.
Operating credibility
Experience in payment-grade commerce environments, European banking, SME lending, logistics platforms, ecommerce infrastructure, internal ERP/CRM systems, data-heavy operations, and cloud-native delivery.
Small senior core from Vilnius. Built to support companies where legacy systems, scale, and operational risk cannot be waved away.
Common constraints
The symptoms are operational. The cause is architectural.
The buyer usually sees slow decisions, manual work, unreliable reports, brittle integrations, or stalled AI pilots. Underneath is a system boundary, ownership, data, or production-path problem that needs one accountable owner.
01
Manual work hides inside the operating model.
Teams compensate for software gaps with spreadsheets, Slack approvals, duplicate entry, and tribal knowledge. Growth turns the workaround into a cost center.
02
Data exists, but nobody trusts the decision view.
Reports disagree, ownership is vague, metrics lack lineage, and leaders wait for manual exports before they can act.
03
Legacy integrations quietly become revenue risk.
Payment, carrier, finance, CRM, ERP, and product systems keep running until one undocumented edge case blocks revenue or operations.
04
AI pilots stall before production.
Useful prototypes stall because permissions, evaluation, logging, source data, human approval, and fallback paths were not designed before the demo.
Capabilities
Architecture only matters when it changes the operating reality.
The work is scoped around business drag: fewer manual loops, clearer ownership, safer platform choices, reliable reporting, and production software the team can maintain after handover.
Pillar 01
Architecture & System Diagnosis
Map the workflow, domain boundaries, legacy dependencies, data ownership, technical risk, cloud constraints, and delivery bottlenecks. Output is a concrete architecture decision record, risk register, and phased delivery path.
Pillar 02
Critical Workflow Engineering
Design and build internal tools, workflow automation, ERP/CRM-style systems, operational dashboards, payment/finance/logistics integrations, and approval flows that replace fragile manual coordination.
Pillar 03
Data & Decision Infrastructure
Create reliable data models, pipelines, reporting ownership, metrics definitions, analytical systems, and decision views so leaders can trust the numbers without waiting for manual reconciliation.
Pillar 04
AI-Enabled Operations
Use AI only where it improves a real workflow: private assistants over company knowledge, retrieval over governed data, secure LLM integration, permissions, evaluation, logging, fallback paths, and human approval.
Method
Senior diagnosis first. Production path second.
No bloated discovery. No abstract target-state diagrams with no delivery route. The work starts at the operating constraint and ends with ownership transferred.
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Step 01
Diagnose the operating constraint.
Trace the workflow, data, systems, team responsibilities, failure modes, and cost of delay. Name the real constraint before prescribing technology.
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Step 02
Design the target architecture.
Define boundaries, data flows, integration contracts, infrastructure posture, security assumptions, and decisions the team can defend later.
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Step 03
Build the first production path.
Ship the smallest reliable slice: working code, real data, monitored deployment, clear rollback, and a path that proves the architecture under load.
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Step 04
Transfer ownership to the team.
Document decisions, coach maintainers, simplify runbooks, and leave the system understandable enough to operate without ongoing dependency.
Engagement models
Clear entry points. No vague consulting fog.
Each engagement is scoped around business consequence, not hours. Start with diagnosis, expand into the first production fix, then retain architecture leadership only if the constraint continues.
10 business days
System Audit Sprint
Best for: leadership teams that know a system is slowing the business but need a clear map of workflow, data, architecture, ownership, and the first build sequence.
What happens: interviews, flow mapping, architecture review, data ownership review, risk analysis, and delivery sequencing.
Output: decision map, risk register, architecture options, and first-build recommendation.
4–8 weeks
Critical Build Sprint
Best for: one painful workflow, integration, reporting path, or internal tool that needs to move from workaround to production.
What happens: architecture, implementation, deployment, monitoring, operator feedback, and production hardening.
Output: a working production path, source code, decisions, runbook, and handover.
Monthly / interim
Fractional Architecture Partner
Best for: companies with recurring architecture decisions across platform, data, workflow, legacy modernization, or AI-enabled operations — without hiring a full-time executive.
What happens: decision support, design reviews, team mentoring, platform guidance, delivery unblockers, architecture governance, and critical implementation when advice alone is not enough.
Output: faster decisions, cleaner ownership, safer delivery, fewer expensive wrong turns, and a senior owner forcing architecture back into execution.
Fit filter
Built for business-critical system constraints. Not generic delivery demand.
Good fit
- A leadership owner has a painful system constraint tied to revenue, reporting, approvals, fulfillment, risk, or delivery speed.
- The business depends on reliability, reporting accuracy, workflow speed, or legacy systems that cannot simply be replaced overnight.
- The team needs senior architecture and hands-on delivery, not another unmanaged vendor queue or slide deck.
- AI is wanted only if it improves a real workflow and can be governed safely.
Bad fit
- Generic marketing website.
- Cheap app build with no ownership of outcome.
- AI showcase for optics rather than operational use.
- Chat-only product with no integration, data, permissions, or reliability requirements.
- Staff augmentation where architecture accountability stays nowhere.
Technology range
TypeScript, Python, Go, AWS, GCP, Kubernetes, Terraform, event-driven services, search, queues, serverless infrastructure, data pipelines, dashboards, internal platforms, and legacy integration paths. Tools are selected after the system constraint is understood.
Contact
Send the constraint.
Keep it concrete: what is slow, manual, risky, expensive, unreliable, or hard to own? If there is a fit, you get a direct reply within one business day.