Approach

Custom software design and implementation.

Vyshyvka takes on projects where off-the-shelf software cannot represent the records, decisions, or review process.

The engagement covers product definition, interface design, implementation, data modelling, automation, deployment, and the documentation needed to operate what ships.

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Requirements before implementation

Define records, permissions, and staff actions before designing screens.

The data model must include every field, state, and action shown in the interface.

Unowned strategy

A deck names the opportunity, but nobody owns what should exist, how it works, or what must ship.

Unmodelled design

Screens look plausible while records, states, permissions, source material, and staff workflows remain unmodelled.

Disconnected implementation

Tickets ship, but interface, data, workflow logic, and operational ownership drift apart.

Unreviewed AI output

Generated output appears, but there is no evidence trail, validation, retry path, review queue, or human control.

Fragmented version

Strategy lives in documents.
Design ignores the available data.
Engineering ships disconnected tickets.
AI output has no review path.
Operations happen in spreadsheets.
Launch happens without ownership.

Integrated version

Product model guides the build.
Interface reflects staff tasks and decisions.
Data has structure and provenance.
AI is bounded by validation.
Admin tools support daily use.
Deployment and documentation are product work.

Engagement types

Projects that require custom implementation.

Layered diagram of a custom product connecting pages, workflows, data, documents, and infrastructure.

Custom product systems

Applications that give customers and staff different views of the same records, rules, and decisions.

Institutional platform diagram showing a public website connected to media, publishing, records, permissions, and staff tools.

Institutional platforms

Public sites connected to records, media, publishing, permissions, and staff workflows.

Archive system diagram showing source materials flowing into searchable records, provenance, review, and storage.

Archive & knowledge systems

Structured collections for source material that needs search, review, provenance, and publication.

AI, data, and security workflow diagram showing sources, validation, model processing, analyst review, audit trail, and writeback.

AI/data & security systems

AI workflows with source ingestion, validation, review, telemetry, writeback, and security-grade evidence handling.

Architecture

Decide what the interface must read, change, and publish.

The diagram traces those decisions from the public page to the records, staff actions, automation, and runtime underneath it.

Specimen-plate illustration showing five connected layers: public pages, domain model, workflow core, intelligence layer, and infrastructure.

Delivery process

Seven steps from scope to launch.

01Shape
Clarify the product, decision-maker, users, constraints, source material, and success conditions.
02Model
Map records, relationships, workflows, permissions, review states, sources, and system boundaries.
03Design
Design the public pages and staff screens around actual tasks and decisions.
04Build
Implement the application, data layer, workflows, storage, automation, permissions, and admin tools.
05Validate
Test real material, edge cases, review paths, failure modes, permissions, and deployment constraints.
06Launch
Deploy the product, prepare documentation, and make ownership explicit.
07Evolve
Use production feedback to repair, extend, or plan the next stage.

Related case studies

Examples from four domains.

These projects cover chamber music, household planning, security analysis, and language learning. Each demanded a different record model and set of staff or user tasks.

A bold teal chamber music homepage with editorial typography and concert calls to action.

Cultural product systemChamber Music OS

A public cultural platform with staff tools for concerts, repertoire, archive records, patrons, operations, and review-gated AI-assisted content and data pipelines.

Tall Tracker application screen showing a food operations interface with structured recipe, pantry, and planning content.

Full-stack product systemModern Recipes

An authenticated recipe platform that uses shared ingredient records for pantry inventory, USDA nutrition data, meal planning, and shopping.

Dark Narrative security product screen with operational security analytics, dense panels, and cyan accent controls.

Security analytics prototypeNarrative Security

A behavioural security analytics prototype that turns raw user activity into modelled sequences, anomaly scores, and analyst-facing investigation workflows.

Slovka vocabulary gallery with image-backed word cards, search, and filter controls.

Language-learning product systemSlovka

A multilingual learning platform with structured vocabulary, progress tools, grammar references, protected content operations, and AI-assisted pipelines for turning songs and poems into organized study material.

Typical team structure

The roles behind a custom platform.

Building this team in-house requires product leadership, design, engineering, data architecture, AI systems work, infrastructure, validation, documentation, and delivery ownership.

Senior product lead

Shape, tradeoffs, scope, success conditions.

Principal designer

Interface language, workflows, product clarity.

Senior full-stack engineer

Application, APIs/actions, permissions, production behaviour.

Data architect

Entities, relationships, provenance, migrations.

Data scientist

Exploration, cleanup, analysis, prototypes, model inputs.

AI/ML systems engineer

Extraction, enrichment, validation, review loops.

Infrastructure lead

Deployment, storage, secrets, runtime constraints.

QA / validation lead

Edge cases, failure modes, release confidence.

Documentation / delivery lead

Handoff, operating assumptions, launch readiness.

Vyshyvka shapes, designs, builds, and launches the product before a client needs a permanent product organization. The estimator below models the annualized cost of the corresponding senior roles, with separate assumptions for employer and coordination overhead.

Budget fit

Focused engagements start in the mid-five figures.

Cost grows with the number of user groups, workflows, integrations, records to migrate, and approvals the product must support.

Larger platform and AI/data builds are scoped individually after the operating requirements are clear.

Start with a project brief

Better served elsewhere

A brochure site or visual refresh.
AI strategy with no implementation owner.
Staff augmentation for an existing team.
A low-cost MVP, clone, or speculative build.
Vendor coordination without product ownership.

Built for

A custom product used by staff, visitors, customers, or members.
Public pages and internal tools that have to share the same records.
Complex records, workflows, or source material.
AI or automation that needs review and traceability.
Production software with deployment and ownership.

Describe the users, records, and decisions the software must support.

Send a project brief