01
Custom Software Development
From product idea to a platform that survives its third year.
Systems built around how your business actually works — not around a template.
- Java
- Spring Boot
- Kotlin
- Node.js
Software engineering & product operations
Software Product Engineering for Businesses That Ship
Accelerating delivery with senior teams, pragmatic architecture and applied AI
POC helps businesses design, build, modernise and scale digital products and software platforms — with small senior teams that take responsibility for outcomes, not ticket counts.
Reference platform architecture
Positioning
Software measured by what it returns
Every engagement starts from the commercial result you need and ends with a system your own team can run, extend and rely on.
How we work- 01Revenue
- Products and platforms that open a new channel, shorten a sales cycle or let you serve customers you cannot serve today.
- 02Operating cost
- Manual, repetitive work automated, and infrastructure sized to the actual load rather than to a worst-case guess.
- 03Risk
- Unsupported runtimes, single points of knowledge and untested release paths removed before they become an incident.
- Work is scoped around a commercial outcome, not a feature list.
- Small senior teams and direct communication — no account layer in between.
- You own the code, the documentation and the know-how from day one.
Services
Engineering services, end to end
Engagements range from a two-week assessment to a multi-year platform build. The starting point is usually smaller than people expect.
All services02
Product Development
Zero to a product that has actual users and a roadmap.
Product thinking joined to engineering — discovery, MVP, iteration and scale.
- TypeScript
- React
- Astro
- Node.js
03
Web Application Development
Interfaces that stay fast when the data gets ugly.
Web platforms, dashboards and portals built for real data volumes and real users.
- TypeScript
- React
- Astro
- Vite
04
Backend Engineering
The unglamorous layer everything else depends on.
APIs, services, data models and integrations designed for correctness under load.
- Java
- Spring Boot
- Kotlin
- Node.js
05
AI & Automation
Practical AI aimed at real operational cost, not at the press release.
LLM features, retrieval systems and workflow automation with measurable payback.
- LLM integrations
- RAG
- AI agents
- Python
06
Application Modernization
Turn a system nobody wants to touch into one people can change.
Incremental modernisation of legacy platforms — without a big-bang rewrite.
- Java
- Spring Boot
- Kotlin
- Node.js
07
Cloud & DevOps
Infrastructure that is boring on purpose.
Reproducible environments, automated delivery, and observability worth paging on.
- AWS
- Azure
- Docker
- Kubernetes
08
Technical Consulting
An outside read on the decision you are about to make.
Architecture review, due diligence, and second opinions with a written verdict.
- Architecture review
- Due diligence
- Delivery assessment
Why POC
What you get that a staffing vendor will not give you
No claims we cannot back up. These are operating principles you can hold us to during the engagement.
- 01
Engineering with product judgement
We push back on requirements that will not survive contact with users. Scope gets argued about before it is built — the cheapest place to have that argument.
How we work - 02
Architecture proportional to the problem
The right architecture is the simplest one that meets the load, the compliance requirement and your team's ability to operate it. Frequently that is less infrastructure than you were quoted.
Technical consulting - 03
Built to be handed over
Tests, decision logs, runbooks and deployment automation ship with the software. You can take the system in-house whenever you choose, without a rescue project.
Custom development
Industries
Operational depth across the industries we serve
Domain knowledge shortens discovery and prevents the class of mistake that only appears in production.
All industriesFinTech
Software for financial products — payments, ledgers, onboarding and reporting — built with the auditability, idempotency and operational discipline the domain demands.
What usually goes wrong
- Payment flows that must be idempotent and reconcilable end to end
- Auditability and data retention under regulatory scrutiny
- Third-party provider integrations with inconsistent reliability
SaaS
SaaS product engineering — tenancy models, billing, permissions, onboarding and the operational tooling a growing subscription business needs.
What usually goes wrong
- Tenancy and data isolation decisions that are expensive to reverse
- Billing, plans and entitlement logic sprawling across the codebase
- Enterprise buyers demanding SSO, audit logs and role granularity
E-commerce
E-commerce engineering — high-performance storefronts, catalogue and pricing systems, order orchestration and integrations with ERP, PIM and logistics.
What usually goes wrong
- Page performance directly affecting conversion
- Catalogue, pricing and stock accuracy across channels
- Order orchestration spanning payment, warehouse and delivery
Travel & Hospitality
Travel and hospitality platforms — availability and booking engines, channel and supplier integrations, dynamic pricing and operational tooling.
What usually goes wrong
- Availability calculations under concurrent booking pressure
- Supplier and channel integrations in inconsistent formats
- Cancellation, modification and refund flows with real financial impact
Enterprise Software
Enterprise software engineering — internal platforms, workflow systems, integration layers and modernisation of business-critical applications.
What usually goes wrong
- Business logic encoded in systems nobody documented
- Integration sprawl across ERP, CRM and homegrown tools
- Release processes constrained by risk and compliance
Data & Analytics
Data engineering and analytics platforms — ingestion, transformation, warehousing, streaming and the quality controls that make reporting defensible.
What usually goes wrong
- Reports that disagree with each other and with the source systems
- Batch windows that no longer fit the business day
- Pipelines that fail silently and are noticed a week later
Case studies
Selected engagements
Problem, constraint, decision, consequence. No vanity metrics, no logos we are not allowed to name.
All case studiesSample contentSample engagement. Illustrative scenario used while the site is in build — not a named client project.
fintech2025
Payment Platform Modernization
A ten-year-old payment monolith moved from quarterly release windows to weekly deployments, without a rewrite and without a settlement incident.
- Release cadence moved from quarterly windows to weekly deployments
- Reconciliation lag reduced from overnight to near real time
- Java
- Spring Boot
- Kotlin
- PostgreSQL
- Kafka
travel and hospitality2025
Enterprise Booking Platform
A booking platform serving multiple properties and channels, rebuilt around a single availability model and a back-office console operators actually wanted to use.
- Overbooking incidents eliminated in the peak season following launch
- Channel synchronisation latency reduced from minutes to seconds
- Kotlin
- Spring Boot
- PostgreSQL
- Redis
- Kafka
saas2026
AI-Assisted Operations Platform
Document intake and support triage moved to a draft-first AI workflow with human approval, measured against an evaluation set built from two years of historical cases.
- Routine classification and extraction handled as drafts, with review rather than authoring
- Quality tracked as a published accuracy metric per task type, not an impression
- Python
- TypeScript
- LLM integrations
- RAG
- Vector databases
Expertise
The stack we are opinionated about
Not a logo wall. These are the technologies we run in production, argue about, and will tell you when not to use.
Backend
Transactional systems, domain modelling, integrations.
- Java
- Spring Boot
- Kotlin
- Node.js
- REST & gRPC
- Event-driven design
Frontend
Data-dense interfaces, design systems, performance budgets.
- TypeScript
- React
- Astro
- Vite
- Design systems
- Accessibility (WCAG AA)
Data
Storage, streaming and the correctness checks around them.
- PostgreSQL
- Redis
- Kafka
- CDC pipelines
- Warehouse modelling
- Data quality
Cloud & platform
Reproducible environments and delivery you can trust.
- AWS
- Azure
- Docker
- Kubernetes
- Terraform
- CI/CD & observability
AI
Applied to operations, with evaluation attached.
- LLM integrations
- RAG
- AI agents
- Workflow automation
- Evaluation harnesses
- Guardrails
How we work
Six steps, and you can stop after any of them
Each phase produces something you own outright — a document, a decision, a running system. There is no point in the engagement where walking away leaves you with nothing.
- 01
Discovery
1–2 weeks
Business goal, users, constraints, integration surface and the riskiest assumption. Output: a written problem statement everyone recognises.
- 02
Technical assessment
1–2 weeks
Existing systems, data, dependencies and delivery process reviewed. Output: risk-ranked findings with effort estimates.
- 03
Solution design
2–3 weeks
Architecture, domain model, delivery plan and cost envelope. Output: a plan you can execute with us or without us.
- 04
Development
Ongoing
Two-week increments, demoable builds, automated tests and a running decision log. Output: working software in your repository.
- 05
Launch
2–4 weeks
Hardening, load testing, observability, runbooks and a rehearsed rollback. Output: production, without the drama.
- 06
Continuous improvement
Ongoing
Measurement, iteration and gradual handover to your team. Output: a system that keeps improving after we leave.
Content hub
Notes from inside the work
Written by the people doing the delivery, about decisions we actually had to make.
All articlesHow AI Can Improve Software Delivery Operations
Where AI measurably helps software delivery — code review support, test generation, incident context assembly and documentation — and where it quietly makes things worse.
When Should You Build a Proof of Concept?
A decision framework for proofs of concept — when a PoC earns its cost, how to scope one so it answers a single question, and the failure modes that turn PoCs into accidental production systems.
How to Modernize a Legacy Java Application
A sequencing guide for modernising a long-lived Java system incrementally — assessment, characterisation tests, strangler-fig extraction and runtime upgrades — without a big-bang rewrite.
Practicalities
Questions we get before the first call
01How do engagements usually start?
With a short paid discovery — normally one to two weeks. It ends in a written problem statement, an architecture direction and a delivery plan that is yours to keep, whoever builds it.
02What size of team do you provide?
Small senior teams, typically two to six people, embedded in your process. We do not scale by adding junior headcount to a fixed-price scope.
03Do you work fixed-price or time and materials?
Discovery and assessment phases are fixed-price because the scope is genuinely known. Build phases run time and materials against a capped, re-planned-every-increment budget.
04Can you work with our existing team and codebase?
Yes — that is most of our work. We use your repositories, your review process and your ticket tracker, and we document decisions so knowledge stays in your organisation.
05Who owns the code and the IP?
You do, from the first commit. Everything is developed in your repositories or transferred at each milestone, with no dependency on our infrastructure.
06What happens after launch?
Whatever you need: a support and improvement retainer, a gradual handover to your team, or a clean exit with documentation and runbooks. All three are normal outcomes.
Start a conversation
Have a technical challenge?Let's talk.
Tell us what you are trying to build, fix or decide. If we are not the right team for it, we will say so and point you somewhere better.