Full-stack engineering, from first commit to production.
Architects, engineers, designers and testers who build software that ships — web platforms, mobile and desktop applications, integrations and AI systems — and then keep it running.
- commitfeature branch · reviewed
- buildtypecheck · lint · compile
- testunit · integration · end-to-end
- scanSAST · dependencies · secrets
- packagecontainer image · SBOM · signed
- deploystaging → canary → production
Software for the systems a business runs on.
Every line below is work PDTL engineers do: product software, the services behind it, and the integrations that make it useful on day one.
Web platforms and SaaS
React, Next.js and TypeScript front ends on Node.js, .NET or Python services. Multi-tenant architecture, single sign-on, role-based access, admin tooling and billing.
Mobile applications
Native Swift and Kotlin, or cross-platform Flutter and React Native. Offline-first sync, push notifications, and release management through the App Store and Google Play.
Desktop and systems software
Windows applications, background services and installers in C++, C#/.NET and Flutter, with auto-update and telemetry. PDTL builds and ships this in its own products.
AI-enabled software
Large language models applied to real workflows: retrieval-augmented generation over your own documents, agents with tool use, evaluation harnesses, guardrails and cost control.
APIs, integration and automation
REST, GraphQL and gRPC interfaces, event-driven services on queues and streams, ETL pipelines, and integration with the finance, CRM and operational systems a business already runs.
Cloud-native back ends
Containerised services and serverless functions, PostgreSQL and Redis, infrastructure as code, and deployment pipelines that ship small changes safely.
AI features, engineered like software.
A language model is a component with failure modes, latency and a price per call. We treat it that way: measured, bounded and reversible.
Retrieval-augmented generation
Your documents, contracts or tickets made answerable: ingestion and chunking, embeddings in pgvector or a managed index, retrieval tuned and answers grounded in cited sources.
Agents with tools
Models given narrow, permissioned tools — search a system, draft a record, raise a ticket — with approval steps where a mistake would matter.
Document and data extraction
Structured output from unstructured input: invoices, forms and reports turned into validated records your systems can accept.
Evaluation and regression testing
A graded test set for every AI feature, run in the pipeline, so quality is a number that moves rather than an opinion.
Guardrails and privacy
Scope enforced in code, refusal paths, input and output limits, redaction of sensitive fields, and provider terms checked before a byte of your data leaves.
Cost and latency engineering
Model choice per task, prompt caching, batching and streaming, with spend caps and dashboards so the bill never surprises anyone.
How a project runs.
Short cycles, visible progress, and a decision point at the end of each one. No six-month silence followed by a reveal.
01Discovery
1–3 weeksRequirements, constraints, integration points and risks. Ends with an architecture, a delivery plan and a cost you can take to a board.
02Design
2–4 weeksInterface design and a clickable prototype, plus the data model and API contracts the build will follow.
03Build
2-week sprintsWorking software every sprint, deployed to an environment you can use, with a demo and a decision at the end of each one.
04Harden
Before launchPerformance and load testing, security review, accessibility pass, monitoring and alerts, and a rehearsed release.
05Operate
OngoingReleases, monitoring, fixes and the next increment, at service levels agreed with you.
Ways to work with us.
Pick the shape that fits the problem. Most clients start with discovery and continue into a build.
Discovery
1–3 weeksRequirements, architecture options, risks and a costed plan. It ends with a document you can act on, with or without us.
Product build
Fixed scopeAn agreed scope delivered in two-week sprints, with working software demonstrated at the end of each one.
Dedicated team
RollingNamed engineers working as an extension of your team, with your tools, your board and your standards.
Run and support
OngoingReleases, monitoring, fixes and improvements to software already in production, at service levels agreed with you.
What you get with the code.
Repositories, pipelines, infrastructure definitions and documentation are yours from the first sprint, not at the end.
Reviewed change
Every change goes through pull request review and an automated pipeline before it reaches production.
Tested
Unit, integration and end-to-end suites run on each change; releases are blocked when they fail.
Infrastructure as code
Environments are defined in code and rebuildable, never configured by hand in a console.
Secure by default
Threat modelling, dependency and secret scanning, and least-privilege access from the first sprint.
Observable
Structured logs, metrics and traces, with alerts that point at a cause rather than a symptom.
Accessible
Interfaces built to WCAG 2.2 AA: keyboard reachable, screen-reader labelled, legible at any size.
Stack.
Chosen for the job. Built to evolve.
Languages
- TypeScript
- Python
- C#
- Dart
- Kotlin
- Swift
- Go
- SQL
- C++
Front end
- React
- Next.js
- Tailwind CSS
- Flutter
- SwiftUI
- Jetpack Compose
Back end
- Node.js
- .NET
- FastAPI
- PostgreSQL
- Redis
- Kafka
- GraphQL
- gRPC
Cloud
- AWS
- Microsoft Azure
- Google Cloud
- Kubernetes
- Terraform
- Docker
- Cloudflare
Data and AI
- dbt
- BigQuery
- Snowflake
- Databricks
- Anthropic Claude
- OpenAI
- pgvector
Delivery
- GitHub Actions
- Azure DevOps
- Argo CD
- Playwright
- OpenTelemetry
- Grafana
Bring us the hard part.
A system that needs replacing, a process that eats a day a week, an idea that needs proving. Tell us the problem and we will scope it honestly.