Technical Capabilities

Technical Capabilities Guide

How we combine AI-augmented development with human oversight. This is how we catch what AI misses and turn impressive demos into production systems that actually work.

AI Solutions Across Industries

We bring together UI strategy, design, and engineering under one roof ensuring your customer experience is not just designed—but built to perform as one.

Key Principles

Human Judgement at Decision Points AI proposes, architects dispose. We never let AI make critical architecture decisions without human validation.
Deep Detection Built-In An AI model can't evaluate what's risky if the context of it isn't in its training set. Our team applies field-tested intuition. We've been building for a decade.
Production Experience Over Model Confidence If your AI is most confidently wrong about something it's all-too-common. We know the edge cases models miss because we've deployed them to production across more than 300 products.

Validation Phase

Before writing production code, we audit AI-generated inputs for production readiness. This phase catches catastrophic mistakes before they become expensive.

AI PRD Sanity Check

Sycophancy Detection • — AI agrees with everything, even bad ideas. We identify where the model said “yes” when it should have pushed back.
Impossible Requirement Flagging • — AI doesn't know what's impossible. We catch contradictions and logical impossibilities that would become dead-ends.
Unstated Assumption Surfacing • — AI doesn't ask clarifying questions—it guesses. We surface those assumptions so you can validate them before development.

Prototype Archaeology

Salvage Assessment • — For AI-generated prototypes, what's production-ready, what needs rework, what needs rebuild?
Cue Engineering Detection • — AI can copy patterns it's been trained on but can't innovate. We identify where architecture was borrowed from training data.
Scale Projection • — Prototypes don't need to handle production load. We identify what breaks at scale and plan for architecture improvements.

Development Process

An augmented process that multiplies throughput while catching oversight. We use AI to move fast + experienced engineers to move right.

Human-AI Workflow

AI Acceleration, Human Verification • — AI writes the first draft. Human reviews and handles the 20% that requires judgment, experience, and intuition to dominate results.
Mandatory Human Review Points • — Architecture decisions, security logic, and data handling are 100% human-reviewed.
Vibe Detection Protocols • — Automated and manual checks for AI-generated anti-patterns, code replication, naming inconsistencies.

Quality Gates

Automated Testing • — AI-generated code needs better automated tests and unit. We write tests that cover all base scenarios.
Simplification Review • — Removing AI-generated complexity that serves no purpose. Less code = fewer bugs = faster delivery.
Production Failure Analysis • — Before deploy we apply 17 years of production knowledge. Can the process break adversarial users?

Architecture Standards

Our full-stack technical standards and patterns that balance flexibility with maintainability.

Design Principles

Separation of Concerns • — Formal separation between UI, business logic, and data layers. Changes in one area don't cascade to others.
API-First Design • — Backend APIs designed and documented before implementation. Enables parallel frontend/backend development.
Database Schema Versioning • — All schema changes are version-controlled and reversible. No manual database modifications.

Security Standards

Authentication & Authorization • — Industry standard implementations (OAuth 2.0, JWT) with role-based access control.
Data Protection • — Encryption at rest and in transit. All handling follows privacy regulations (GDPR, CCPA).
Dependency Scanning • — Automated vulnerability detection with mandatory patching policies for dependencies.

Quality Assurance

Quality is built in, not bolted on. Our QA process runs continuously throughout development.

80%+

Code Coverage

<24h

Bug Resolution

100%

Code Review

Critical Bugs at Launch

Testing Strategy

Unit Testing • — Individual components tested in isolation. Clear feedback during development.
Integration Testing • — Testing how components behave together. Catches issues that unit testing misses.
End-to-End Testing • — Full user journey validation through actual browser-based testing. Core user experience guarantees before each release.
Performance Testing • — Load testing on realistic scenarios. Identifies bottlenecks before they affect users.

CI/CD Pipeline

We set up infrastructure and processes that enable continuous delivery and operational excellence.

Automated Build & Test • — Every commit triggers automated builds and tests. Failed builds block deployments.
Staging Environments • — Identical-to-production environments for final validation before release.
Zero Downtime Deployments • — Blue-green or rolling deployments ensure zero service interruption during updates.
Rollback • — Changes can be rolled back in minutes with recovery plans tested before deployment.

Monitoring & Observability

Application Performance Monitoring • — Real-time insights into application health, error rates, and response times.
Alerting • — Proactive alerts for anomalies, threshold breaches and system errors. Prioritised by severity.
Error Tracking • — Individual errors tracked with stack traces and user context. Prioritised by severity.

AI Technologies We Work With

Foundation ModelsAI FrameworksInfrastructure

GPTLangChainLlamaIndexHugging FacePineconeCloud FunctionsPrompt Engineering

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