Programming

What We Build

We design and build the software layer that surrounds AI models: custom applications, internal tools, orchestration APIs, and review interfaces. The goal is always a system your team can operate, extend, and hand to the next engineer without a tribal-knowledge tax.

Core Capabilities

Custom AI Applications End-to-end web applications and internal tools with AI at the core — from the data layer through to the user interface. Framework-agnostic; we use what fits the use case, not what we happen to know.

Orchestration & API Design Clean, versioned APIs that expose LLM capabilities to the rest of your stack. Rate limiting, authentication, structured error handling, and OpenAPI documentation included by default.

Human-in-the-Loop Interfaces Review and approval UIs for AI-generated outputs — the interface layer between model and production sign-off. Built for speed of review, not just display of output.

Workflow Automation Scheduled pipelines, event-driven triggers, and multi-step agentic workflows — designed with explicit state management and failure handling so operations staff can diagnose and recover without developer intervention.

Programming Standards

• All APIs are versioned and include an OpenAPI specification
• Test coverage targets the contract, not the implementation detail
• Dependencies are pinned and regularly audited
• No proprietary runtime dependencies — only open-source or vendor-portable choices
• Code is delivered with a runbook covering deployment, rollback, and common failure modes

When this work is useful

Programming is the right layer when an AI capability needs a real interface, permissions, state, or integration instead of another notebook. Triggers include a manual review queue that has outgrown spreadsheets, an internal tool with unclear ownership, or an agent that cannot recover safely from partial failure. The boundary of the application is defined before framework choices are made.

Delivery model

We map the user or operator journey, data contracts, authentication boundary, and failure states. A vertical slice then runs through the actual UI, API, model call, validation, and persistence layer. This exposes integration constraints early. The remaining work adds the smallest useful set of screens, jobs, permissions, tests, and deployment controls. Deliverables can include a working application, OpenAPI contract, state model, review interface, test suite, deployment notes, and runbook. The engineering service covers the reliability layer; the multi-agent insight describes when orchestration should remain bounded.

Dependencies, risks, and acceptance

The customer team must provide a process owner, an integration environment, and decisions on access and retention. Custom software can amplify a bad process if the state model is vague. Acceptance therefore covers contract behaviour, permission checks, recoverability, representative journeys, and handover: another engineer should be able to deploy, diagnose, and roll back the system from the delivered documentation. Discuss a bounded application build.

Integration notes

The smallest useful application often exposes an existing service rather than replacing it. Clear seams between UI, orchestration, persistence, and provider calls make later changes safer. We document what is intentionally not automated, which actions require confirmation, and what happens when a dependency is unavailable. These boundaries are as important to maintainability as the code itself.