What This Is
We build standalone AI-powered tools that solve specific business problems — a content generator that produces SEO briefs from structured input, a classifier that routes support tickets by urgency and topic, or an analyser that scores product listings against quality criteria. Each tool is a discrete, deployable unit with defined inputs, defined outputs, and predictable behaviour.
These are not chatbots or conversational interfaces. A custom AI tool receives structured data, processes it through a managed prompt pipeline with OpenAI’s API, and returns typed results in a format your application can consume directly. A content generator might accept a topic, target audience, and keyword list, then return a structured brief with title options, section outlines, and meta descriptions — all validated against your content standards before delivery.
We have built and run this exact architecture in production. Our own platform operates a tool-based AI framework where each capability — content generation, site analysis, lead scoring — is a registered tool with its own prompt templates, output parsers, and validation rules. One client’s content tool generates over 200 structured briefs per month with a 94% first-pass acceptance rate, because the output schema enforces the quality criteria that would otherwise require manual review.
When You Need This
The trigger is usually a team spending hours on a task that follows a pattern. Someone reads input data, applies known criteria, and produces a structured output — a brief, a classification, a score, a summary. If the decision logic can be described in a rubric, it can be encoded in a tool.
Common situations: your marketing team writes the same style of product description hundreds of times with minor variations. Your operations team manually categorises incoming requests before routing them. Your content team scores articles against a checklist before publication. These are all tool-shaped problems — high volume, consistent criteria, structured output.
How We Work
Every tool starts with defining the contract: what structured data goes in, what typed result comes out, and what constitutes a failure. We agree this specification before writing any code, and it becomes both the development target and the test suite. If we cannot define the output schema precisely, the tool is not ready to build.
We implement each tool as an isolated unit with its own prompt template, output parser, and validation layer. The prompt is version-controlled and tested against real data samples, not hypothetical examples. Output validation ensures the AI’s response matches the expected schema before your application ever sees it — malformed results are caught, logged, and retried automatically.
All tools run asynchronously through job queues. Your application submits a request, receives a run identifier, and polls for completion or gets notified via webhook. This means a slow AI response never blocks your users. Token usage is tracked per-tool and per-request, so you know exactly what each tool costs to operate and can set spending limits accordingly.
What You Get
- A deployable AI tool with typed input/output schemas and validation
- Prompt templates tested against real production data
- Asynchronous processing with queue-based execution and status polling
- Automatic retry with exponential backoff on failures or malformed output
- Per-tool token tracking and cost reporting
- Integration with your existing application via API or direct service call
- Documentation covering input formats, output schemas, and error handling
Technologies We Use
- OpenAI API — GPT-4 with structured output mode and function calling for reliable, typed responses
- Laravel — job queues for async processing, service classes for tool registration and orchestration
- PostgreSQL — artifact storage, token usage tracking, and audit logging for every tool run
- Redis — rate limiting and queue concurrency management
Related Systems
Custom AI tools are components inside larger systems. A content generator lives within a content management system, a classifier powers a query management system, and an analyser feeds into a reporting dashboard. The tool handles the AI; the system handles the workflow.
Build a Tool That Does the Work For You
If your team is repeating the same structured task at volume, get in touch and we will scope a tool that handles it reliably.
