AI & Automation Development Services

What's in scope

AI-powered apps & tools

Purpose-built AI features inside a product you already have, or a standalone tool if that's what the problem calls for — from a single well-scoped AI feature to a full AI-native application.

Chatbots & virtual assistants

Not a generic FAQ bot bolted onto a website. A conversational system scoped to what your users actually ask, grounded in your real data, with clear boundaries on what it should and shouldn't attempt to answer.

Workflow & business process automation

The unglamorous, highest-ROI category. Manual data entry between two systems that don't talk to each other. Reports that get built by hand every week. Approval chains that live in someone's inbox. These get systematized — reliably, with error handling, not just "happy path" automation that breaks the first time something unexpected happens.

AI integration into existing systems

You don't always need a new product — sometimes you need your CRM, your support desk, or your internal tools to have an AI layer added without a rebuild. This is scoped as integration work, not a rip-and-replace.

A worked example

Taking orders over WhatsApp, straight into your ERP

A distributor takes around eighty orders a day across phone calls and WhatsApp messages. Someone reads each message, checks whether the stock exists, and types the order into Tally by hand. At peak, messages get missed. Stock counts drift from reality because entry lags behind the order. And nobody can answer what a particular customer ordered last Tuesday without scrolling a chat thread.

The system: customers message the same WhatsApp number they already use, in the same plain language they already use. The assistant reads the order, checks live stock, confirms it or offers a substitute, and writes it into the existing ERP. Duplicate or repeated messages can't create duplicate orders. Anything ambiguous — an unclear quantity, a customer over their credit limit — routes to a person rather than being guessed at.

What changes: no manual re-entry, nothing missed at peak, stock counts that reflect what has actually been sold, and an order history you can query.

How we approach it

AI automation projects fail most often for one reason: nobody scoped the edge cases. A demo that works on the happy path is easy. A system that keeps working when the input is messy, the API times out, or a user does something unexpected — that's the actual engineering work, and it's where most automation projects quietly fall apart after launch.

Every engagement here starts with mapping failure modes before writing implementation code: what happens when the AI is uncertain, when an integration fails, when volume spikes. That's not extra scope — it's the difference between a demo and a system you can actually rely on in production.

Frequently asked questions

How is this different from just using ChatGPT or an off-the-shelf tool?

Off-the-shelf tools are built for the average use case. If your workflow is genuinely average, they may well be the right answer, and we'll tell you that. Custom automation earns its cost when your process has specific rules, specific data, or specific failure conditions that a generic tool doesn't handle — which is common the moment a workflow touches real business logic.

Do you build the AI model, or use existing ones?

Existing frontier models (from providers like OpenAI, Anthropic, and Google) are almost always the right foundation — training your own model from scratch is rarely justified for business automation. The engineering work is in orchestration, prompting, guardrails, and integration: making a general-purpose model behave reliably for your specific, narrow use case.

What does "automation" actually include — do you handle the ongoing maintenance?

Scope is agreed upfront per engagement — some clients want a system handed off with documentation, others want ongoing support. Both are workable; the difference is quoted and agreed before work starts, never assumed.

How long does a typical automation project take?

Depends entirely on scope — a single well-defined workflow automation can be scoped and shipped fast; a multi-system AI integration takes longer. Timeline comes out of the scoping conversation, not a generic estimate.

Can you work with our existing tech stack, or do we need to migrate anything?

Integration work is designed around your existing stack wherever possible. Migration is only proposed when it's genuinely necessary for the automation to work reliably — never as a default.