Somebody in your business
is doing this by hand.
Pick the situation that sounds like yours.
Each needs a different piece of work. Select one and the panel changes — no scrolling past four capabilities to reach the one you came for. Not sure which applies? The assessment establishes that before any scope is written.
What AI Automation Involves
What AI development involves
AI development is the design, engineering and deployment of applications that use large language models and related techniques to perform work inside a business. It covers conversational assistants across web and messaging channels, data and document processing systems, retrieval-based knowledge assistants, autonomous agents that execute multi-step tasks, and the integration of model capability into existing products.
Almost every engagement we take begins as a description of manual work. Not "we want an AI strategy" — but four people processing supplier invoices, or a support inbox nobody can keep up with, or a database somebody maintains by copying rows between two systems.
That is the right place to start, because the value is measurable before anything is built.
The models themselves are commoditised. Access to them stopped being a differentiator some time ago. Three other things determine whether an AI project produces value.
- Whether the data is ready
A system reading from disorganised material returns confident, wrong answers — worse than no system, because people act on them. - Whether it reaches production
Demonstrations are forgiving. Live systems, real users and edge cases are not. - Whether it was built like software
An AI application still needs authentication, error handling, logging, monitoring, deployment and a maintenance path. Model capability is one layer of an application, and usually not the difficult one.
Chatbot and WhatsApp Business API development
Assistants deployed where your customers already are — your website, WhatsApp, or inside your own product.
- —WhatsApp Business API setup, verification and template approval
- —Web chat and in-app assistant development
- —Conversation flow design across channels
- —Retrieval-based answering from your own content
- —Qualification, routing and CRM capture
- —Booking, confirmation, reminder and order-status flows
- —Human handover carrying the full conversation context
- —Adversarial testing before launch, conversation review after
AI data processing and document intelligence
The least glamorous capability here and frequently the most valuable — the work being replaced is measurable, repetitive and already costing a known number of hours.
- —Document extraction — invoices, contracts, forms, statements, reports
- —OCR combined with model-based extraction for scanned material
- —Classification and routing of incoming documents or messages
- —Structured output into your database, CRM or ERP
- —Validation rules and confidence thresholds, with human review queues
- —Bulk data cleaning, normalisation and deduplication
- —Batch and real-time processing pipelines
- —Exception handling, so low-confidence cases reach a person
RAG and knowledge assistant development
Answers traceable to a source is the difference between a tool a regulated business can adopt and one it cannot.
- —Document ingestion, cleaning and chunking
- —Vector database implementation and retrieval tuning
- —Source citation on every answer
- —Hybrid retrieval combining semantic and keyword matching
- —Permission-aware retrieval, so people see only what they should
- —Evaluation harness measuring accuracy before launch
- —Internal deployment for staff, or external for customers
AI agent development and workflow automation
An agent retrieves context, decides what to do, acts, and escalates when it should not proceed alone.
- —Agent design: scope, permitted actions, boundaries and escalation
- —Multi-step execution across connected systems
- —Tool and API access with permission control
- —Human-in-the-loop approval at defined decision points
- —Logging and audit trail for every action taken
- —Monitoring, failure alerting and rollback
- —Integration with CRM, commerce, internal and custom systems
LLM integration and AI feature development
- —Model selection against requirement, cost and latency
- —API integration with fallback and rate handling
- —Prompt architecture, versioning and evaluation
- —Summarisation, extraction, classification and generation features
- —Cost monitoring and token budgeting
- —Guardrails, output validation and refusal handling
- —React and Node implementation, consistent with our custom software work
When We Advise Against Automation
When AI is the wrong answer
We build this and we are paid to build it. These are the conditions under which we will tell you not to.
Six of them cost us work. That is the point of publishing them.
When a rules-based system would do
When your data is not ready
When the cost of a wrong answer is high
When the process is not stable
When the volume does not justify it
When you need an enterprise AI research partner
AI development Timeline
AI questions, answered
Do we need new tools?
Usually not. We automate on the stack you already run, and only recommend a new tool when it clearly pays back.
How do you measure the result?
Time and cost recovered, against your own baseline. If we can’t measure it, we’re cautious about claiming it.
Is this just ChatGPT wrappers?
No. We use AI where it genuinely helps and deterministic rules where they’re safer, with guardrails and evaluation either way.
Do you only build AI into HubSpot, Shopify and WordPress?
No. We build standalone AI applications with their own databases and interfaces, and we build AI into existing systems. Both are ordinary engagements. The platform work is one route in, not the boundary of what we do.
What is a data processing tool, in practice?
A system that reads documents or records, extracts the information you need, checks it, and writes it somewhere useful. Invoices into an accounting system. Contracts into a database. Forms into a CRM. Low-confidence cases go to a person rather than through.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An agent carries out multi-step work across connected systems — retrieving context, deciding what to do, acting, and escalating when it should not proceed alone. Agents are more valuable and considerably more demanding to build safely.
What is RAG, and do we need it?
Retrieval-augmented generation grounds a model in your own documents rather than its training data, so answers are traceable to a source. You need it whenever accuracy matters and the subject matter is specific to your organisation — which is most business use cases.
How much of the project is data work?
Usually the majority. This is the part most proposals understate and the main reason AI projects disappoint. We audit data readiness during the assessment and price it visibly rather than discovering it mid-build.
Will it say something wrong?
It can. We test adversarially before launch, ground answers in your own material, set confidence thresholds, and escalate to a person outside defined boundaries. Where the cost of a wrong answer is high, we recommend against generated responses entirely.
Can it connect to our CRM, store or website?
Yes. We build and maintain HubSpot, Shopify, WordPress and custom applications, so the integration surface is familiar rather than exploratory — but a standalone system with its own database is equally normal.
What are the ongoing costs?
Inference charges scale with usage, and messaging channels charge per conversation. We model both at expected volume before you commit.
Which models do you build on?
We select against the requirement — accuracy, cost, latency, and whether data can leave your infrastructure. We are not tied to a provider, and we will tell you when an open-source model on your own infrastructure is the better answer.
Can you prove it worked?
Yes. We baseline before building and measure afterwards, reporting actual recovery against the original projection — including where it fell short.
Thirty minutes, and a document you keep.
One conversation, followed by a written recommendation: what we would build, in what order, and roughly what it costs. It is yours whether or not you engage us.