AI Solutions & Intelligent Automation

Applied AI works best on a specific business task with clear inputs, a measurable result and a person who can review what matters. We build assistants grounded in your own documents, document classification and extraction, request triage and workflow copilots, integrated with your systems, permissions and audit trails. Where fixed rules do the job more cheaply and reliably, we recommend workflow automation instead.

Who it helps: Operations, support and product teams that want practical AI built into the systems they already use.

Discuss an AI use case

Scope to agree

Pricing: quoted after requirements review.

Schedule: depends on scope, integrations and review availability.

Deliverables, ownership and support are confirmed in the proposal and agreement.

Use-case and data-readiness assessment
Prototype evaluated against your own test cases
Integration with permissions, review steps and audit logs
Monitoring of quality, usage and running costs

What applied AI can do in your operations

Each solution below is a capability we can scope and build, described with the guardrails it needs. The right choice depends on your data, volumes and how mistakes are caught.

AI solution types we can scope and build (capabilities, not past deliveries)
Solution	What it does	Good fit when	Guardrails
Knowledge assistant (retrieval-augmented generation)	Answers staff or customer questions from your approved documents and shows the sources it used	Policies, manuals and past tickets are spread across files and inboxes	Document-level permissions, cited sources, a clear "not found" answer, scheduled content refresh
Document processing	Classifies documents and extracts fields from invoices, forms, delivery notes or applications into your systems	Many semi-structured documents are re-typed by hand	Confidence thresholds, human review of uncertain items, validation rules, audit log
Request triage and reply drafts	Reads incoming emails or tickets, assigns a category and owner, and drafts a reply for a person to send	Inbound requests are numerous and repetitive	A person approves replies, escalation rules, personal data handling
Workflow copilot with approvals	Prepares the next step in a process, such as filling a form or proposing an update, for someone to approve	A multi-step process needs judgement at a few points	Approval gates, narrowly scoped permissions, full action history, manual override
AI features in your SaaS product	Adds summaries, smart search, classification or an assistant inside your application	A product team wants AI that customers can trust	Evaluation set, cost per request, tenant data isolation, an off switch
Model and API integration	Connects model APIs such as Anthropic Claude, OpenAI or Google Gemini to your systems	The use case is clear and needs a reliable integration	Provider fallback, key management, logging without exposing personal data

How AI projects work

Discovery: agree the task, the people involved and what a good result looks like.
Data readiness: check where the information lives, its quality, access rights and what may be sent to a model provider.
Prototype and evaluation: test against an agreed set of real, redacted examples and record the results, including failures.
System integration: connect to your applications with permissions, logging and error handling.
Human approvals and guardrails: decide which outputs need review and what happens when confidence is low.
Monitoring and iteration: track quality, usage and running costs, and improve the prompts, retrieval or rules over time.

Data, security and cost considerations

Permissions come first: an assistant should only retrieve what the person asking is allowed to see. Personal and confidential data needs a decision about what is sent to a model provider, what is stored, for how long, and where logs are kept.

Language models can produce confident but wrong answers. Grounding answers in your documents, citing sources, measuring retrieval quality and routing uncertain cases to a person reduce that risk; they do not remove it. Consequential actions, such as payments, customer commitments or record changes, keep a human approval step and an audit trail.

Running costs depend on volumes, document sizes and the model chosen, so they are estimated during the prototype and monitored afterwards. Every AI step needs a fallback: a manual path or a rules-based default when the model is unavailable or unsure.

AI is not always the answer

If the inputs are structured and the rules can be written down, conventional workflow automation is usually cheaper, faster and more predictable. Approvals, notifications, data synchronization and scheduled reports rarely need a model. We recommend that route when it fits, and add AI only to the steps that involve unstructured text, documents or judgement.

Capabilities and published work

The solution types on this page describe what we can design and build and how we approach it. They are not descriptions of past client deployments. When project evidence is published, it is labelled with its status (prototype, pilot or live) and shared only with permission.

Scope, cost drivers and exclusions

Estimates depend on the number and quality of data sources, the integrations involved, the review effort needed to reach an acceptable error rate, expected volumes and any security or data-residency requirements. A bounded pilot on one use case is the usual first step.

Training foundation models, guarantees of accuracy, regulatory certification and model-provider usage fees are excluded unless explicitly agreed. Confirm data handling, ownership of prompts, configuration and evaluation data, and the provider terms in the contract.

Questions and practical answers

Do we need AI, or would workflow automation do?: If the inputs are structured and the rules can be written down, automation is usually cheaper and more predictable. AI helps with unstructured text, documents and judgement calls that a person can review.
Which AI models do you use?: We integrate commercial model APIs such as Anthropic Claude, OpenAI and Google Gemini, or open-weight models where data handling or cost requires it. The choice follows the task, data requirements and running cost. We do not train foundation models.
Is our data used to train AI models?: It depends on the provider and plan. Business API terms of major providers generally do not use submitted data for training by default, but terms differ and change, so we confirm them for the provider you use and document what data is sent, stored and logged.
How do you reduce wrong answers?: By grounding answers in approved sources with citations, testing against an agreed evaluation set, using confidence thresholds, answering "not found" when sources do not cover a question, and keeping human review for consequential outputs. No AI system is error-free.
What does an AI pilot involve?: One use case, a sample of real but redacted data, an evaluation set agreed in advance, a prototype measured against it, and a go or no-go decision with expected running costs before wider integration.
Who owns the prompts, data and code?: Your data remains yours. Ownership of code, prompts, configuration and evaluation data is set out in the agreement, and the model provider terms govern the API usage itself.

References

These sources support the general methods and technical considerations discussed above. They do not establish company delivery history or endorse this service.

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Share the workflow, constraints and decisions you need help with. The next step is a project discussion, not a confirmed booking or delivery commitment.

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