AI & Automation

AI Integration & Automation

AI applied where it is measurably better than the alternative.

AI is worth using where the alternative is worse: reading unstructured documents, classifying free text, surfacing the right record from a large corpus, drafting something a person will review. It is a poor choice where the answer must be exactly right every time and a rule would do the job deterministically.

We build features on top of language models and, where the problem suits it, conventional machine learning: document extraction, search and retrieval over your own content, classification and routing, summarisation, and assistants scoped to a defined task rather than to open-ended conversation.

The engineering that makes these usable is mostly not the model. It is evaluation, so you can tell whether a change made things better; guardrails, so failures are contained; and a review path, so a person stays responsible for consequential decisions.

At a glance

Typical stack: Laravel Node.js TypeScript PostgreSQL Redis
Engagement: Fixed scope, dedicated team, or staff augmentation

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The problem

What this solves

The common failure is deploying something impressive in a demo and unmeasured in production. Without an evaluation set there is no way to know whether last week's prompt change improved accuracy or quietly degraded it, and no way to compare two approaches other than by impression.

The second failure is scope. A general-purpose assistant bolted onto a product tends to be confidently wrong in public. A narrow feature with a clear input, a clear output and a defined failure behaviour is both more useful and far easier to stand behind.

Impressive demo, unmeasured in production

With no evaluation set, nobody can say whether a change helped or hurt.
Confidently wrong output

An unconstrained assistant produces plausible answers with no signal about reliability.
Unclear data boundaries

Nobody has written down what leaves the building, which becomes a problem the moment somebody asks.
Unpredictable cost

Per-token pricing without caching or limits turns a feature into an open-ended bill.

Our approach

How we go about it

We start by writing down what success means in terms someone non-technical can check, and by building a small evaluation set from real examples. Everything after that is measured against it, so decisions are comparative rather than anecdotal.

We prefer the simplest mechanism that clears the bar. Retrieval over your own documents before fine-tuning; a deterministic rule before a model; a model with a constrained output format before free text. Each step up adds capability and adds failure modes, so it should be justified.

Cost, latency and data handling are designed in. That means knowing what is sent to a third-party provider and what is not, caching where responses repeat, and deciding in advance what happens when the provider is slow or unavailable — because at some point it will be.

Capabilities

What is included

Document processing and structured extraction
Retrieval-augmented search over internal content
Support and internal-knowledge assistants
Classification and routing automation
Evaluation harnesses and accuracy measurement
Workflow automation between existing systems

Outcomes

What changes for you

Measured against real examples

An evaluation set built from your data, so every change can be compared rather than argued about.
Scoped to a defined task

Clear input, clear output, and a defined behaviour when confidence is low.
A person stays accountable

Consequential decisions get a review step; the model drafts, a human decides.
Known data handling

Written down: what is sent to a provider, what is retained, and what never leaves your infrastructure.

Related work

Where we have done this

All case studies

Frequently asked questions

Common questions about ai integration & automation.

Do we need to train our own model?

Rarely. Most business problems are better solved by retrieval over your own content combined with a general model, which is faster to build, cheaper to run and much easier to change. Training or fine-tuning earns its place when you need a specific behaviour or format that prompting cannot reach reliably, and it should follow evidence that the simpler approach fell short.

What happens to our data?

That is a design decision made before anything is built. Depending on your requirements, data can stay inside your infrastructure, go to a provider under an agreement that excludes training use, or be redacted before it leaves. Whichever applies gets written down, because at some point a customer or an auditor will ask.

How do you stop it giving wrong answers?

You cannot eliminate it, so the design has to contain it. Ground answers in retrieved source documents and cite them, constrain output to a validated format, detect low confidence and hand off rather than guess, and keep a person in the loop wherever the consequence of being wrong is real. Honest framing in the interface matters too — presenting a draft as a draft.

Is this worth it for our size of business?

It depends entirely on whether there is a repetitive, text-heavy task consuming real hours. Reading invoices, triaging inbound email, or searching years of documents are worth automating at almost any size. If there is no such task, AI will not create value just by being present, and we will tell you that.

Related services

Have a repetitive, text-heavy task?

Describe it and we will tell you honestly whether AI is the right tool — or whether a rule would do it better.

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