A demo that impressed, then stalled
Someone built a chatbot in an afternoon and everyone loved it. Then it confidently got a policy wrong, nobody could say how often that happens, and it never left the pilot.
Add AI
AI development and automation, built into the work you already do.
LLM-powered features, answers drawn from your own documents, workflow automation and the data pipelines underneath them — added to the tools your team uses today. Tested against your real examples before launch, with a person in the loop wherever a wrong answer would cost you.
Sound familiar?
The problems this solves.
Answers buried in documents and inboxes
Your team asks the same questions every week — what the contract says, which process applies, where last quarter’s figures are — and the answer sits in a PDF or in one person’s head.
Hours of reading, sorting and re-typing
Emails, forms and invoices arrive in a dozen formats. Someone reads each one, pulls out the details and types them into another system. It’s slow, and it’s where mistakes creep in.
Data nobody quite trusts
Reports are stitched together from exports by hand, so meetings start with an argument about whose number is right. Any AI built on top of that data inherits the same doubt.
What you get
Concrete things, not adjectives.
An honest use-case check
We look at the task, the data and the cost of a wrong answer, then tell you plainly where AI fits, where ordinary software would do better, and where neither is worth it yet.
LLM-powered features in your product
Drafting, summarising, classifying and extracting, built into the screens your team and customers already use — not a separate chat window nobody opens.
Answers grounded in your own knowledge
Retrieval over your documents, policies and records, so answers come from your sources and link back to them. Each answer shows its sources, so the person reading it can check them in one click.
Automation with a person in the loop
Routine steps run on their own. Anything uncertain, unusual or high-stakes goes to a person to approve first, and you decide where that line sits.
Evaluation and running costs you can see
A test set built from your real examples, rerun before launch and after every change to prompts, models or data. Usage and spend are tracked per feature, with limits, so a busy month shows up as an alert, not a surprise bill.
Data pipelines and dashboards
Pipelines that pull data from your systems, clean it and keep it current, feeding BI dashboards your team can rely on — and giving every AI feature something trustworthy to work from.
How an engagement runs
A demo every two weeks. No six-month silences.
Most projects start with a short, fixed-price discovery and move in two-week sprints you can stop, pause or extend.
From discovery to launch
Weeks 1–2
We learn the game before we play it
What happens
We sit with your users, your constraints and your numbers. Then architecture, scope, and the honest version of what fits your timeline — including the parts we would cut.
What you get
A scoped plan with a real budget, a technical architecture you own, and a working prototype of whichever part carries the most risk.
Fixed priceYours to keepNo obligation to continue
Two-week sprints
A demo at the end of every sprint
What happens
Design and engineering run together rather than in sequence. Every sprint closes with something you can click — never a status report standing in for progress.
What you get
A product growing in front of you, written status every week, and a direct line to the engineers writing the code.
Demo every two weeksYour repository from day oneRe-scope between sprints
Launch and after
We stay on past the launch party
What happens
A security review, a performance pass, and a handover your team can actually act on. Then we watch the first weeks of real traffic alongside you.
What you get
Production infrastructure in your accounts, documentation and training, and a support arrangement sized to your stage.
Security reviewed before launchFull handover and trainingSupport that answers
Expertise
The tools, and who it suits.
Technologies we use
- AI and automation
- Languages and frameworks
- Data and APIs
- Cloud and delivery
A good fit when
- People spend hours reading, sorting or re-typing information that follows a pattern most of the time.
- Your team keeps asking questions whose answers already exist somewhere in your documents.
- You can describe what a good answer looks like, and you have past cases we can check it against.
- You want AI inside your existing systems, not another tool to log in to.
Probably not the right fit when
- The task needs the exact same answer every time, like a price calculation or a fixed rule. That is a job for ordinary software, and we can build that instead.
- There’s no specific task in mind yet, just a feeling you should be “doing AI”. A short discovery can find a real use for it — or confirm you don’t need it yet.
Questions
AI Development: your questions.
Do we actually need AI for this?
Often not, and we will say so. If a task follows fixed rules, ordinary software does it more cheaply and more reliably. AI earns its place when the input is messy — free text, documents, emails — and an answer a person can quickly check is worth more than no answer at all. Discovery is where we work that out together.
How do you stop it from making things up?
You can’t remove the risk entirely, so we design around it. Answers are grounded in your own sources and show where they came from, the system is built to say it doesn’t know rather than guess, and anything high-stakes goes to a person for review. Before launch, we run it against the test set and agree with you what “good enough” means.
What happens to our data?
You decide where it goes. Production runs in your own cloud accounts, and a model only receives the data a task needs. If a feature uses a hosted model, that data is processed by the model provider, so we check their terms on data retention and training with you before any real data goes near it. Where data is sensitive, we can strip out personal details first or look at options that keep processing inside your own cloud.
What will it cost to run once it’s live?
That depends on how often it runs and how much text each request handles, so we estimate it during discovery from your real volumes. Once live, spend is tracked per feature with limits and alerts. Because the model sits behind a small layer of our own code, you can move to a cheaper or better one later without a rewrite — and rerunning the test set shows whether the new one is good enough before you switch.
Should we use RAG or fine-tune a model?
Most teams should start with retrieval-augmented generation, or RAG: when someone asks a question, your system finds the relevant passages in your documents and hands them to the model with it, so answers stay current and can show their sources. Fine-tuning is better at changing how a model behaves than at teaching it facts, and is worth it in narrower cases. Our article on RAG vs fine-tuning, linked below, walks through the choice in plain English.
How long before we see something working?
You see working software at the end of every two-week sprint, and a focused first release is usually six to twelve weeks. Because testing starts in the first sprints, you find out quickly whether AI is good enough for the task — before most of the budget is spent.
Further reading
From Step2Play Insights.
AI & automation
RAG vs Fine-Tuning: Choosing the Right Approach
Two ways to make a language model useful with your own knowledge. When each one fits, what each costs to run, and why we usually start with retrieval.
Product development
How to Turn a Business Idea into an MVP
A practical path from idea to a first version real users can try: what to build, what to leave out, and how to know it worked.
Often paired with
Related services.
Custom Software Development
Platforms built around how your business actually works — not around a template someone else outgrew.
Cloud & DevOps
CI/CD pipelines, infrastructure as code and observability. Ship on a Friday without flinching.
QA & Testing
Automated testing, penetration-test remediation and managed support once you are live.
Get in touch
Tell us what you’re building.
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