AI integration
AI is everywhere.
Just not in how we work.
For teams that want AI in their operations but don't know how to build it in. Rather than stopping at a general-purpose chatbot, ALBY Studio works with you from workflow analysis through designing, building, and improving AI that fits your operations.
Problem
There's a structural reason
behind that frustration.
The world won't stop talking about AI, and yet when you look at your own company, it isn't clear which process it belongs in or how it would fit. Buying ChatGPT licences and stopping there changes nothing. What you need is AI built into the way your organisation actually works.
- 01We rolled out ChatGPT, and only a handful of people use it
- 02We want AI that knows our processes and our data
- 03We don't know which process to start with to see real results
ALBY Studio works with you from workflow analysisthrough to AI that's built in.
Scope
What this page
covers.
Where we're strongest is handing scattered internal information and everyday judgement calls to AI. Not "AI in general" — the areas below, built all the way to something that runs inside your existing workflow.
Search and answers over internal knowledge
Search across policies, manuals, past projects, and meeting notes, and answer while citing the documents the answer rests on.
First-line response to enquiries
AI handles the first reply to internal help-desk or customer enquiries, and passes on only the ones that need human judgement.
Summarising, classifying, and tagging
Summarise and categorise meeting notes, daily reports, applications, and enquiry histories, cutting down how much anyone has to read.
AI inside your existing systems
Build AI into the screens and flows of your core systems, SaaS products, and internal tools, so nobody has to open a separate tool.
Work handled by AI agents
Hand tasks that span several steps — searching, drafting, filing — to AI, with the procedure and permissions defined up front.
Reading and transferring documents
Read invoices and application forms with AI, reconcile them against existing data, and route only the exceptions to a person.
With ALBY Studio
With ALBY Studio,
you get AI built
around your operations.
Not help rolling out a generic tool, but a monthly-retainer engineering team alongside you. We analyse the workflow to find where AI actually pays off, design AI around your operations and your data, and finish it into something people use.
01
It starts with workflow analysis
The first thing we do isn't development — it's breaking the workflow apart. We map where the time goes, and separate the steps where AI helps from the steps that should stay human.
02
Not a generic chatbot — AI that fits the work
We build in your data and your operating rules to make AI a real tool for the people doing the work, sitting naturally inside the screens and flows they already use.
03
We watch how it's used, and improve it
On a monthly retainer, rollout isn't the end. We check accuracy and real-world usage, tune prompts and features, and stay until it's genuinely part of the work.
Flexible team
You can't apply AI
to work you don't understand.
From workflow analysis through day-to-day operations, a product manager and an AI engineer form the core team.
Specialists join only while development is at its heaviest, and we keep improving until it's part of how people work.
It starts with workflow analysis, and the team stays on after rollout to keep improving it.
Workflow analysis
Break the workflow apart and find where AI pays off
Product manager
AI engineer
AI development
Design AI around the work and the data, and build it in
- Product manager
- AI engineer
Full-stack engineer
UI/UX designer
QA engineer
Rollout
Pilot
Start with one part of the organisation and watch how it's used
- Product manager
- AI engineer
- Full-stack engineer
- QA engineer
Full rollout
Extend across the organisation, improving accuracy and usability
- Product manager
- AI engineer
Note: this is one example. Photos are illustrative. We adjust the team to the project, your budget, and your priorities.
Process
How it goes,
from first call to rollout.
The standard path from the first conversation, through a proof of concept that shows whether it works, to production. We don't build big up front: we narrow to one process, try it, and expand from where the results are.
- STEP 0160 min
First call and current-state discussion
We ask about the work that's causing you trouble and how it runs today. Nothing needs to be documented or specified yet. We'll also tell you honestly whether AI is the right tool, or whether something else fits better.
- STEP 02~2 weeks
Workflow analysis and choosing the target
We break the workflow into steps and identify where time is lost and where AI helps. We agree which process to start with, and what success means for it.
- STEP 03~4 weeks
Proof of concept — test accuracy and impact
We build something working for that one process and check accuracy and usability against real data. If it doesn't reach the accuracy we expected, we revisit the scope or the approach.
- STEP 04~2–3 months
Production build and rollout
We build it into your existing systems and workflows, and put permissions, logging, and operating procedures in place. We start with one team, then widen based on how it's used.
- STEP 05Ongoing
Operation and improvement
As a monthly-retainer team, we keep tuning against accuracy, real usage, and changes on the business side. Decisions to extend to more processes are made on the evidence.
Note: these durations are a guide for a typical engagement and shift with the breadth of the process and the state of your data. Cost is quoted individually once scope is confirmed, with the proof of concept and the production build priced separately.
Comparison
How is this different
from buying a tool?
Aspect
Rolling out a generic AI tool
ALBY Studio
- Where it starts
- Generic toolWith a tool subscription
- ALBYWith workflow analysis
- Shape of the AI
- Generic toolA generic chatbot, used as-is
- ALBYBuilt around your operations and data
- Adoption
- Generic toolSome people use it, some don't
- ALBYBuilt into the workflow, so everyone can
- After rollout
- Generic toolGetting value from it is left to each team
- ALBYThe team watches usage and improves it
The value of AI is decided by how deeply it's built into the work.
Quality & Security
How accuracy and security
are handled.
These come up in every AI project. We settle the approach at the start and implement the configuration we agreed on.
- Whether your input trains the model
- For each AI service we use, we confirm the settings and contract terms that keep your input out of training, and agree which service is used for which process before building.
- What the AI can see, and under whose permissions
- We define per process what data the AI is given and how far it reaches. Answers draw only on information the user is already permitted to see, following your existing permission model.
- How accuracy is measured
- We build an evaluation set from real operational data and tune against a measured accuracy figure. Improvement is driven by numbers, not impressions.
- What happens when it's wrong
- We design the handoff to a person from the start, for cases where the AI can't answer or its confidence is low. The workflow assumes the AI is never left entirely on its own.
- Traceability and logging
- Inputs and outputs are logged, so you can trace which documents and data an answer was based on. When something is wrong, you can follow it back to the cause.
Contact
Turn the AI everyone talks about
into something that works for you.
Tell us where you'd like AI, or where the inefficiency is right now. Starting from workflow analysis, we'll work out how to build it in so it pays off.