AI implementation
AI implementationYou've heard about AI.We put it to work in your business.
Every company knows AI belongs somewhere. Almost none know where to start. We find the repetitive work AI can take off your team's plate, test it on your real work, and put it into use.
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Sound familiar?
Everyone says AI. Nobody says where.
Every vendor, podcast, and LinkedIn post says AI belongs in your business. None of them can tell you which task to hand it first.
We bought the tools. Nobody uses them.
The licenses are paid for and a few people tried them for a week. The real work still runs the way it always has.
The team is drowning in copy-paste.
Intake forms, follow-ups, weekly reports, moving data from one system to another. Hours every week on steps that follow the same pattern every time.
We don't know what's safe to put in.
Client data, contracts, financials. Nobody wants to be the person who pasted the wrong thing into the wrong tool, so nobody tries.
Our view
Our viewThe hype is loud and the use cases are abstract. Your inbox is full of demos that don't quite fit your business, and every vendor says theirs is the answer. Meanwhile the manual work piles up: spreadsheets, copy-paste, follow-ups, weekly reports. Most teams don't need an AI strategy. They need one task done well, then the next one.
What we do instead
We sit with your team, watch the work, and pick the task AI should take on first. No 47-item roadmap, no vendor demo theater. We build it on your real data, run it next to the manual process until it earns trust, and only then roll it out. Replace the work, not the team.
Where we've done it
The assistant on this website is our own working example: it helps visitors find relevant projects, understand how we work, and put together a project brief, within a defined set of information. We also use AI agents inside our own studio's daily work, so we know where they help and where a person still needs to check.
The proof
You're already looking at it.
The assistant on this site is AI we built and run ourselves. It answers from our own case studies, approach, and pages, points you to relevant work, and helps you shape a brief for the team. Ask it something about your own business.
“Splash delivers powerful brand ID, slick websites and UX, beautiful graphic design, and intelligent software solutions. Great team, great experience, great creative partners.”
The manual work AI can take off your team's plate
- 01
Where to start
We watch how the work happens and tell you which task AI should do first, and which ones it shouldn't touch. You get a short, ranked list, not a transformation deck.
- 02
Repetitive workflows
Intake forms, data entry, follow-ups, weekly reports, routing, triage. If it happens every week the same way, it's a candidate to run with far less hands-on time.
- 03
Assistants and agents
Internal helpers and customer-facing assistants for the questions your team answers a hundred times a week. Built on Claude, GPT, Gemini, or whatever fits the job.
- 04
Your documents, answerable
Contracts, SOPs, tickets, and internal wikis turned into something your team can ask questions of, with answers that point back to the source.
- 05
Connected to the tools you use
Slack, HubSpot, Gmail, Notion, Google Drive, or anything with an API. The work happens where your team already works, not in another tab nobody opens.
- 06
Training your team
We don't disappear after launch. We teach your team to run, check, and extend what we built, so it doesn't stall the day we step back.
How we work, for free
Five rules for picking the first task to hand to AI.
Most AI projects stall because they start with the tool instead of the task. These are the checks we run before building anything. Run them on your own team's week.
- 1
The weekly test
List every task your team does the same way every week. If it doesn't repeat at least weekly, and follow roughly the same steps each time, set it aside. AI earns its keep on volume and pattern, not on the one-off.
- 2
Write down what good looks like
Before anyone builds, collect three examples of a great result and three that would be unacceptable. That's your spec and your first test. If you can't write them, the task isn't defined well enough yet.
- 3
Let it draft before it sends
Start where AI prepares and a person approves: drafts, summaries, first-pass sorting. Name who checks the output and how long it takes them. If checking takes as long as doing, it isn't ready.
- 4
Run it next to the old way
Put the AI version and the manual process side by side on the same real inputs for a couple of weeks. Trust comes from seeing it hold up on your own work, not from a demo on someone else's.
- 5
Check the boring answer first
Before any model, ask whether a better form, a template, a simple automation, or a tool you already pay for would fix it. Sometimes the right AI project is no AI at all, and we'll say so.
How an AI project with us works
- 01
We watch the work
We sit with your team and see how the work really gets done, usually over about a week. You get a one-page map of the tasks AI could help with, ranked by time it could save.
- 02
We pick the first task
We choose one task, agree how we'll judge success, and put the scope and fee in writing. You know exactly what you're getting before we build.
- 03
We build it on your real work
We build against your actual data and tools and run it alongside the manual process. No isolated demo, no made-up data.
- 04
We roll it out
When it's earned trust, we roll it out to the team, hand over plain-language instructions, and stay close through the first weeks of real use.
- 05
We come back for the next one
Some clients keep going with us, adding a new task each round and fixing anything that drifts. Others take it from there. Your call.
Why not off-the-shelf AI tools, a big consultancy, or an in-house hire?
Each is right for some teams. The difference is where the work starts and who can run it afterward.
Splash Creative
- Where it starts
- Watching how one real task gets done
- What you get first
- One task, tested on your real data next to the manual process
- Tool choice
- Tool-agnostic: Claude, GPT, Gemini, or an existing product if that fits better
- Size of the commitment
- One task first, then you decide on the next
- Who runs it afterward
- Your team, trained, with plain-language instructions
Off-the-shelf AI tools
- Where it starts
- The features the product offers
- What you get first
- A login, a setup guide, and a blank prompt box
- Tool choice
- One vendor's models and features
- Size of the commitment
- Ongoing seat licenses
- Who runs it afterward
- You, with vendor support
Large consultancy
- Where it starts
- An assessment of the wider organization
- What you get first
- Often a strategy and roadmap phase before anything is built
- Tool choice
- Varies by firm, sometimes tied to technology partners
- Size of the commitment
- Usually a larger, longer engagement
- Who runs it afterward
- Often a separate implementation or support contract
Hiring in-house
- Where it starts
- Wherever the new hire decides to begin
- What you get first
- Progress once the right person is found and onboarded
- Tool choice
- Shaped by what that person knows
- Size of the commitment
- A full-time role
- Who runs it afterward
- The hire, for as long as they stay
We're a good fit if...
- You keep hearing about AI and want a straight answer on what to actually do with it.
- Your team spends hours every week on the same repetitive steps: intake, reporting, follow-ups, data entry.
- You've thought about hiring more people when what you really need is fewer manual steps.
- You're a small or mid-size business watching bigger competitors move faster with AI.
- You want one working result you can judge, not a 12-month transformation plan.
- You'd rather own the system than rent a black box from a vendor.
Not a fit
If you want a vendor demo, a strategy deck, or a SaaS license, we're not the right fit. And if a task is better handled by an existing product or a simpler process, we'll tell you that instead of building something.
In short
Splash Creative helps small and mid-size businesses put AI to work on specific, repetitive tasks. We start by watching how the work actually happens, pick one task worth automating, build and test it on your real data alongside the existing process, and roll it out with training for your team. We're tool-agnostic and will recommend an off-the-shelf product, or no AI at all, when that's the better answer.
- Where it starts
- A repeated task, bottleneck, or information problem
- What it covers
- Agents, knowledge systems, and workflow automation, from discovery and pilot to implementation and handoff
- Who does the work
- A senior team led by David Herskowitz, Founder and Creative Director
- How it is priced
- Scoped around a specific use case; data access, integrations, evaluation, and ongoing operation affect the investment
Questions, answered.
How is this different from hiring a consultant or a freelancer?
A consultant usually leaves you with a deck, and a freelancer often leaves you with a prototype. We aim for AI actually running inside your business, checked against a measure we agree on up front, with your team trained to run it.
We don't even know what to automate. Is that okay?
That's where most clients start. You don't need a hypothesis, just a sense of where the manual work is piling up. Finding the right first task is our job.
Is our data safe?
We review what data is involved, the vendor terms, and your requirements before recommending any approach, and we'll sign an NDA before you share anything sensitive. Where data can't leave your environment, that becomes a constraint we design around, not an afterthought.
How soon will we see a return?
It depends on the task, how much your team uses the result, and the running costs. We agree on what to measure before we build and report against it. We don't promise a standard payback period.
Do we need engineers on staff?
No. We build with your team in mind: clear instructions, low-code where it makes sense, and training so a non-technical operator can run what we built.
How is AI implementation priced?
The fee depends on the task, the data and tools involved, and whether it's a first project or ongoing work. Scope, timing, and fee are agreed in a written proposal before kickoff. Tell us your budget and we'll tell you what's realistic.
Bring us one task.
Tell us the job your team repeats every week and roughly how long it takes. We'll tell you honestly whether AI is the right fix, what a first pilot would look like, and send a written proposal if we're the right fit.
- 01
You tell us what needs to change
An unfinished brief is welcome. The business situation matters more than a list of deliverables.
- 02
We talk it through
A first conversation about the business, what's already in place, and what should happen next.
- 03
You get a written proposal
Scope, responsibilities, timing, and fee, agreed before any work begins.
Rather talk first?
Book a call with DavidTell us about the project