AI that actually saves you time — not just another tool you have to learn.
You don't need another ChatGPT subscription. You need AI that handles the specific stuff eating your week — drafting quotes, answering the same customer question for the 40th time today, summarizing a client's history before you pick up the phone. Built into the tools you already use, grounded in your data, priced so the math actually works. From $500.
Is AI actually worth it for your business?
Real talk, up front.
AI probably earns its keep if…
- You're doing the same task over and over — drafting quotes, replying to the same question, sorting invoices — and it's eating hours every week.
- You have a pile of data (orders, tickets, emails, PDFs, call notes) that nobody has time to read, but there are answers buried in it.
- Your team fields the same 10 questions from customers every day, and your website could answer them if it knew how.
- You want to take on more work without hiring another person to handle the admin that comes with it.
- You've played with ChatGPT yourself and thought "there's got to be a way to wire this into my actual business, not a separate browser tab."
Probably hold off if…
- The real bottleneck is that you don't have the data yet. AI without your real information behind it tends to make things up — so the better first step is getting your data into one place. Happy to help with that piece if it'd be useful, then we revisit AI later.
- You want "an AI" but can't yet point to a specific task it would handle. Totally common — AI is everywhere right now. The fix is easy: think of one specific, repetitive thing on your plate (drafting quotes, replying to the same question, sorting paperwork) and bring that to the call. We'll build one good answer to one real problem.
- Your team already has the process solved in a spreadsheet and it genuinely works. Save your money — if that's what we find on the call, that's what we'll tell you.
- You're considering AI mostly because it's trendy right now. That's totally understandable, but worth a beat: customers usually care whether you pick up the phone, not what's running behind the scenes. If we can find a specific real problem AI would solve, great. If not, that's a fine answer too.
If you're not sure which side you land on, that's exactly what the free call is for.
What every AI feature we build gets by default.
Not upsells. How we keep AI honest.
- Grounded in your actual data
- Cost tracked from day one
- Answers in under 3 seconds
- You can see what it's doing
- Backup plan when it breaks
- Human approval for the risky stuff
- You own the prompts and data
How we actually build AI features.
Picked per task. Embedded where the work already happens. Measured before it earns a promotion.
Right tool for the task
Claude for the thinking-heavy stuff, OpenAI for fast classification and extraction, smaller cheaper models for the high-volume grunt work. Mixed and matched, not married to one vendor — so when a better model drops next month, you upgrade with a config change, not a rewrite.
It reads your stuff before it answers
The AI pulls from your docs, PDFs, orders, past customer emails — whatever's relevant — and cites its sources. That's most of the difference between "useful" and "embarrassing." No more made-up prices, made-up policies, or made-up last names.
Tested before it ships
Before anything goes live, we run it against 30–100 real examples from your actual business and score it. Accuracy, cost-per-answer, how often it refuses. If the numbers don't clear the bar, we tune it or call it — you don't pay for production code that fails the test.
Embedded where you already work
AI shows up inside your website, your app, your email drafts, your Power Automate flow — not as another tab to remember. The best AI feature is the one nobody has to log into.
We ship AI every day — ours and clients'.
We write production code with Claude daily, run multi-step AI workflows inside our own Rec Soccer app, and replaced about $40K/year of vendor reporting at a previous role with AI-assisted automation. Attuned Ventures isn't selling something we read about — we're selling what already runs on our own machines.
The AI models powering your build.
We're not married to one vendor. We pick the model that wins on your task — accuracy, cost, and speed — and swap it out when a better one drops. Usage is billed to your account, not ours, so you never pay a token markup.
Claude
AnthropicMy default for reasoning, long-context work, careful writing, and tool use. Claude Sonnet for most tasks, Opus for the hard ones.
GPT
OpenAICheap, fast classification and structured extraction. My go-to when you need "read this email and pull out the invoice number" at scale.
Gemini
GoogleHuge context windows and strong multimodal — reading full PDFs, long meeting transcripts, or whole codebases in one shot. Plays nicely with Google Workspace data.
Llama
MetaOpen-weight models for when you need to run it yourself — on your own servers, in regulated industries, or to keep data off a third-party API entirely.
ElevenLabs
VoiceThe best voice we've heard — cloned voices, multilingual, conversational agents. Where AI needs to sound like a person, this is it.
Sora + Nano Banana
OpenAI · GoogleVideo from Sora, images from Nano Banana (Google's Gemini 2.5 Flash Image). For marketing assets, demo reels, ad creative, product mockups, and social posts — when you don't have a camera crew or a designer in the budget.
sora.com deepmind.googlePlus specialty services when they earn it — Whisper and Deepgram for speech-to-text, Azure OpenAI or Amazon Bedrock for regulated or enterprise contracts, Hugging Face for the long tail of open models. Whatever wins on your task.
Three ways to start. Pick the shape that fits.
Every package is grounded in your data, evaluated before launch, and yours to own. The ranges are typical — we send a fixed, one-page quote after a discovery call.
Spark
- Timeline
- 1–2 weeks
- Scope
- Single feature
- Languages
- English
- Discovery on the actual task — what's in, what's good, what's broken
- Model + prompt picked and tuned against 30+ real examples
- Structured output (JSON schema) so downstream tools can use it
- Wired into your existing inbox, CRM, sheet, or workflow
Stack
- Timeline
- 3–5 weeks
- Scope
- Multi-step workflow
- Languages
- English + 1
- Everything in Spark
- RAG pipeline over your docs, PDFs, sheets, Notion, or Slack export
- Vector store + chunking strategy tuned for your content
- Source citations on every answer — no "trust me" responses
Suite
- Timeline
- 6–12 weeks
- Scope
- Multi-feature, app-embedded
- Languages
- 2–12 languages
- Everything in Stack
- Tool use — AI calling your APIs, CRM, calendar, ERP, or custom endpoints
- Multi-step agent loops with retries, fallbacks, and human approval gates
- Multimodal where it earns its keep — voice (STT/TTS), images, PDFs
Extras, when they earn it.
Slot these onto any package, or add them later as the use-case grows.
RAG corpus / vector store
$800–$2.5KIngest, chunk, embed, and index your docs — plus a re-ingest job for when content changes. Firestore vector, Pinecone, or pgvector.
Voice (STT + TTS)
$1K–$3KWhisper or Deepgram for speech-to-text, ElevenLabs or OpenAI voices for replies. Phone, browser, or in-app.
Vision / image understanding
$800–$2KRead receipts, IDs, forms, screenshots, product photos. Extract structured data or answer questions about what's in the picture.
Eval harness
$600–$1.5KA test set, scoring rubric, and a one-command runner so you can see the impact of every prompt or model change before shipping it.
Prompt-versioning UI
$1K–$2.5KA small admin panel where you can edit prompts, A/B test variants, and roll back — without redeploying the app.
Monthly AI retainer
$300–$1K / moPrompt tuning, cost monitoring, model upgrades when new ones drop, and "why did it do that" investigations — on call.
How an AI project actually goes.
No magic-wand demos. A call, a scoped pilot, real measurement, then production — or an honest stop.
20-minute discovery call
We ask what task you're trying to fix, who does it today, what "good" looks like, and where the data lives. If AI isn't the right tool, we'll say so — sometimes a Power Automate flow or a SQL view is the answer.
Scoped pilot
Within 48 hours you get a fixed quote — one feature, one model, one success metric. We build against 30–100 real examples from your data, not made-up ones.
Measure with evals
Before anything goes live we score it. Accuracy, cost-per-call, p95 latency, refusal rate. If the numbers don't clear the bar we tune, swap models, or call it — you don't pay for production code that fails the test.
Ship and iterate
Live in your app, monitored in real time, with logs you can see. Most clients keep us on a small retainer to tune prompts and ride model upgrades; some don't. Both are fine.
Things people usually ask.
Fair worry — and the honest answer is, sometimes it will. Less often than the wild AI demos you've probably seen, but never zero. The way we build keeps that risk small: every AI feature we ship is grounded in your real information (your documents, your records, your numbers) so it's answering from facts instead of guessing. For anything high-stakes — quotes that go to customers, contracts, money — we add a step where a person reviews before anything goes out. The skill is catching mistakes before your customer ever sees them.
Under the hood: this is RAG (retrieval-augmented generation) plus structured outputs and an evaluation suite that runs against real examples before each release.
You do, but it's billed directly to your OpenAI or Anthropic account — not ours. We don't resell AI usage or take a markup on what the AI costs to run. You see exactly what every interaction costs in your own dashboard, and if we ever part ways, the account (and any unused credit) stays with you.
Why it works this way: some agencies bundle AI usage into a monthly fee with a hidden markup. We think that's bad for trust, and it makes it harder for you to see what's actually being spent. Direct billing keeps the math honest.
For a small AI feature (something like a chatbot answering common questions), usually $5–$50 per month in AI usage. For a bigger AI assistant that's reading through your documents to answer questions, typically $30–$300 per month depending on how much your team or customers are using it. There's also a behind-the-scenes trick called prompt caching that can drop the cost of repeat questions by 50–80%, and we turn that on by default. You get a usage dashboard from day one so there are no surprises at the end of the month.
Big concern, totally understandable. Short version: your business's data stays yours. The two main AI providers we use (OpenAI and Anthropic) both have contracts that say they don't use your data to train their models when accessed through their business APIs (which is how we build). Your data lives in your accounts, not a third-party AI service. And nothing gets logged anywhere outside your own setup unless you specifically ask for it.
For regulated industries (healthcare, finance, legal): we can also use Azure OpenAI or Amazon Bedrock, which add extra contractual privacy coverage that satisfies HIPAA and similar requirements.
All of the major ones — the answer really is "whichever is best for the specific job." The four big AI providers each have strengths: Claude (from Anthropic) is our default for careful writing, reasoning, and using business tools. GPT (from OpenAI) is great for fast, cheap classification work at high volume. Gemini (from Google) wins when there's a huge amount of context to process — whole PDFs, long meeting transcripts — or when your business already lives in Google Workspace. Llama (from Meta) is the right pick when you need to run the AI on your own servers, especially in regulated industries where data can't leave your premises. The good news for you: we write everything so the AI provider can be swapped with a single configuration change. When the next better model comes out, you upgrade without a rewrite.
For the technical folks: for voice we use ElevenLabs; for image generation, Google's Gemini 2.5 Flash Image ("Nano Banana") and OpenAI's Sora for video. All routed through the same abstraction layer.
Tell us the task you're tired of doing. We'll tell you if AI is the right fix.
First calls run about 20 minutes — just a real conversation about the task that's eating your time. You'll walk away with a clear next step: a scoped pilot, a recommendation, or sometimes an honest "this is a spreadsheet problem, not an AI problem." Whichever one fits, that's what you'll hear.