Your AI is only as useful as the data it can reach.
I connect your apps, pull the data into one clean place in your own cloud, and make it usable by your AI, your reports, and your whole team. Most builds: 4–6 weeks, from $15K.
- 3 priority systems connected & flowing by week 2
- Up to ~10 sources, 5 reports/dashboards
- 1 production-grade workflow (AI assistant, ops automation, or leadership report)
- Source code, schemas & docs in a Git repo you control
- Runs in your own AWS account: you own storage, credentials, access
AI is the car. Your data is the roads.
A brand-new car is useless if it can only drive on a handful of pre-paved highways. That's what off-the-shelf AI gives you: twenty supported connectors, maybe thirty, and nothing for the system of record that runs your business.
A pipeline is the road network: from your apps, databases, spreadsheets, and internal tools into one place you own. Now the car can go anywhere, and you're not at the mercy of whether ChatGPT or Claude decides to support your stack next quarter.
A few pre-paved highways.
The whole road network. Yours.
One week, two ways.
Same business, same five moments. The only difference is whether the data underneath is connected.
Your ops lead exports four CSVs, reconciles them in a sheet, sends it at 11am. It disagrees with the one finance sent earlier.
Auto-generated Monday report hits your inbox at 7am. Same numbers every team is looking at. No one prepped it.
Slack thread. Four people loop in. The analyst is on PTO. You get an answer Thursday, caveated.
You type the question. You get an answer in under a minute, with the sources shown.
Great at writing emails, useless for running the business. Your team stopped opening them weeks ago.
It knows your orders, ad spend, subs, and support, and pulls real numbers with citations.
Run on screenshots and luck. Break silently. Someone discovers it a month later.
Run on the same clean data your reports use. You get alerted the minute something goes sideways.
"Here are the 12 tools. Good luck figuring out which numbers to trust." Takes a month to be useful.
One clean view. One set of numbers. Contributing in week one.
One fixed package, clearly defined.
3 priority systems connected and flowing by week 2. 1 production workflow shipped in 4–6 weeks.
- Up to ~10 sources connected and pulling on a schedule
- 5 reports / dashboards built on the clean data
- 1 production workflow: an AI assistant, an ops automation, or a leadership report
- Source code, schemas & docs committed to a Git repo you control
- Runs in your own AWS account (your storage, your credentials, your access policies)
Alerting on every connection, change-tracked in Git, retries when an upstream API misbehaves. It's the boring infrastructure that keeps things running when nobody's watching.
- ~2–4 hrs/week from you during the build
- One kickoff call to align on priorities
- Credentials, handed over source-by-source (least privilege)
- Optional: your SSO, if you want it wired in
A priority map
You approve it before I build anything, so scope doesn't creep.
Agreed definitions
What counts as a "new customer" or "active subscription". Locked once, so every team and the AI use the same number.
Alerts when something breaks
You know before your team does, instead of discovering a silent failure later.
Plain-English docs
Written for the people who'll run it, and short enough that they will.
Four to six weeks, real progress every week.
Map
Discovery, then a priority map you approve before a single line of code. We agree on what matters and in what order.
Connect
Your first 3 sources are live by end of week 2, then the rest follow. Custom Python where no connector exists.
Organize
Locked definitions and clean, usable tables. No more arguing which number is right.
Activate
I ship the workflow, hand the code to your repo, and 30 days of support are included.
Fair warning: week 1 feels slow. It's also what decides whether week 6 ships something useful.
If your business runs on it, I can pull from it.
CRM
Salesforce · HubSpot · Pipedrive
Billing & finance
Stripe · Chargebee · QuickBooks · NetSuite
Ads
Meta · Google · TikTok
Support
Zendesk · Gorgias · Intercom
Project & ops
Asana · Jira · Monday · Notion
Product database
Postgres · MySQL · that ERP from 2003
Commerce
Shopify · Amazon
Spreadsheets & files
Google Sheets · Excel · CSV / SFTP drops
+ everything else
If it has an API, it's on the table.
Off-the-shelf connectors cover the easy 60%. Custom connectors cover the 40% that decides whether the AI can answer at all.
If it has an API, a database, a webhook, or even an SFTP drop, I connect it. A brand-new custom connector takes about a week.
shipped
The AI tools you already use. Now grounded on your real data.
I don't build you another chat UI. You keep using Claude, ChatGPT, Copilot (whatever you already pay for), and the foundation connects them to your real data, so every answer is cited from a source table instead of guessed from last quarter's PDFs.
No more "based on the documents you uploaded."
What was blended CAC last week vs the week before?
Blended CAC was $42.10 last week[1], down from $48.30[2], driven by lower Meta spend[3] with steady new customers.
Illustrative: your real tables, your numbers.
Fixed-fee, set at kickoff.
fixed at kickoff before any code is written.
Less than three months of the data hire you'd otherwise need. A junior analyst runs $120–150K/yr; a data engineer $180–220K. The foundation does that job and runs for years.
- $7,500 deposit reserves the kickoff
- Balance due at handoff
- 30 days of support included
- Optional ~$2,500/mo retainer (month-to-month, cancel anytime)
- Run-rate: a few hundred dollars/month in AWS for most teams
What it looked like in practice.
How a fast-growing DTC brand stopped reconciling spreadsheets.
Their store, email & SMS, ad platforms, payments, ERP, help-desk, and 3PL, plus a long tail of operational tools, pulled into one clean foundation in their own cloud. It now feeds the daily ops report and an AI assistant grounded on real orders, spend, inventory, and support. It cut the team's morning ops review from 90 minutes to 10.
Before the build, three different tools reported three different customer-acquisition costs, and the Monday meeting was spent arguing about which one was right. Now there's one number, and the meeting is about what to do next.
[ specifics under NDA ] · anonymized + representative
An honest read on fit.
- Your business runs on a growing pile of SaaS apps.
- You've got real volume: orders, customers, revenue moving every day.
- You want AI that knows your business, not the internet.
- You're tired of copy-paste and arguing whose number is right.
- You want a practical start, not a 9-month rebuild.
- You just want a chatbot glued to your FAQ.
- Your key tools have no exports or APIs at all.
- You're pre-revenue and unsure what data you'll even need.
- You want everything modeled perfectly before anyone sees a result.
The engineering under the hood.
Here's what makes this a foundation instead of a pile of scripts. It's the part I'm probably most opinionated about.
A governed medallion lake, in your cloud
Every source lands in one place you own and flows through clean layers (raw → curated → aggregated → analytics), catalogued and queryable in seconds, with a lakehouse table format for the high-churn data and a sub-second hot-cache for live screens.
- Three ingestion patterns: real-time webhooks (signature-verified), scheduled pulls, on-demand batch.
- State-machine orchestration with retries, dead-letter queues, and freshness gates.
- Typical build spans 20+ sources, ~100 catalog tables, dozens of pipelines.
Standards enforced by the build itself
The reason it stays trustworthy as it grows: one shared internal SDK every service uses, and a CI 'drift checker' that fails the build the moment someone reaches for a raw cloud client, a hard-coded webhook, or a direct write.
- OIDC CI/CD: no stored keys; infrastructure-as-code; secrets in a vault.
- One isolated cloud account per brand; least-privilege by default.
- 30+ alarms, structured alerting, self-healing deploys.
Cost and reliability, engineered on purpose
Most data plumbing runs up a cloud bill over time. I treat cost as a feature: compacting storage cut objects scanned by ~490×, rewriting heavy queries took several from multi-gigabyte scans to a few kilobytes, and one brand's monthly cloud spend dropped from ~$3.6k to ~$1.3–2k with zero data loss.
- AI done with discipline: a cheap model triages, a stronger model does the hard part (one split cut a document-AI bill ~86%).
- Strict-JSON outputs with deterministic fallbacks. Nothing critical depends on a model 'feeling helpful'.
- Semantic search across the whole corpus in under half a second.
[ anonymized, specifics under NDA ]
Common questions.
Where does our data live?
In your own AWS account, in a region you choose. Never a vendor cloud you can't see into. You own the storage, the credentials, and every access policy.
Who owns the code & infra?
You do. Every line, in a Git repo you control. There's no secret sauce and nothing you have to keep paying me to keep running.
What if you get hit by a bus?
A fair question for a one-person shop. I build on standard, widely-supported tooling rather than a proprietary platform, you own all the source, and plain-English docs are a deliverable, so any competent engineer could pick it up.
What about security?
Encryption in transit and at rest, IAM-scoped credentials per source (least privilege), and no third-party SaaS sitting in your data path. I'll wire it into your SSO and VPC if you have them.
Compliance: HIPAA / SOC 2 / GDPR?
It runs in your AWS, so your existing posture extends to it. I architect to your control set and produce documentation your auditors can read, and I've shipped under HIPAA-aligned and SOC 2 environments before.
Our key tool isn't supported?
If it has an API, a database, a webhook, or an SFTP drop, I connect it. A custom connector takes about one to two weeks and is baked into the build rather than billed as an extra.
Can we cancel partway?
Yes. You keep everything shipped so far (it lives in your own cloud and your repo from day one), and we settle up for the time spent. That's the whole arrangement.
Isn't this just another chatbot?
No. I don't build a chat UI. I ground the AI tools you already use on your real data, so answers are cited from your tables instead of generated from thin air.
Why not just use Fivetran / an off-the-shelf tool?
Those are great for the easy 60%: the standard SaaS connectors. The value here is the 40% they don't cover: your system of record, the legacy ERP, the internal tool nobody else will integrate.
Let's find where to start.
A 30-minute scoping call, and you'll leave with a clear plan whether or not we end up working together.