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AI projects don't fail because of the AI.
They fail because of the data underneath.
Point an AI tool at messy data — the same customer listed three ways, numbers nobody can trace, documents nobody can find — and it gives you confident, wrong answers. DataMug fixes the foundation first: clean records, numbers you can trust, and documents your tools can actually find and use. Then the AI you're paying for turns into something that works — and keeps working. And before you spend a dollar, we'll tell you straight whether you're ready.
Most AI projects don't fail because of the model. They fail because of the data underneath it.
You've seen the pattern, or you're living it. The AI tool isn't the problem. The tools themselves are getting cheaper and better every few months whether you do anything or not. What doesn't fix itself is the data those tools read, learn from, and write back to.
If that data is a mess, the AI project is a mess. No amount of clever prompting or guardrails fixes it.
The demo that looks great, then quietly falls apart
A pilot that demos beautifully, then quietly falls apart the moment it meets your real, everyday data.
An AI assistant that's confidently wrong
An AI assistant that gives confident, polished, completely wrong answers — because the documents it's pulling from are out of date, duplicated, or unlabeled.
Scores that look great in testing and flop in the real world
A score (like who's likely to leave or buy) that looks great in testing and underperforms once it's live — because the data it learned from didn't match the data it's now used on.
The tool that loses support after the first board demo
An AI rollout that loses leadership backing after the first board demo, because nobody wants to be the one who said 'let's let the AI loose on our master vendor list.'
The project stuck because nobody trusts the data
An AI project that gets stuck because nobody can answer a basic question: which version of the customer record is the one we trust?
A lot of the AI work we get asked to do shouldn't be AI work yet.
We turn down — or deliberately rework — a fair share of the AI projects that come to us. Not because the idea is wrong, but because the data foundation isn't there. An AI assistant that answers questions about your customers needs each customer to show up as one clean record. A pricing model needs a clean sales history with consistent units. A policy assistant needs documents where the current version is actually marked as the current version.
If those things don't exist, the right move is to build them first — and then put the AI on top. Not the other way around. We'd much rather have that conversation in week one of a readiness check than nine months into a stalled project.
What clean, well-organized data actually means for AI.
This is the part most "AI strategy" decks skip. The needs are concrete, and they go beyond what a reporting dashboard requires.
Each customer, vendor, and product shows up as one clean record.
One customer is one record. One vendor is one record. One product is one record. If your sales, billing, and support systems each hold a different version of the same customer, every AI feature you build on top inherits that confusion — and makes it worse. We do the matching and merging so the AI is working from records it can actually trust.
You can trace where every number came from.
Where did this number come from? Which system? What happened to it along the way? When was it last updated? If you can't answer that for a dashboard, you definitely can't answer it for an AI answer a customer is going to read. Once AI is involved, being able to trace a number is the difference between 'we trust the answer' and 'we hope the answer.'
Agreed, trusted definitions for your business numbers.
'Revenue,' 'active customer,' 'lost customer,' 'deal stage' — these each need one agreed definition that both the AI and your reports read from. Otherwise the AI tells the CFO one number, the dashboard tells the COO another, and trust disappears within the first few months. The tool matters less than the discipline of having one shared, agreed definition.
Documents your AI tools can actually find — not just store.
Pointing an AI at a messy shared drive isn't a real assistant. It's a search box with a chatbot taped on, and it will confidently make things up. A real one needs clean text pulled from your documents, sensible organization, version control, and clear labels (who owns it, when it took effect, which version is current), with old versions actually marked as old. The quality of the answers is only as good as the documents underneath.
Documents turned into data your systems can use.
A lot of what people call 'unstructured data' is really information nobody has bothered to pull out yet. Invoices, contracts, filings, support tickets, claim forms — the names, dates, amounts, and details are all in there. Pulling them out into a clean, searchable form (in Snowflake or Databricks) is what unlocks everything from search to scoring to AI that can take action.
Document search that's kept up to date.
The part that lets the AI search your documents by meaning isn't a one-time build. Documents change, products change, policies change. If it's six months out of date, your answers are six months out of date. We build the refresh schedule and a test set that checks the answers are still right — so you can tell when something has slipped.
Sensitive data protected — not as an afterthought.
The moment AI touches customer data, the questions change. Who is the AI allowed to show what to? What's logged? What can be deleted on request? What's hidden before it ever reaches the AI? Building this in from the start is far cheaper than bolting it on after your legal team raises a flag.
Three ways to work together — all built to actually finish.
Same way we run our data-move work. Clear scope, a project-specific quote, a real finish line, and a proper handover so your team owns it when we leave.
Free readiness check
A short, focused review that answers one question: can your data support the AI work you're considering, and if not, what's the fastest path to 'yes'? You get a project-by-project go / fix-this-first / wait recommendation, a clear list of what's missing, a step-by-step plan to fix it, and a short summary you can share with your board. If we tell you not to start the AI project yet, that's the answer — and you keep the report.
Take the readiness checkData Foundation for AI
We build the clean data foundation AI needs: each customer, vendor, or product as one clean record; the ability to trace where every number came from; agreed, trusted definitions for your business numbers; the plumbing that turns your documents into something an AI can search; and the controls that keep sensitive data protected. Often runs alongside (or right after) a move to Snowflake or Databricks. You end up with a foundation your team owns and can build on — not a black box.
Talk through Data Foundation for AIAI Implementation
For teams whose foundation is solid, we build the AI systems on top — an assistant that answers from your own documents, reliable everyday machine learning your team can run (on Databricks or Snowflake), customer-facing AI features, and AI that can take an action with a person approving anything that matters. Same way we work everything else: clear scope, a project-specific quote, and a proper handover.
Talk through AI ImplementationIt moves in stages, and every stage can be undone. AI doesn't change that.
Every stage has a clear deliverable, a clear finish line, and a way back if something goes wrong. Your old systems stay live until the new ones prove they work just as well.
Discovery
We talk with the person sponsoring the project, the team that will run the system day to day, and the people who'll use what it produces. We take stock of the relevant data, any AI work already underway, and the projects you have planned.
Readiness check
We score how ready your data is, project by project, so you can see which ones are good to go, which need foundation work first, and which we'd recommend holding off on. We estimate the cost of both the foundation work and running the AI (computing, document search, model fees, and monitoring), and lay out the risks.
Foundation
The data work: getting each customer, vendor, and product to one clean record; making numbers traceable; agreeing trusted definitions; turning documents into something the AI can search (if an assistant is in scope); and protecting sensitive data. Tests and monitoring from day one. Reversible by default — your old systems stay live until the new ones prove they work just as well.
AI implementation
Building the plumbing, choosing or tailoring the model, setting up document search, creating a test set that checks the AI is right, adding human review where it matters, launching, and monitoring. We don't do research experiments; we build systems you can run, with a way back if something goes wrong.
Handover & support
Written guides, a test set your team can run, a schedule for retraining and refreshing, a watch for the AI quietly getting less accurate, and cost dashboards. Training by working alongside your team, not a slide deck. Ongoing support is quoted based on your needs.
The AI builds we do most often — and where they actually pay off.
We're skeptical by default about AI projects. These are the ones where we've actually seen the numbers work at mid-sized companies — each one sitting on top of a healthy data foundation.
An AI assistant that answers from your own documents
A tool your team can ask questions in plain English — about policies, contracts, procedures, support docs — and get back accurate answers with a link to the exact source. It works when your documents are well-organized, version-controlled, and clearly labeled. It fails when it's pointed at a messy shared drive.
Read the deep diveTurning documents into usable data
Invoices, claims, statements, and contracts turned automatically into clean data your systems can use — pulled in, checked, and routed for approval or paid straight through. Every piece comes with a confidence score and a link back to the source, and anything uncertain goes to a person. Often handles 60–80% of invoices without a human touching them.
Read the deep diveOne clean customer view — plus scoring on top
Every customer as one trustworthy record, pulled together from your sales, billing, and support systems — so the scores you build on top (likely to leave, likely to buy, fraud risk, lifetime value) are actually reliable. These projects rarely fail at the model; they fail because three systems disagree about who the customer is. Getting to one clean record is the real work.
Read the deep diveReliable, everyday machine learning
The predictive models your business runs on — pricing, demand forecasting, recommendations, risk scoring — moved out of a one-off file on someone's laptop and into a real system your team can run, watch, and fix (on Databricks or Snowflake). Every prediction can be traced back to what produced it, and someone gets alerted when accuracy starts to slip — from a dashboard, not a customer complaint.
Read the deep diveAI that takes action — with a person in the loop
AI that can do real work — read across several systems, pull together a view, draft a response, and tee up the next step — while a person (or a clear rule) approves anything that matters. Our firm rule: the AI never changes an official record, sends an external message, or moves money on its own. The AI drafts; a human or a rule confirms.
Read the deep diveWe'd rather lose the project than ship an AI build that hurts your business.
Candid warning signs that you're not ready. If two or more are true, the right first move is fixing the foundation, not an AI build. We'll say so.
Your systems disagree about who your customers are
If three different systems hold three different versions of the same customer, fix that first.
You can't trace where your key numbers come from
'Where does this number come from?' should have an answer in under a minute. If it doesn't, that's the project before this one.
Nobody owns keeping it running after launch
AI has a long tail — checking it's still right, watching for it getting less accurate, retraining, and being on call. If nobody owns that, the project decays the day after launch.
There's no realistic cost estimate
Model fees, document search, monitoring, testing, and human review can easily land at 3–5× the first guess. If the business case can't survive that, the business case isn't real yet.
There's no clear way to measure whether it's working
'Improve the customer experience' isn't a measure. How many questions it handles on its own, how fast issues get resolved, how often it's wrong compared to today — those are.
The right tool depends on the kind of work you're doing.
Snowflake-native AI
For teams that have settled on (or are moving to) Snowflake, we build on its built-in AI tools: Cortex for AI features and reading documents, Snowpark ML for building and tracking models, Cortex Search for finding the right answer in your documents, and Cortex Analyst for asking questions in plain English. It keeps your data, your access controls, and the ability to trace where numbers came from inside the platform you already trust. Best fit when the work is close to your reporting and keeping data secure matters most.
Read the Snowflake Cortex deep diveDatabricks-native AI
For teams whose plans lean heavily on machine learning, live data feeds, or lots of documents and other unstructured data, Databricks is usually the right target. It brings together the tools to build, track, and run models reliably (MLflow and Model Serving), a shared, reusable library of the data your models rely on (its Feature Store), Mosaic AI for tailoring and running models, and a built-in way for AI to search your documents by meaning. Because it keeps storage, data prep, and serving in one place, there's far less shuffling data back and forth — which matters a lot at machine-learning scale.
Read the Databricks Mosaic AI deep diveA mix & vendor-neutral
Plenty of mid-sized companies use one platform for everyday reporting and another for heavier data-science work. We'll fit into what you have rather than force everything onto one tool. If you need an AI assistant that answers from your own documents on top of your current setup — using tools like Pinecone, pgvector, or OpenSearch for document search, and models from OpenAI, Anthropic, or AWS — we have proven patterns for that too.
Not sure which platform fits your AI plans? The free readiness check gives you a recommendation matched to your actual needs — not to whatever a vendor is selling. Take the readiness check or talk through your situation.
What's in the free readiness check.
It's 25 questions and takes about fifteen minutes. No login, no payment, and no sales call required to get the report. (One's there if you want it.)
- A readiness score across five areas: the business case and who's backing it, your data foundation, how well your AI ideas fit, risk and protecting sensitive data, and your team and how it'll run
- A project-by-project go / fix-this-first / wait recommendation for the AI ideas you have planned
- A platform recommendation (Snowflake, Databricks, or a mix) based on the kind of work you're doing
- An estimate of the foundation work — what would need to be built before your top-priority AI project can launch, and how long that typically takes
- The top three risks we'd flag for a team in your position
- A PDF report you can share internally with your CFO, CTO, or board
Every project is scoped and quoted.
Same as the rest of our work. No per-person license fees on anything we build. No locked-in tools you have to keep paying us for. You own the code, the AI instructions, the tests that check the AI is right, the written guides, and the way it all runs.
Readiness check
Clear deliverables. You get the full report whether you continue with us or not.
Data Foundation for AI
Depends on what's missing and which platform you're moving to. We provide a quote once the readiness check makes the scope clear.
AI Implementation
The quote reflects the use case, data foundation, integrations, controls, and rollout scope.
Support & after launch
An ongoing support plan is available — useful because AI quietly gets less accurate over time. Pair sessions, written guides, and a test set your team can run to check the AI is still right.
The questions we get most often.
Should we wait for the AI hype to settle before we invest?
We understand the instinct, but no. The AI tools themselves are getting cheaper and better on their own — that's actually a reason to spend now on the things that don't go out of date: clean records, numbers you can trace, agreed definitions, and well-organized documents. That work pays off no matter which AI tool you end up using — Claude, GPT, Gemini, an open one, or whatever comes next.
Do we need to move to Snowflake or Databricks before we can do AI?
Not always. We've built AI on top of older systems before — including cloud Postgres, BigQuery, and even SQL Server (with caveats). But most teams that hit a wall on AI are hitting the same walls — cost, keeping data governed, performance — that push them toward a modern platform anyway. The readiness check tells you whether the move is the thing that unlocks AI, or whether you can do it in steps.
Can we do this with our existing data team?
That's the goal of every project. We work alongside your team, write everything down, and build around the way you actually run — not the way a big-consultancy slide deck says you should. By handover, your team owns the system. We're available on an ongoing plan afterward if you want a safety net for the first few quarters.
What about open-source models, or running everything inside our own walls?
We work with all of it. For regulated industries (banking, credit unions, healthcare) we've built AI assistants and models that run entirely inside the customer's own private environment, with no data ever leaving. The right setup depends on how sensitive your data is and how fast answers need to come back — the readiness check covers it.
How do you handle sensitive data and rules like HIPAA and GDPR?
Same way we handle them for data moves: as a core part of the design, not a bolt-on. We hide sensitive personal data at the foundation, control who's allowed to see what, keep audit logs, handle deletion requests, and respect where data has to live. For regulated work we'll often run the AI inside your own private environment so sensitive data never leaves, and we build to your existing compliance program rather than asking you to adopt ours.
We already have a data team building AI features. Why bring you in?
Two common reasons. One: you want a second opinion on the foundation before you grow a pilot into something the whole company relies on. Two: you have a specific gap — one clean customer record, agreed definitions, document search, or turning a one-off model into a system your team can run — that nobody in-house has built before, and you'd rather not learn by failing in production. We slot in either way.
Do you build the AI model itself, or just the data underneath it?
Both, depending on the project. For most mid-sized companies, the right answer is a leading model from Claude, GPT, or Gemini paired with good document search and clear instructions — not a custom-trained model. Where tailoring a model genuinely helps (your industry's language, repetitive document reading, or cutting cost at scale), we do that too — usually on Databricks Mosaic AI or with open models run on Snowflake or Databricks.
What's the typical AI project budget for a mid-sized company?
As a rough range, the first-year cost (foundation work + first project + running it) usually lands between $250k and $750k all-in, depending on how much foundation work is needed and how complex the first project is. Once it's live, the monthly cost to run it (computing and model fees) usually sits between $5k and $25k for the kinds of projects we see most. The readiness check produces a specific estimate for your situation.
Are you a Snowflake / Databricks / OpenAI / Anthropic / AWS / Azure partner?
We're deliberately vendor-neutral. We work across both modern platforms (Snowflake and Databricks) and all of the major AI providers (OpenAI, Anthropic, Google, and open models on AWS or Azure). The right answer for you depends on where your data already lives, your compliance needs, and where your team already has skills — not on a partnership we'd be defending.
Ready to find out if your data is actually ready for AI?
Take the free 15-minute readiness check — no sales call required — or book 30 minutes to talk through your situation directly. Either way, you'll leave with a clearer picture of where you stand and what the next steps should look like.
We've also published The Data Foundation for AI Checklist — a 22-question plain-English guide for teams thinking about an AI build. Get the checklist