Data Preparation for AI
The specimen outlives the study
A herbarium sheet is useful two centuries later because someone cleaned the specimen, wrote the label, and filed it where it could be found. Data for AI is the same job. Point a model at raw data and it'll be confidently wrong. Prepare it properly and that corpus outlives whatever model you train on it.
Specimen sheet
catalogued
Prepared in 4 stages
The label is the whole value. An unlabeled specimen is a dried plant; a labeled one is evidence a researcher who isn't born yet can still use.
The brief
Garbage in, garbage out. Here's what the garbage actually looks like
As collected
Double space in the name. An ambiguous date: is that March or April? A currency welded to the amount. An email address nobody cleared for training. And this record exists twice.
Prepared
Normalized, dated unambiguously, currency split out, PII redacted, duplicate merged, and labeled, so it can be learned from and measured against. Multiply by a few million rows and that's the project.
What changes
The model stops being confidently wrong
AI faithfully learns or retrieves whatever you hand it. Fix the duplicates, gaps, and inconsistent formats first and the thing built on top stops inventing answers from noise.
Retrieval finds your real information
Chunked, embedded, and indexed properly, your own documents become something the assistant can actually search, so it answers from your content instead of guessing plausibly.
Evaluation becomes possible at all
Clean, labeled examples are what let you measure whether the thing works. Without them you have opinions about the output and no way to settle them.
The dataset outlives the project
A well-prepared corpus isn't a one-off cost. It powers this model and the next three, which is why the preparation is the part that compounds and the model is the part that gets replaced.
What we do
Everything between as-collected and cataloged
Drawer 01
Cleaning & validation
Fix errors, gaps, and inconsistencies so the model learns from good data. The muddy specimen gets washed before anything else happens.
Drawer 02
Deduplication
Find and merge duplicates so the same thing isn't counted, or learned, five times over and quietly weighted five times as heavily.
Drawer 03
Structuring & shaping
Turn messy, mixed data into the clean, consistent form models and pipelines expect, so every record means the same thing.
Drawer 04
Labelling & annotation
Create the labeled examples supervised models and evaluations need: the determination on the sheet, without which a specimen is just a dried plant.
Drawer 05
Embeddings & vectorisation
Prepare and index your content for retrieval so AI answers from your real data. This is the catalog that makes the collection findable.
Drawer 06
PII handling
Detect, redact, or protect sensitive data so preparation is safe and compliant, and the corpus doesn't become a privacy problem.
The engagement
From an honest look to a corpus that keeps
Week 1
Look at it honestly
What's actually there, what's usable, and what's missing. This is the assessment that decides whether the project is a data problem or a model problem, and usually it's the former.
Weeks 2–3
Clean and structure
Fix, dedupe, and shape the data into a consistent, reliable form. Unglamorous, and most of the reason the project will work.
Weeks 3–4
Label and enrich
Label, annotate, or embed as the use case genuinely needs, with PII detected and handled safely as we go, never audited afterward.
Handover
Deliver and keep it fresh
A clean, documented, AI-ready dataset, plus the pipeline that keeps it prepared as new data arrives so it doesn't quietly degrade back.
How we work
Four rules for preparing a corpus
Assess before promising
We look at the data honestly first and tell you what's usable. If the corpus can't support the use case, that's worth knowing in week one. Month six is an expensive time to find out.
Label only what earns it
Supervised models and proper evaluation need labeled examples; retrieval and many modern techniques need far less. We label what your case requires and stop there.
Handle PII on the way in
Detection and redaction happen as part of preparation, designed around your obligations, because a corpus assembled first and reviewed later is already a problem.
Build for the second model
The dataset outlives whatever you're training or retrieving with today. We prepare and document it so the next project inherits an asset and skips starting again.
Why the boring part decides it
Six things nobody puts in the launch post
Most of an AI project is data preparation
The modeling gets the attention; the data work decides the result. We'd rather say that up front than watch a model underperform because nobody cleaned what it was fed.
A model is only as good as the data behind it
There is no prompt, no fine-tune, and no larger model that repairs a corpus full of duplicates and contradictions. The ceiling is set underneath, before anything is trained.
Clean, labeled data makes evaluation possible
Labels are what turn "it seems better" into a number you can act on. Without them you can't tell improvement from a good day, which means you can't ship responsibly.
Well-prepared data cuts cost and improves accuracy
Less noise means fewer tokens, fewer retries, and a smaller model doing the job a larger one was brought in to brute-force. Preparation is usually the cheapest accuracy you can buy.
Good retrieval data is what makes RAG trustworthy
Chunking, embedding, and indexing decide whether the assistant finds the right passage or a confidently adjacent one. RAG failures are almost always preparation failures.
The dataset outlives any single model
Models are replaced yearly; a well-prepared, documented corpus keeps paying out across all of them. It's the only part of an AI project that reliably appreciates.
Why it pays
Projects stop failing quietly
The single biggest reason AI work disappoints is the data underneath it. Fixing that first is what turns "we have data" into "we have data an AI can use."
You can prove it works
Labeled examples make evaluation real, so shipping is a decision backed by a measurement instead of a demo that felt convincing.
The privacy question is answered
PII detected and handled during preparation means the corpus is safe to use, with no compliance conversation waiting to happen.
The next project starts ahead
A clean, documented dataset and the pipeline that maintains it mean model number two begins where model number one finished.
Selected work
Two corpora that made the model work
What you get
A corpus, cataloged and kept
Most AI projects that stall, stall here, and the first item is why: an honest readiness assessment often concludes that the data isn't ready, which is a cheaper answer to reach now than after a model has been trained on it.
- Slip 01An honest assessment of your data's readiness
- Slip 02Cleaned, deduplicated, validated data
- Slip 03Structured and labeled datasets for training or RAG
- Slip 04Embeddings and indexes for retrieval, if needed
- Slip 05PII detection and redaction where required
- Slip 06A pipeline to keep the data prepared over time
Industry expertise
Where the corpus is the hard part
Professional services — Document corpora (contracts, matters, advice) prepared so retrieval answers from the firm's real precedent. No plausible inventions.
Healthcare & clinical — Records where PII handling is mandatory and a mislabeled example carries a cost beyond accuracy.
E-commerce & retail — Catalog, review, and transaction data cleaned and structured so forecasting and recommendation learn from signal instead of duplicates.
Financial services — Transaction and document data prepared under obligations, where the audit of what was redacted matters as much as the model.
Logistics & distribution — Operational history shaped into training data, where inconsistent formats across depots are the whole problem.
Media & publishing — Archives chunked and embedded so decades of content become searchable by meaning instead of by keyword luck.
Accession — pending
Send us the data you think is ready
We'll look at it honestly and tell you what's there, what's usable, and what's missing, including if the corpus can't carry the use case yet. Better to know in week one.
Why us for this
We tell you if the data can't support it
The assessment is honest, including when the answer is no. A project stopped in week one costs a fraction of a model that underperforms for a year.
We do the boring part properly
Cleaning, deduping, and labeling isn't glamorous, and it's most of what makes the project succeed. We spend the effort where it decides the outcome.
We prepare for the next model too
The corpus is documented and pipelined so it stays prepared. Models get replaced; we build the part that doesn't have to be.
Working with Flaidex
Honest about the effort split
Data prep is usually the majority of an AI project. We say so before you sign, so nobody discovers it together in month three.
PII designed in from the start
Detection and redaction are part of the process and shaped around your obligations, so preparation never creates the problem it was meant to avoid.
No labelling for its own sake
If your use case doesn't need labeled examples, we won't sell you a labeling program. What the case actually requires is the whole scope.
Questions
What people ask before the data work
- ACC-01Why can't we just point AI at our data as-is?
- Because real-world data is messy (duplicates, gaps, inconsistent formats, and errors) and AI faithfully learns or retrieves whatever you give it. Point a model at raw data and it'll be confidently wrong. Preparation is the unglamorous work that turns "we have data" into "we have data an AI can actually use," and it's usually the difference between a project that works and one that quietly doesn't.
- ACC-02How much of an AI project is data prep?
- More than people expect, often the majority of the effort. The modeling gets the attention, but the data work is what determines whether it succeeds. We'd rather be honest about that up front than have a model underperform because the data underneath it was never cleaned.
- ACC-03What is this "embedding" and do we need it?
- If you want AI to answer from your own documents (retrieval-augmented generation), your content needs to be chunked, embedded into vectors, and indexed so the AI can find the right passages. We handle that preparation so the assistant retrieves your real information instead of guessing.
- ACC-04Is our sensitive data safe during this?
- Yes. We detect and handle PII carefully, redacting or protecting it as your compliance requires, so preparing data for AI doesn't create a privacy problem. For regulated data we design the process around your obligations.
- ACC-05Do we need labeled data?
- It depends on the approach. Supervised models and proper evaluation need labeled examples; retrieval and many modern techniques need less. We'll tell you what your specific use case actually requires and label only that.
- ACC-06Is the prepared data reusable?
- That's a big part of the value. A clean, well-structured dataset isn't a one-off: it powers this project and the next, and we build a pipeline so it stays prepared as new data arrives instead of degrading over time.
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