Stone Age SoftwareHouston, TX
Data Entry & Analysis · Houston, TX

Get the data in. Get an answer out.

Somebody in your office retypes the same numbers into the same workbook every week, and the report everyone argues about in the Monday meeting comes out of it. Both halves are fixable: we automate the entry that shouldn't be manual, clean up what's already there, and turn the pile into numbers your team stops disputing.

Get a data assessment What we do
Sound familiar?

Six symptoms of a data problem

Every one of these has a fixed-scope first project hiding inside it.

The business runs on one workbook

Forty megabytes, a dozen tabs, formulas nobody dares touch, and exactly one person who understands it. That person takes holidays.

Same question, three answers

Sales, operations, and accounting each produce a different number for last month — and every meeting starts by arguing about which one is real.

Data entry is somebody's whole week

Numbers retyped from one system into another, or from a PDF into both. Every retype is a chance to be wrong and a cost you're already paying.

The data is dirty and everyone knows it

Duplicates, 'TX' and 'Texas' and 'tx', dates stored as text, and trailing spaces that quietly break every lookup.

Reports take three days to produce

Export, paste, pivot, format, email. Every month, by hand, by the person you hired to analyze the business rather than assemble it.

Years of data, no answers

The transactions are all there. Nobody has ever asked them which customers are profitable, which routes lose money, or what next quarter looks like.

Capabilities

Entry, cleanup, reporting, analysis

Usually in that order — an answer built on data nobody checked is just a confident guess.

Data Entry & Digitization

Backlogs and recurring entry handled — automated first, and made fast where a person is still required.

  • Paper, scans, PDFs, and emailed forms turned into structured records
  • Extraction that reads the document, with validation before anything is saved
  • Keyboard-first entry screens for the fields a human genuinely has to judge
  • Confidence thresholds and review queues so the doubtful cases reach a person
  • Fixed, per-batch scope so a backlog has a price and an end date

Case study request workbooks read in whatever shape each team sends

Cleanup, De-duplication & Standardization

The unglamorous pass that makes every later number believable.

  • Duplicate detection and merging with a record of what was merged into what
  • Addresses, names, units, and codes standardized to one agreed format
  • Dates stored as dates, numbers as numbers, and text that stops carrying spaces
  • Profiling first: a written report on what's wrong and how much of it there is
  • Validation rules at the point of entry, so the mess doesn't grow back

Spreadsheets Into Real Systems

When the workbook everyone depends on has outgrown being a workbook.

  • Excel or Access promoted to a proper SQL database — PostgreSQL or SQL Server — with a real interface over it
  • Multi-user access without the file-locking and 'final_v3_USE_THIS' problem
  • Validation, permissions, and an audit trail of who changed which value
  • History preserved — the old workbook stays readable, not abandoned
  • Excel kept where it's good: analysis and export, rather than system of record

Case studies a warehouse's spreadsheets replaced by one Flask and SQL Server app · one ledger row for every receipt, issue and count

Reporting & Dashboards

The monthly export-paste-pivot ritual, done by software instead of a Tuesday.

  • Recurring reports automated end to end and delivered on a schedule
  • One written definition per metric, so two departments stop disagreeing
  • Power BI, SQL, or plain scheduled files — whatever your team can actually run
  • Reconciliation checks that flag when a number stops matching its source
  • Documentation of where every figure comes from, in language a non-analyst reads

Analysis That Answers a Question

A specific question, a defensible answer, and the method shown rather than hidden.

  • Cost-to-serve, margin by customer or SKU, and which accounts actually pay
  • Python and pandas for most of it, Julia where the numerical work earns it
  • On-time performance, throughput, error rates, and where the time goes
  • Demand patterns and seasonality for staffing and inventory decisions
  • Written findings with the method, the sample, and the caveats stated plainly
  • An honest 'the data can't answer that yet' when that's the true result

We'd rather delete the job than bill you for it.

If the documents arrive as files, or the data already exists in another system you own, then paying anyone — us or an offshore service — to retype it by hand is a rate you pay forever instead of a pipeline you pay for once. So the first question on every entry project is how much of it can stop being manual. What's genuinely left for a person, we make as fast as possible: the right screen, the right keyboard order, and validation that catches the typo at entry rather than in next quarter's report.

How a first project runs

  1. 1

    Profile

    We look at what you actually have: record counts, duplicate rates, which fields are usable, and what proportion is beyond saving. You get that in writing.

  2. 2

    Agree the definitions

    What counts as an active customer, a completed order, a late delivery. Most disagreements about numbers turn out to be disagreements about words.

  3. 3

    Build & reconcile

    The cleanup, pipeline, or report gets built — then checked against the source with counts, totals, and spot checks you can verify yourself. Where two systems keep disagreeing, it becomes a variance register only the system can close.

  4. 4

    Hand over

    Documented, scheduled, and runnable without us, with validation rules at the point of entry so the mess doesn't quietly grow back.

Automate before you outsource

A pipeline you buy once beats a per-record rate you pay forever — whenever the source is a file rather than paper.

One definition per metric

Written down, agreed, and used by every report. That single step ends most of the arguments about whose number is right.

You keep the pipeline

Readable code, documentation, and scheduled jobs in your accounts, runnable by anyone on your team.

Sensitive data stays put

If it can't leave your building, the processing — including AI-assisted extraction — runs on your hardware instead.

Where it pairs well

  • AI document extraction for the invoices, BOLs, and POs behind the entry backlog
  • Power BI once the numbers are worth putting on a screen
  • Migrations — a system move is the cheapest moment to clean the data
  • A Lean Six Sigma baseline, which needs measured data before it means anything
  • Logistics reporting: cost per order, cost to serve, and on-time performance
  • The recurring version of that for 3PLs is the 3PL reporting layer — extraction on a schedule, a database you own, reports that send themselves
  • The application that replaces the workbook for good

Excel isn't the enemy

Your team keeps its spreadsheets. Excel is an excellent analysis tool and a poor database, and nearly all the pain we get called about comes from asking it to be the second one — shared by nine people, edited by all of them, with no rule stopping anyone typing "Texsa" into a column that feeds the board pack.

So the shared, must-be-right data moves somewhere that can enforce rules, and your team keeps Excel for what it's genuinely good at: modelling, one-off questions, and the export nobody should have to ask permission for.

Toolkit

What we work in

Excel & Power Query Power BI SQL Server PostgreSQL SQLite MySQL Microsoft Access Python & pandas Julia CSV & fixed-width files OCR & document parsing Google Sheets & Apps Script Airtable ETL & scheduled jobs Data validation rules Reconciliation checks
Questions we get

Straight answers

Why not just hire an offshore data entry service?

For a one-off backlog of genuinely unstructured paper, that can be the right call, and we'll say so. The difference is that we try to delete the job first: if the documents arrive as files, or the same data already exists in another system, the honest answer is a pipeline you pay for once rather than a rate you pay forever.

Our data is a mess. Is it too far gone?

Almost never. We start with a profiling pass — how many records, how many duplicates, which fields are unusable, what proportion is recoverable — and you get that as a written report before committing to a cleanup. Sometimes it shows that only the last two years are worth saving, which is a cheaper project than you feared.

Do we have to give up Excel?

No. Excel is an excellent analysis tool and a poor database, and most of our work here is just moving it to the first job. Your team keeps their spreadsheets for modelling and one-off questions; the shared, multi-user, must-be-right data moves somewhere that can enforce rules.

How do we know the numbers are right?

Reconciliation, not reassurance. Migrated and cleaned data is checked with record counts, control totals, and spot checks against the source, and reporting pipelines carry tests that fail loudly when a figure stops tying out. You sign off on numbers you can verify.

How is our data handled while you work on it?

We take the least data that does the job, work under an NDA when you want one, keep access scoped to the people doing the work, and delete or return the working copies at the end. If it can't leave your building at all, the processing runs on your hardware — including the AI-assisted extraction, which we can host entirely on-premise.

What does a first project look like?

One dataset or one report, fixed price, usually a couple of weeks. That's enough to prove the numbers, hand you something you use immediately, and let you judge whether the bigger cleanup is worth doing — without signing up for it first.

Get in touch

Send us the spreadsheet everyone depends on.

We'll tell you what shape it's really in, what a cleanup would take, and which question it can honestly answer today.

Prefer to talk?

Call 832-598-8234 or email msco@stoneagesoftware.com. Houston, Texas — serving Houston, The Woodlands, Conroe, Sugar Land, Katy, Pearland, and the Greater Houston metro.

What happens next

We read your message, an engineer replies with questions or a straight answer, and if a call would help, we book one. From the first reply on, you're talking to the people who'd do the work.

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