Bring every source in clean — reconciled to the penny, traced to the line.
“Can we run the business on these numbers — and prove where each one came from?”
The general ledger, cloud cost and usage files, AI bills and activity, and the documents that explain them. Type 1 analysis applies the rules you trust. Type 2 analysis puts AI on only what the rules can’t resolve — and a person approves every proposal. Each line is cleaned, cataloged into your operating model, reconciled to the source total and traceable back to the row it came from.
General ledger · October 2026
gl_2026_10.csv- Landed and verifiedchecksums match
- Type 1 · rules97.4% of lines resolved
- Type 2 · AI proposals3 waiting for review
- Reconciled to sourceties out
Proposed rule · Vendor from description
Lines described “AMZN WEB SVCS*” → Amazon Web Services
Matches 312 lines · 0 conflicts · 5 samples
Stored
$48,191,034.12
Refused
$14,220.00
Excluded
$8,653.00
Source total
$48,213,907.12
Illustrative view · sample data
The pressure
The numbers arrive. Trusting them takes the rest of the month.
Every ledger, cloud bill and AI invoice arrives in its own shape. Teams map columns by hand, patch the same exceptions every period and rebuild the mapping when a source changes. By the time the numbers reach a decision, nobody can say which line they came from — so finance, engineering and the AI all argue from different totals.
What it looks like today
- Mapping spreadsheets that only one analyst understands
- The same exceptions fixed by hand, every single month
- Loads that don’t tie back to the ledger — and nobody knows why
- AI answers built on numbers no one can trace
What changes
What changes.
Every source mapped by hand, every period
Rules resolve what’s known; AI proposes only for what’s new
Fixes that live in someone’s spreadsheet
Approved patterns become versioned rules anyone can read
Totals that drift from the source
Every load reconciled to the source total before it moves on
A number with no way back to where it came from
Every value traced to its source row and the decision that set it
Cost data in one tool, context in another
The ledger, cloud, AI spend and documents in one operating model
What it brings in
The money, the usage and the context — in one model.
Cost data usually lives in one tool, cloud and AI spend in another and the documents that explain them nowhere at all. The Ingestion Engine brings all of it onto the same operating model, so every number sits beside the reason it exists.
Every posting, line by line
General ledger
Load the ledger the way your finance system exports it. Columns map to a canonical ledger — account, cost center, vendor, PO, amount, currency, posting date, entity and description — plus the fields only your company uses. Accounts, cost centers and vendors resolve to the records already in your model.
- Your chart of accounts, not a template
- Accruals paired with their reversals
- Ties out to the ledger total every period
Lands as · Cost transactions tied to accounts, cost centers, vendors and the plan
The cloud bill, at the grain it was billed
Cloud cost and usage
Cost and Usage Report files read from where you already deliver them, checked against their delivery manifest and kept period by period. When the provider revises a bill, the period is restated — not double-counted — and it settles when the bill is final.
- Read from your own landing location
- Revised bills restated by key
- Line-level detail kept for every period
Lands as · Cloud cost and usage tied to accounts, services, teams and products
What every model costs — and who used it
AI bills and activity
Model and token bills, plus the usage activity behind them, from the AI vendors and platforms you run. AI spend lands in the same model as the cloud and the ledger, against the teams, products and workloads that drove it.
- Token and model spend across vendors
- Usage tied to teams and workloads
- Feeds tokenomics and AI value
Lands as · AI token usage and spend tied to workloads, teams and outcomes
The why behind the numbers
Context documentation
Operating documents, contracts, policies and plans — cataloged into the model as context, linked to the strategy, spend, systems and controls they describe, so Dot and every AI read them alongside the data.
- Cataloged against the records they describe
- Linked back to the document they came from
- Readable by Dot and any MCP client
Lands as · Context attached to strategy, spend, applications and controls
…plus plans and actuals from spreadsheets, and records from the tools your teams work in through Integration Hub.
How it works
Eight stages from file to model. None of them skipped.
Every delivery takes the same path. Rules go first, AI only fills the gaps, a person decides and the load has to tie out before anything downstream sees it.
- 01
Land
Files arrive where you already send them. Every delivery is logged on arrival.
- 02
Verify
Each file is checked against its manifest — declared lengths and checksums — before a line is read. A short or altered delivery stops.
- 03
Identify and map
The source is recognized and its columns map to the canonical schema. AI suggests the mapping for a new source; a person confirms it once.
- 04
Type 1 analysis
Fast and deterministic. Versioned rules, exact and alias matches and decisions already made resolve most of every delivery.
- 05
Type 2 analysis
Deliberate AI on what’s left. It proposes a pattern or a one-off fix, and the rules engine measures it across the whole data set.
- 06
Review
A person approves, edits or rejects each proposal in Dotwork, with the evidence beside it.
- 07
Reconcile
Stored, refused and excluded must add up to the source total — for every source, period and currency — or nothing moves on.
- 08
Catalog
Clean, line-level records land in the model, linked to the accounts, teams, vendors, products and workloads they belong to.
Type 1 and Type 2 analysis
Rules for what you know. AI for what you don’t. You for the call.
Most of a delivery looks like last period’s, so it shouldn’t need judgment. What’s new gets deliberate analysis — and a person still decides.
Type 1
Fast, deterministic, every line.
Known patterns resolve the same way every time, from rules you can read and decisions you’ve already made.
- Versioned rules anyone can read
- Exact, normalized and alias matching
- Remembered decisions — next period resolves on its own
- Codes such as accounts and cost centers never matched loosely
Same input, same answer — every time.
Type 2
Deliberate AI, only on the gaps.
Whatever the rules can’t resolve goes to AI analysis. Its job is to propose, with evidence — never to write a value on its own.
- The AI sees only the fields that are still unresolved
- Its output is a proposal, never a value
- Each proposal measured across the whole data set — support, samples and conflicts
- Patterns become rules; one-off fixes change one record only
The AI proposes. The rules engine measures. A person decides.
The learning loop
The first period needs help. Each one after needs less.
- 1Type 2 proposes a pattern, with its support and conflicts
- 2A person approves it — or edits it first
- 3It becomes a Type 1 rule, so the next delivery resolves it on its own
One-off fixes stay one-off: an approved fix applies to the single line it was made for and never quietly becomes a rule.
Traceability
Every number has a return address.
Click any figure in Dotwork and follow it down — to the record, the rule or decision that shaped it, and the exact row in the file it came from.
- Every record keeps its source file, row and a fingerprint of the original line
- Every derived value names the rule or decision that set it
- Every decision records who made it, when and what changed
- Rules are versioned, so a re-run reproduces the same result
In the model
Application hosting · October run cost
$1.24M
Record
Cost transaction 48,213
Vendor · Amazon Web Services
Set by
Rule v12 · Vendor from description
Approved by finance partner · 14 Oct
Source
gl_2026_10.csv · row 48,213
Line fingerprint 9f3c…a71e
Illustrative view · sample data
Built for financial data
Financial data gets financial controls.
Accuracy isn’t a report you run afterwards. It’s enforced at every stage, so a number that reaches the model is one you can defend.
Reconciled to the source
Stored plus refused plus excluded equals the source total, for every source, period and currency. A load that doesn’t tie out doesn’t move on.
Exact money
Currency is handled in exact decimal arithmetic — never floating point — so pennies don’t drift across millions of lines.
Read integrity
Declared lengths and checksums are verified before a line is read, so a truncated or altered file is caught at the door.
Nothing silently dropped
Refused and excluded lines are counted, kept and explained — never quietly left behind.
Safe to re-run
Idempotent loads, restatement by key, replay and backfill from the source — a second run never doubles a number.
Held when it looks wrong
A suspicious change waits for a person instead of flowing through to the forecast.
People stay in charge
AI does the sorting. People own the decisions.
Rights to process and to approve are separate, and every decision is recorded with who made it and what changed.
Onboarding analyst
Confirms how each new source maps to the schema and builds the starting rule set with AI help — once per source.
Approver
Usually finance or the TBM office. Gets notified in Dotwork and approves, edits or rejects each proposal with the evidence beside it.
Admin
Sets who can process and who can approve, and which AI models the analysis may use.
Where it lands
Cataloged into the operating model — not a warehouse.
Ingested lines don’t wait in a staging table for someone to model them. They land on the same records your strategy, portfolio, architecture and controls already use.
Cost Allocation
Ledger lines become the cost base you allocate to towers, applications and services.
ExplorePlanning, Budget & Forecast
Actuals against the plan, with every variance traceable to the ledger.
ExploreCloud Spend
The cloud bill by account, service, team and product — tied to the ledger it was paid from.
ExploreTokenomics
What each model, workload and team spends on AI, and what it returns.
ExploreAI Governance & Value
AI spend beside the controls, risks and outcomes it has to answer to.
ExploreDot and every AI
Clean, traceable numbers that Dot and any MCP client can read and cite.
ExploreCapabilities
Everything you’d expect from an ingestion engine — and the controls finance needs.
- Landing locations and scheduled loads
- Replay and backfill from the source
- Manifest and checksum verification
- Source recognition by header fingerprint
- Column mapping with AI suggestions
- Canonical schemas with your own extension fields
- Normalized periods, currencies, money and names
- Matching to records already in your model
- Duplicate detection
- Accrual and reversal pairing
- Data-quality checks and exceptions
- Approval inbox with evidence
- Versioned rule sets and a decision log
- Reconciliation gates and period close
- Line-level lineage to the source row
- Dead-letter handling and stall alerts
- Separate rights to process and to approve
- Your choice of AI model for analysis
How it compares
Pipelines move data. Cost tools map it to their schema. Dotwork maps it to yours.
Pipeline tools land data in a warehouse for someone else to model. Cost tools normalize bills into a fixed taxonomy. Dotwork brings every source into the operating model you already run — reconciled, explained and traceable.
| Capability | ETL and pipeline tools | Cost management tools | Dotwork Ingestion Engine |
|---|---|---|---|
| Ledgers, cloud bills and files loaded at line level | Partly | ||
| Mapped to your own operating model, not a fixed schema | Partly | ||
| AI proposals measured across the whole data set before approval | Partly | ||
| Approved fixes become versioned, readable rules | Partly | Partly | |
| Ties out to the source total every period | Partly | Partly | |
| Ledger, cloud, AI spend and context in one model | Partly | ||
| Every value traced to its source row and the decision that set it | Partly | Partly | |
| Lands beside strategy, portfolio, architecture and controls |
The model underneath
Every module makes the next one smarter.
Steer, Fund & Optimize and Govern & Deliver all read and write the same Enterprise Operating Model. The first module you license builds the model. Every module after it adds context instead of another silo — and Dotwork AI reads all of it, stopping where the decision is yours.
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