Gather what arrived.
Find what didn't.
Statements, emails and updates arrive at different times. Someone needs to know what is still missing.
CompletenessFrom scattered files
to a clearer picture.
Start with the problem
Less time bringing information together.
More confidence in the reports and answers that follow.
Many sourcesOne shared picture
Concept illustration
Prepared for Amrita · By Eric WuFor discussion. To refine together.
01Our understandingTo validate with your team
The work between
the documents.
Each reporting cycle means bringing different sources into agreement. Our understanding is that the repeated work sits here.
Gather what arrived.
Make the figures
mean the same thing.
Different accounts, periods, currencies and formats need to fit together without losing their meaning.
ConsistencyCheck, prepare.
Then check again.
Valuations, transactions and corrections need to agree before they become a client report.
ConfidenceFind the answer.
Explain where it came from.
A client question can mean reopening several files and reconstructing the same facts.
TraceabilityThe question for today: where does your team spend the most time, and where does uncertainty cause the most trouble?
02The proposed approachCurrent direction, open to refinement
Bring it together.
Build trust into every step.
A background service would turn incoming documents into checked information, ready for the files and questions your team already works with.
Follow one statement through the proposed processSelect a step
01 / CollectStart where the
Start where the
information arrives.
Read from agreed shared folders and mailboxes, so routine documents would not need to be downloaded and uploaded again.
Keep missing documents and failed reads visible. A duplicate should not become a second investment.
One statement, followed throughIllustrative
Incoming document01
An account statement
arrives.
arrives.
Received fromAgreed source
Document versionIdentified
Expected informationTracked
Ready to read
AI would help read and interpret. Defined calculations would produce the numbers.
Your team would resolve exceptions and authorize release.
03The implementation briefCandidate components, to evaluate
The thinking
behind the workflow.
I would start with a small service around your existing files, within an agreed data-processing boundary. These are the components I would evaluate and the checks I would put around them.
Connect to the sources.
Microsoft GraphRead agreed OneDrive or SharePoint locations and relevant mailboxes. Track document versions, duplicate arrivals and failed reads before processing.
First confirm the actual Microsoft 365 setup, ownership and permitted access.
Read the document.
Native readers · Specialist OCRUse spreadsheet data and usable PDF text directly. Compare Mistral OCR, Azure Document Intelligence, Reducto and PaddleOCR for scans and complex tables, with other readers available for comparison.
Choose by correct financial fields, complete tables and review effort. Open source is optional; the processing location needs agreement.
Interpret with evidence.
Private Qwen model · Extraction servicesCompare a privately hosted language model with an approved extraction service for fields such as account, period, currency and value. Keep the source evidence attached and missing information explicit.
The exact model, license, hardware and approved processing location still need evaluation.
Keep records and checks explicit.
Python / FastAPI · PostgreSQLSeparate extracted proposals from approved records. Use coded rules for calculations and reconciliation, and keep effective dates, source versions and review history.
Agree identities, financial definitions, source precedence and correction rules with the report owner.
Coordinate the work.
Rules · Jev candidate · Controlled jobsTest TypeSafe AI’s Jev for narrow choices after reading, such as document type, a candidate match or the next permitted route. Use ordinary jobs or LangGraph to execute the workflow; code retains calculations and approval gates.
Jev is not an OCR engine or a free-form extractor. Its published input price is $0.042 per million tokens, with output free. Compare it with rules and the interpretation model before adoption.
How questions would workNumbers and documents
Numbers and documents
need different checks.
Calculate over approved records.
Resolve the metric, date, currency and eligible population. The model would request an approved query; code would calculate the result and account for missing records.
Retrieve the supporting evidence.
Search permitted source documents for terms and explanations. Start with metadata and full-text search, then add semantic retrieval if evaluation shows a need. Preserve versions and citations.
Before relying on itWhat I would prove first.
What I would prove first.
- Establish a reference. Observe one reporting cycle and collect authorized documents, approved calculations and a report the team trusts.
- Test reading and decisions. Compare OCR on exact fields, complete tables and correction time. Separately compare rules, Jev and the interpretation model on routing errors, abstention and cost.
- Reproduce a complete result. Follow a statement into a checked record, an agreed report draft and an answer that matches the same reporting snapshot.
- Exercise the difficult cases. Replay duplicates, corrections, missing pages and interrupted work. Compare staff effort and total operating cost against the current process.
Start with the baseline. Consider fine-tuning only if repeated interpretation errors justify it. Scope, success thresholds, support ownership and any rollout decision would be agreed with your team.
04What this could change
Keep the familiar.
Improve what sits behind it.
The aim is to make the information dependable and reusable, while preserving the formats your team needs.
Illustrative outputs
Reports your team recognizes.
Prepare the agreed client reports and working files from checked records, with estimates and missing inputs made clear.
Answers you can trace.
Look up fund and client information, with an optional question interface. Answers would use the same records and show their evidence and gaps.
Fund materials, ready to assemble.
Find the right versions and combine selected fund teasers into a pack. Your team would choose the contents and authorize distribution.
The principle
One agreed basis for the report,
the answer, and the explanation.
05Trust and responsibility
Your team stays
in control.
Evidence stays attached.
A figure should lead back to its source, with corrections and previous versions kept explainable.
Uncertainty stays visible.
Missing information and conflicting values should be flagged for resolution before they are treated as approved.
Access follows your rules.
Private processing is the current direction. Where data may be used, who can see it and how long it is kept still need your approval.
People own the decisions.
The proposal supports operational records and reporting. Your team retains judgment, approvals and external release.
06Our conversation
Let's start with
one real reporting cycle.
A recent report, the documents behind it, and the person who prepared it.
That would let us test this understanding against your actual work and choose a useful place to begin.
01Where does the work get stuck?
Show us the last difficult reporting cycle. What took the most staff time, what caused delays, and which tools already helped?
02What makes a report ready?
Compare the month-start and month-end reports. Which sources and calculations govern each, what happens when information is late, and who approves release?
03What needs to stay familiar?
Show us the workbooks and document formats the team relies on. Who edits them, and when should a change become an official record?
04What would earn your trust?
Agree a representative report, its source records and a person to judge the result. Define acceptable checking effort, errors and operating responsibilities before setting targets.