High-stakes work buried in copy-paste
Tax filings, bank statements, policy schedules, and claims arrive in inconsistent formats. Qualified staff spend time finding values, checking pages, and moving information into another system.
Document automation for financial operations
Move Client Documents From Intake to Review, With the Evidence Attached.
Custom document AI and Agentic RAG workflows for insurance brokerages, accounting firms, and wealth-management teams. Extract information, cross-check approved sources, and prepare a draft for an accountable reviewer.
01Evidence beside each finding
02Reviewers own the final decision
03Data paths stay visible
The friction
Your reviewers should spend their time on judgement.
The cost sits in the repeated search, cross-checking, and drafting around a decision that still needs a qualified human.
Answers without an evidence trail
A generated answer is difficult to trust when the reviewer cannot see the source. Missing pages, conflicting values, and outdated policies need explicit handling.
Drafting that repeats the same work
Once the review is complete, staff still prepare client letters, summaries, and approval notes from information they have already checked.
The custom AI solution
A document workflow your reviewers can inspect.
The system keeps extraction, retrieval, and drafting connected to the sources and decisions that matter.
Extract and organize
Classify incoming documents, extract agreed fields, and attach page or section references. Missing information and conflicting values enter a review queue.
BoundaryThe workflow shows where a field came from and marks uncertainty for review.
Retrieve approved evidence
Agentic RAG combines document retrieval with controlled tool use. The system searches your approved playbooks and policy library, then presents relevant evidence beside the proposed finding.
BoundaryYour team owns the source library, its effective dates, and its update process.
Draft for human sign-off
Prepare review summaries, client letters, and approval drafts from the structured record. Authorized reviewers can edit, reject, escalate, or approve before anything is sent or written downstream.
BoundaryThe workflow supports review. Regulatory interpretation and final approvals remain with your authorized professionals.
Evidence interface
Every finding should be traceable.
When the evidence is incomplete, the workflow marks the case for review.
CLIENT DOCUMENT
Extracted valueSource page
Applicable playbookEffective date
DiscrepancyReviewer decision required
Security and data handling
Know where sensitive information goes.
The architecture brief maps document storage, processing, model access, logs, backups, and deletion. We assess local or private-hosted deployment where required and document the controls for any external service.
The proposed design can include role-based access, encryption in transit and at rest, tenant separation, audit logs, and retention rules.
If a hosted model is used, we verify its training-use terms, retention settings, eligible features, and contractual requirements before production.
Data boundary Sensitive client data follows the storage, processing, retention, and deletion controls agreed for your deployment. We do not describe a provider-wide retention promise where the configuration or product terms do not support it.
A focused first pilot
Test one document family against a known review standard.
Begin with redacted examples, your approved playbook, and a defined decision. We build a draft-only workflow and compare its outputs with your reviewers’ expected results.
You receive
- A workflow and data-ownership map
- A working integration for the agreed systems
- Conflict, retry, and approval rules
- An operator view, runbook, and pilot readout
What we measure
Field accuracyCitation accuracyMissed discrepanciesReviewer timeDraft acceptanceProcessing cost
The working stack
Built around the systems you already use.
Model choice follows document quality, hosting constraints, evaluation results, and operating cost.
LangChainLlamaIndexPostgreSQL + pgvectorLocal Llama modelsSecure Node.js backends
Questions founders ask
A few useful answers before we start.
Can documents stay in our environment?
We can assess private-hosted or local inference. Hardware, model quality, identity controls, and support requirements determine whether that approach fits.
Does the system know the latest regulations?
It uses the approved sources provided to the workflow. Your compliance owner controls which material is authoritative and how updates are reviewed.
Will it approve a case automatically?
The pilot requires human sign-off. Any further automation needs explicitly approved decision rules and acceptance criteria.
What does the architecture brief include?
The proposed data path, deployment options, principal risks, review boundaries, and pilot recommendation. Detailed specifications can follow through a scoped discovery engagement.
A useful next step
Bring one review process that takes too much expert time.
Tell us which documents arrive, what reviewers must decide, and which data constraints apply. We’ll identify the architecture questions and a practical first pilot.