The data operation,
run by agents
verified by people.
Data Operations as a Service turns raw documents, annual reports, regulatory filings, spreadsheets, into structured, quality-grade datasets. AI agents do the extraction. Humans verify every fact that matters. You get data you can sign off on.
Gross premiums written
USD m · p.18
Profit before tax
USD m · p.18
Total assets
USD m · p.18
Investment income
USD m · p.18
Not software you operate. An outcome we deliver.
Most tools hand you a model and wish you luck. Data Operations as a Service is the whole operation, people, agents and platform pointed at one job: producing the dataset you need, to a standard you can defend.
You bring the documents and the standard. We bring the agents that read them, the analysts who verify them, and the platform that proves it, handing back a living dataset, not a tool to babysit.
WHAT WE OWN, END TO END
Agents for scale. Humans for trust.
Six things have to be true to ship data you can defend. Data Operations as a Service is built around all six.
AI agents do the heavy lifting
Purpose-built agents read documents end to end and structure them against your taxonomy, no templates, no brittle rules to maintain.
A human verifies what matters
Confidence-driven review puts an analyst on every fact that counts. We deliver accuracy a regulator, or your board, can sign off on.
Built on real domain taxonomies
Finance, insurance and regulatory schemas out of the box. Or we model yours in discovery and version it as standards evolve.
Provenance on every fact
Every value traces back to a page, a snippet and a confidence score, with an immutable audit trail from ingest to publish.
From backlog to throughput
Hundreds of documents processed in parallel. Weeks of manual keying compressed into days, without adding headcount.
Enterprise-grade governance
SOC 2 controls, role-based access, SSO and your choice of data residency, deployable inside your own environment.
One pipeline, ingest to published dataset.
Every engagement runs the same four stages. Agents move fast and at scale; a human stands at the gate before anything is published.
A 60-page PDF becomes a dataset.
Watch a line of an annual report become a normalized, traceable, confidence-scored fact.
Normalized
Mapped to a standard taxonomy and consistent units (USD m), so every report is comparable.
Traceable
Every value links back to its source page and the exact snippet it was read from.
Confidence-scored
Each fact carries a model confidence score that drives human review before it publishes.
This is what the review pod actually does.
Pick a fact, check it against the source snippet, then accept, edit or flag it. Watch the dataset complete, nothing publishes until it does.
SELECTED FACT
Gross premiums written
SOURCE SNIPPET · p.18
“…Gross premiums written 2,948.4…”
REVIEWED VALUE
2,948.4
Built on the hardest financial documents there are.
We didn’t start with toy data. The pipeline was forged on regulated insurance reporting, where a wrong number has consequences.
INSURANCE · ANNUAL ACCOUNTS
Lloyd's of London
Syndicate annual report & accounts
THE CHALLENGE
Every syndicate files a 50 to 60 page annual report. Analysts re-keyed ~120 financial facts per report, per year, by hand, slow, costly and error-prone.
WITH DOaaS
Agents extract the full taxonomy from each report; high-confidence facts flow straight through, the rest land in a confidence-ranked review queue. Analysts verify against the source PDF, side by side.
12
syndicates onboarded
8
reporting years
120+
facts per report
99.6%
field accuracy, verified
Review time per report cut from ~3 days to under 4 hours.
REINSURANCE · REGULATORY RETURNS
Bermuda
Re/insurer statutory financial returns
THE CHALLENGE
Carriers file statutory financial statements in differing formats. Comparing solvency and performance across the market meant painstaking manual normalization.
WITH DOaaS
A common taxonomy normalizes every carrier's return into one comparable dataset, statutory and GAAP figures reconciled, refreshed each reporting cycle.
40+
carriers normalized
1
common taxonomy
Quarterly
refresh cadence
100%
provenance to source
Cross-carrier comparison that used to take weeks now refreshes in a cycle.
Any industry buried in documents.
If your dataset lives inside PDFs and filings today, it can be a clean, governed table tomorrow.
Insurance & reinsurance
Syndicate accounts, statutory returns, solvency data, treaty submissions.
Banking & capital markets
Annual reports, prospectuses, credit files and regulatory filings.
Regulators & supervisors
Normalize filings across entities into one comparable, defensible dataset.
Audit, tax & advisory
Digitize financial statements and working papers into structured facts.
ESG & disclosures
Pull metrics from sustainability and disclosure documents at scale.
Private markets
Fund reports, capital accounts and portfolio company financials.
Data you can put your name on.
Speed is worthless if you can’t trust the output. Every fact is scored, reviewed, traced to its source and logged so the dataset stands up to an auditor, a regulator, or your own risk team.
Confidence on every fact
Each extracted value carries a model confidence score that drives review triage.
A human review gate
Nothing reaches the published dataset without passing analyst verification.
Source provenance
Every fact links to its page and the exact snippet it was read from.
Immutable audit log
Every edit, approval and publish event is recorded and attributable.
Versioned taxonomies
Schemas are versioned, so datasets stay comparable as standards change.
SOC 2 · RBAC · residency
Role-based access, SSO and your choice of data region, or your own VPC.
Plugs into how your data already moves.
DOaaS runs on the Praveg platform, agents, connectors and governance, and hands datasets off wherever you need them.
Ingest
- Native PDF
- Scanned PDF / OCR
- Excel · CSV
- Regulatory returns
- Data connectors
- MCP
Runs on Praveg
- Extraction agents
- Data Connectors
- Data Sentinel
- MCPs
- Review workspace
Deliver to
- Excel export
- REST API
- Snowflake · warehouse
- BI tools
- Webhook / push
Start with a pilot. Scale to an operation.
We prove it on your data first. Then you choose how you want it run, by us, or inside your own environment.
01
Discovery
We scope the documents, the taxonomy and the standard of accuracy you need.
02
Taxonomy design
We map (or model) the schema your dataset will conform to, and version it.
03
Pilot
Agents extract, the review pod verifies, you get a delivered dataset to judge.
04
Production
We run it as a managed operation, or embed it in your stack, at full cadence.
Pilot
Prove it on your data
A fixed-scope pilot on one of your document sets. Taxonomy, extraction, full human review and a delivered dataset, plus an outcome report.
- One document set
- Taxonomy mapping
- Full HITL review
- Delivered dataset + report
Managed Service
We run your data operation
An ongoing, fully-managed pipeline with a dedicated review pod and SLAs on turnaround and accuracy. You receive datasets; we run the operation.
- Dedicated review pod
- Accuracy & turnaround SLAs
- Monitored throughput
- Continuous taxonomy upkeep
Embedded
Agents in your stack
Deploy the extraction agents and review workspace inside your own environment. Your team operates day to day; we support and tune.
- Runs in your VPC
- Your team operates
- Onboarding & training
- Priority support
Questions
The things buyers ask first.
Every fact gets a model confidence score, and every fact that matters is verified by a human against the source. We report field-level accuracy and you sign off before anything publishes, so the number you ship is one you can defend.
Native and scanned PDFs, Excel and CSV, and structured regulatory returns, from clean annual reports to messy multi-hundred-page filings. OCR handles the scanned ones; if a person can read it, our agents can extract it.
Either. We ship domain taxonomies for finance, insurance and regulatory reporting out of the box, or we model yours during discovery and version it as your standards evolve.
Your choice. We support regional data residency and deployment inside your own VPC, with SOC 2 controls, role-based access and SSO, so your data stays where your policies require.
A scoped pilot on one of your document sets typically runs in weeks, not months. Discovery and taxonomy design come first, then a delivered dataset you can judge before committing to production.
OCR reads characters; an LLM guesses structure. We map every document to your taxonomy, attach a confidence score and source snippet to each fact, and put a human reviewer on everything that matters, so you get a defensible dataset, not a best-effort transcript.