AI-Native Data Room
An AI-native data room for financial request-list workflows.
Built for diligence and advisory teams that manage structured request lists, collect messy client files, and need to know what actually satisfies each request before the team starts building the master.
Request lists imported from Excel
Bring in the QoE / FDD tracker your team already uses and turn each line item into a tracked request.
No-login client portal
Clients upload files through a secure link instead of replying in email threads or shared folders.
Request satisfied, not just file received
SynthGL checks what arrived, not just that something arrived, and flags missing periods or mismatched documents.
Version history and review trail
Updated files stay linked to the same request item so the team can see what changed before analysis starts.
Pilot Workflow
Run intake like a live request workflow, not a shared spreadsheet.
Bring in the tracker your team already uses, send one secure upload link, and review what is still missing before associates start building the databook.
Import the request list your team already runs on
Start with the Excel tracker your team uses today. Request numbers, categories, owners, due dates, and notes become a live engagement workspace instead of another spreadsheet to maintain.
Send one secure client upload link
Clients upload documents without creating accounts. Every upload is timestamped, versioned, and tied back to the request workflow your team manages.
Review what is missing before the team builds the master
SynthGL auto-matches uploads, surfaces missing period coverage, and raises consistency warnings before associates start copying numbers into the databook.
Positioning shorthand
Suralink tells you a file arrived. SynthGL helps the team understand whether the file actually answers the request.
Trust And Proof
Trust matters before intelligence matters.
For confidential deal workflows, buyers need clear answers on storage, isolation, auditability, and data handling before they care about AI claims. The pilot page makes those boundaries explicit.
Tenant-scoped engagements and secure upload links
Engagements, request items, and documents are designed around tenant boundaries, while external clients upload through scoped, no-login links.
Workbook-aware review, not filename-only triage
Pilot teams see sheet inventory, workbook structure, and review signals so intake starts with the file itself, not just its filename.
Customer uploads are not training fuel
Customer files support the current engagement workflow. They are not used to train shared models, and the page says that plainly.
Live now
Pilot teams evaluate the current alpha.
- Request-list import from existing Excel trackers
- No-login client uploads tied back to request workflow
- Auto-classification and request matching on upload
- Workbook inspection with sheet inventory and integrity badges
- Audit trail, dashboard analytics, and engagement history
Coming next
Auto-ingestion becomes the next milestone, not today's claim.
Once the request workflow is in place, classified documents can move into the normalized review flow. That is the next layer of the product, not the thing pilot teams are being asked to trust today.
Narrow Gtm, Wide Data Model
Start with QoE / FDD. Expand only after the wedge is earned.
The initial buyer is a QoE / FDD team managing messy client intake today. The expansion path matters, but it stays subordinate to the first workflow we are actually trying to win.
Founder Credibility
Built by someone who lived the request-list pain.
SynthGL comes from real deal work: first at PwC Deals, then inside a boutique QoE / FDD firm where the first week of every engagement disappeared into intake, follow-up, and "build the master" work.
PwC CMAAS + boutique QoE / FDD background
Three years at PwC Deals, then direct buy-side and sell-side QoE / FDD work at a boutique firm. The workflow came from living the handoff between client files and analysis.
Accounting engine already exists
Behind the pilot is a 75K LOC financial data platform with 2,500+ automated tests, a compiled accounting rules engine, and a working ingestion stack already under hardening.
Calm proof, not AI theater
The page leads with secure uploads, completeness review, and workbook-aware workflow because that is what deal teams can actually evaluate in a pilot.
FAQ
Answer the objections that actually block a pilot.
A managing director or manager should be able to decide whether this is credible enough to test on a real engagement without hunting for the trust story between the lines.
Pilot Program
One CTA. One next step.
We are not publishing pricing cards or splitting buyers across demo, booking, and waitlist paths. If this matches your workflow, apply for the pilot and we will review fit.
Best fit for the first pilots
QoE / FDD teams that already run structured request lists, collect a large volume of Excel/PDF support, and want a tighter intake review workflow before analysis.
What pilot teams evaluate
Request import quality, client upload experience, review surfaces, versioned document handling, and whether the trust/proof story is strong enough for real deal work.
What happens after you submit
We review fit, follow up on your workflow, and use the pilot to shape the next hardening pass instead of pretending the commercial model is already fully locked.
Apply for the QoE / FDD pilot.
Tell us how your team handles request lists today and where intake slows the engagement down.