Make every data and AI decision traceable.
Davynci turns lineage, quality, privacy and search into the evidence and answers a bank actually needs — and lets an agent, or GitHub Copilot, do the legwork.
Outcomes for banks & fintechs
What each module delivers where it counts.
From BCBS 239 evidence to GDPR anonymization to agents you point at your data — what each module changes for a bank, and where it's headed next.
Prove where your reported numbers come from.
Pick a field in a regulatory report and see the upstream sources and transformations behind it, its downstream impact, the quality and privacy evidence attached, and the parts not mapped yet — then export a Word evidence pack. Weeks of manual evidence-gathering become a review.
Live today
- Attribute- and stored-procedure-level lineage, pending dependencies shown honestly
- Point-in-time data-quality standing for any date
- Trace a COREP/FINREP cell to its canonical data element and columns
- One-click Word evidence pack — lineage, quality, PII, ownership, regulatory mappings, stamped so it can't be quietly altered
On the roadmap
- Deterministic cross-database source-to-report edges
- PDF/A output and an LLM-drafted narrative
Find sensitive data, keep the inventory, generate the fix.
Davynci detects PII across every connected system, keeps a reviewable inventory that records how each finding spread, and generates database-specific anonymization SQL for the findings your team approves — it never mutates a source system on its own.
Live today
- Multi-stage detection (patterns, value scanning, model classification) with human review
- A durable inventory that shows how a finding cascaded to related columns
- Dialect-aware anonymization scripts — mask, hash, tokenize, encrypt, pseudonymize, generalize, suppress, redact — generated for review
On the roadmap
- Direct DSAR (data-subject request) fulfilment workflows
- Single-click apply of approved anonymization to a staging copy
Find the table, column, rule, term or standard you need — fast.
One indexed workbench searches across sources, tables, columns, data-quality rules, standards and glossary terms — and tells you when the index is stale instead of pretending it's current. What you find drops straight into review and rule-authoring.
Live today
- Bounded, indexed search and browse across catalogued assets
- Stable identifiers that flow into review and rule workflows
- Honest staleness — the index tells you when it's behind
On the roadmap
- Wider coverage of un-profiled and newly-arrived assets
Point an agent — or GitHub Copilot — at your data.
Ask an agent to assemble the impact of a change, draft and test a data-quality rule against real statistics, gather the evidence to verify a PII finding, or answer a question in plain English — through read-only, sandboxed queries. A person approves anything that changes. The same workflows run from GitHub Copilot and Claude.
Live today
- Visualize the impact of a change — the agent assembles it, the console renders it
- Draft and test a DQ rule against real statistics — you approve before it goes live
- Verify PII — the agent gathers the pattern, value and model evidence; a reviewer confirms or rejects
- Analyze data in plain English — read-only, allow-listed, row-limited, sandboxed, audited
- Connect from GitHub Copilot, Claude, or your own agents
On the roadmap
- Agents that action low-risk fixes on their own, within policy
- Deeper access controls and audit for external agent tools
The honest gaps are the point. A reviewer sees exactly what's evidenced and what's still pending before they sign — not a clean screen that hides the risk.
The Davynci principle for regulated data
Turn your estate into evidence.
See Davynci trace a field, generate the anonymization, and answer a question — with the gaps shown.