The data intelligence operating system

Give your enterprise
a brain.

Davynci grows a living knowledge graph of your entire data estate. Autonomous agents build it, every human and AI consumes it, and it gets smarter with every question.

See how it works Talk to us →

The problem

Enterprise AI fails on the data it never understood.

Teams ship impressive prototypes, then hit a wall in production. The wall isn't the model — it's that nothing understands the estate end to end: what the data means, where it flows, whether to trust it. Every new agent rebuilds that context from scratch.

The semantic layer

One context layer. Every agent reads it.

Davynci's brain exposes a single semantic layer — the bridge between your data and your AI. Every human and AI agent reads meaning, lineage, quality and access from it with zero integration, while your sources feed it and agents keep it sharp.

Consume · humans & agents read the brain

Davynci UI GitHub Copilot Claude & any LLM Custom agents BI & analysts

The brain — your semantic layer

Every table, column, term, rule and relationship — unified, confidence-scored, always current.

self-reinforcing

Semantic Layer for AI agents

The one context layer every human and agent queries.

zero integration

Five super-agents build & maintain it

↻ Learn — built on ML plus open and frontier LLMs. Every scan reinforces what's true; every question sharpens what matters; stale relationships fade on their own.

autonomous agents build the brain from your estate · Build

Warehouses Databases Data lakes ETL & pipelines Applications

Tap a capability to see what it holds — the lineage, quality, and privacy tools live inside the brain, not on top of it.

How it works

A loop, not a pipeline.

Most platforms ingest once and serve. Davynci runs a loop — and the loop is why it compounds.

01 · BUILD

Agents build it

Five super-agents scan your estate daily — discovering lineage, generating quality rules, finding PII, mapping standards. No one fills in a catalog. The brain assembles itself.

02 · CONSUME

Everyone reads it

Ask anything in plain language. Humans through the UI, AI agents through one open interface — each answer carries the meaning, lineage, and privacy context the brain already knows.

03 · LEARN

It gets smarter

Every rediscovery strengthens a relationship's confidence; every unconfirmed one decays. Feedback and importance re-rank what matters. The graph crystallizes — day by day.

Build → Consume → Learn → Build. An appreciating asset, not depreciating documentation.

Applications on the brain

One brain. Every discipline.

Because the intelligence already lives in the graph, each discipline is an application that reads from it — not a separate tool you integrate and maintain.

Data lineage

Attribute-level, source-to-report — including dependencies not yet catalogued, shown honestly.

Data quality

Rules auto-generated from the graph; anomalies detected, explained, and traced to root cause.

GDPR & PII

Sensitive data found and classified across every system, masked automatically in every answer.

Business glossary

Assets mapped to standard terms, so the business and the data speak the same language.

Ask your data — guardrailed

Plain-English questions become governed SQL — PII-masked, access-scoped, sandboxed, and audited.

These are applications of Davynci — not the product. The product is the brain they all run on. Which is why a lineage answer already knows the quality score, and a quality alert already knows the lineage.

Why Davynci

Not a catalog. Not a point tool. A brain.

vs. data catalogs

Catalogs remember. Davynci understands.

A catalog is an inventory humans type into — and it decays the moment they stop.

Davynci's agents do the work, and the graph learns: relationships gain confidence when re-confirmed and fade when they go stale. It maintains itself.

vs. point tools

One lane, done well — but only one lane.

A quality tool watches quality. A lineage tool draws lineage. Neither shares a brain.

Davynci is the brain every lane runs on — so lineage, quality, privacy, and meaning are one connected intelligence, not four disconnected products.

Built for the enterprise

Serious infrastructure, not a demo.

Scale-first

Designed to hold at 100,000+ tables and millions of columns — indexed, bounded, and materialized from the first query.

Confidence, not guesses

Every relationship carries a Bayesian confidence that strengthens or decays with evidence — you see how sure the brain is.

Hybrid, not a black box

Graph algorithms and statistics do the load-bearing work; AI adds meaning and explanation. Every answer is traceable.

Model-agnostic

Runs on open and frontier LLMs alike. Your intelligence is yours — portable across models, agents, and clouds.

Governed by default

SSO, domain-scoped access, automatic PII masking, and an immutable audit trail on every answer.

Honest by design

No green-over-empty. If something isn't known yet, the brain says so — because a wrong answer costs more than a missing one.

What we believe

Convictions that shape every decision.

Intelligence should compound.

Knowledge that improves with use is an asset. Knowledge you re-enter by hand is a liability. We build the former.

Context is built, not typed.

The understanding your best analyst holds should be produced by agents and refined by people — not trapped in one head or one wiki.

Open to every agent.

Your data intelligence should move freely across models and tools. No lock-in on your own knowledge.

Give your enterprise a brain.

See Davynci build a living graph of your estate — and watch it answer questions no single system could.

Talk to us See how it works →

Platform › Metadata & Lineage

Trace every answer back to where it came from.

Davynci's agents map your whole estate — schemas, keys, and the relationships no one documented — into attribute-level lineage you can trust for every AI answer and every audit.

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If you can't trace it, you can't trust it — or attest to it.

Discovery

Finds the relationships no one documented.

Foreign keys are the easy part. Davynci's agents infer the implied and cross-source relationships — and detect when two columns in different systems are really the same thing.

Declared, implied, and cross-schema keys
Synonym detection across systems (cust_id ≡ customer_no)
A confidence score on every edge — you see how sure it is

Attribute-level lineage

Column to column, source to report.

Lineage isn't table-to-table hand-waving. Davynci parses stored procedures, views and ETL to resolve lineage down to the column — so "where did this number come from?" has an exact answer.

Column-level chains from source to report
Code lineage from stored procs, views, SSIS / PL-SQL
One click from any field to its full upstream

Honest by design

Never a blank panel that lies.

When a procedure depends on tables not yet catalogued, Davynci shows them as pending — visibly incomplete — instead of a clean graph that hides the gap. A regulator-grade answer, not a comforting one.

Pending dependencies flagged, never hidden
No green-over-empty — absence is shown as absence
Exactly the gap list you need before you sign off
One-click Word evidence pack for the field — gaps marked pending

Impact & importance

Know what breaks before it breaks.

Trace downstream to every report a change touches, and upstream to the root. PageRank ranks the columns everything depends on, so you fix what matters first.

Upstream root-cause and downstream blast radius
PageRank centrality — the load-bearing columns
Continuous monitoring: schema changes caught daily

Under the hood

Everything the lineage agent does.

Schema discovery

Full schema pulled across every connected database, kept current.

Cross-source keys

Declared, implied, and cross-schema relationships — not just FKs.

Synonym detection

Finds equivalent or duplicated data across different systems.

Column-level lineage

Attribute-to-attribute chains from source all the way to report.

Code lineage

Parsed from stored procedures, views, and ETL packages.

Impact analysis

Upstream root-cause and downstream blast radius on demand.

PageRank importance

Ranks the load-bearing columns everything else depends on.

Change monitoring

New tables, dropped columns and type changes caught daily.

“I used to assume a procedure with a blank lineage panel was simple. Now I can see it reads eight tables — and three aren't in our catalogue yet. That's exactly the list I need before I sign off on a report.”

— Data steward, regulatory-reporting team

One brain, many modules

Lineage is one module on the semantic layer.

Because it shares the brain, a lineage answer already knows the quality score, the PII, and the business meaning of every column it touches.

Metadata & Lineage Business Glossary PII & Lifecycle Data Quality Ask Your Data

Trace every number to its source.

See Davynci map your estate and answer “where did this come from?” — down to the column.

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Platform › Business Glossary & Processes

Make the business and the data speak the same language.

Davynci maps every column to standardised business terms — with four matching strategies and a confidence score — then links your processes to the data behind them.

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A term a regulator recognises, mapped to the column that actually holds it.

Matching

Four ways to match, one confidence score.

Names rarely line up cleanly. Davynci matches columns to terms four ways — and reinforces the confidence every time the evidence repeats.

Exact, synonym, fuzzy, and semantic (LLM) matching
Bayesian confidence that strengthens with evidence
Business context generated for every term

Standards

Aligned to the standards regulators expect.

Map your estate to BIAN and BIRD, see exactly what's covered and what isn't, and export a glossary that's ready for an audit.

BIAN / BIRD standards loaded and hierarchical
Standards gap analysis — mapped vs. not
Exportable glossary and auto-documentation

Processes

Processes linked to the data behind them.

Document workflows in BPMN, and Davynci links each activity to the glossary terms and the physical data — even to the regulatory cells they feed.

BPMN 2.0 processes, activities auto-extracted
Activity-to-term matching across four strategies
Term-mediated mapping to COREP / FINREP cells

Under the hood

Everything the glossary agent does.

Four matching strategies

Exact, synonym, fuzzy, and semantic (LLM) — each scored.

Standards library

BIAN and BIRD loaded, hierarchical, and queryable.

Gap analysis

Exactly what's mapped to a standard and what isn't.

Business context

LLM-written definitions and context for every term.

BPMN processes

Activities auto-extracted and linked to terms and data.

Delta enrichment

Re-mapping catches schema drift and new assets daily.

“We can finally answer ‘is retail mapped to BIAN, and where are the gaps?’ on one screen — instead of running it as a six-month project.”

— Data governance lead

One brain, many modules

Glossary is one module on the semantic layer.

Metadata & Lineage Business Glossary PII & Lifecycle Data Quality Ask Your Data

Map your estate to the standards you report on.

See Davynci align your columns to BIAN and BIRD — and show you the gaps.

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Platform › PII Identification & Lifecycle

You can't protect what you can't find.

Davynci detects and classifies sensitive data across every system, scores its sensitivity, maps it to the regulations that apply, and masks sensitive values in query responses.

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Every piece of personal data — found, classified, and governed for its whole life.

Detection

Multi-stage detection that catches what single signals miss.

Column names alone are unreliable, so Davynci layers pattern rules, real value scanning, and an LLM — each stage catching what the last one couldn't.

Pattern / rule detection on names and formats
Value scanning with Davynci small models, trained on PII
LLM classification with a confidence score

Classification

Sensitivity — and the regulation that applies.

Each finding gets a sensitivity level and a map to the frameworks that govern it, and Davynci cascades a hit to the synonymous columns hiding elsewhere.

Critical / high / medium / low sensitivity
Mapped to GDPR, CCPA, GLBA, HIPAA, PCI-DSS
Cascades to synonym columns across systems

Lifecycle

Governed for its whole life — not just found once.

Detection is the start. Davynci keeps one PII inventory for the entire bank, generates anonymization scripts for the findings you approve, and keeps watching as new data appears.

Detect → review → remediate → monitor
One PII inventory for the whole bank, in one place
Anonymization scripts generated for approved findings
Scoped exclusions and continuous surveillance

Under the hood

Everything the PII agent does.

Pattern detection

Name and format rules across every connected system.

Value scanning

Davynci small models, trained on PII, read real samples safely.

Sensitivity levels

Critical to low, assigned per finding with confidence.

Regulatory mapping

GDPR, CCPA, GLBA, HIPAA, PCI-DSS per PII type.

Anonymization scripts

Dialect-aware SQL — mask, hash, tokenize, generalize — generated for review.

Continuous surveillance

New PII flagged the moment it appears.

“Now we have a live PII inventory we can hand a regulator — not a spreadsheet that was stale the day we finished it.”

— Data protection officer

One brain, many modules

PII is one module on the semantic layer.

Metadata & Lineage Business Glossary PII & Lifecycle Data Quality Ask Your Data

Find every piece of sensitive data — before someone else does.

See Davynci build a live PII inventory across your whole estate.

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Platform › Data Quality Management

Stop trusting data you never checked.

Davynci generates quality rules straight from the knowledge graph, detects anomalies with statistics and ML, and traces every one to a root cause — because it already knows the lineage.

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From silent data errors to governed, explained, resolved.

Six quality dimensions, scored per column

CompletenessValidityConsistencyAccuracyTimelinessUniqueness

Rule generation

Rules you didn't have to write.

Other tools make you author checks by hand. Davynci reads the graph's statistics and standards and proposes the rules itself — you review and activate.

Auto-generated from discovery stats and standards
Six categories: completeness, validity, consistency, accuracy, timeliness, uniqueness
Severity raised for regulated and load-bearing columns

Detection

Anomaly detection tuned for real data.

Naive thresholds drown you in false positives on skewed financial data. Davynci routes detection through robust statistics, so alerts mean something.

Statistical (Z-score, IQR, KS), LSH clustering, and ML
MAD-routing → far fewer false positives on log-normal data
Continuous execution on a schedule, at scale

Resolution

Explained and resolved, not just alerted.

Because the brain knows the lineage, an anomaly arrives with its likely cause and its blast radius — the detective work is already done.

LLM root cause, grounded in lineage
Incident correlation across five strategies
Contracts, SLAs, scorecards, point-in-time standing

Under the hood

Everything the data quality agent does.

Auto rule generation

Rules proposed from the graph — you approve and activate.

Six rule categories

Completeness, validity, consistency, accuracy, timeliness, uniqueness.

Statistical + ML detection

Z-score, IQR, KS, LSH clustering — MAD-routed.

Lineage-aware root cause

LLM explanation with the upstream cause attached.

Incident correlation

Five strategies group related anomalies into incidents.

Contracts & scorecards

SLA-based agreements and audit-ready quality scores.

“The anomaly didn't just fire — it told me which upstream table changed and which report it would hit. That used to be a day of detective work.”

— Data steward

One brain, many modules

Data quality is one module on the semantic layer.

Metadata & Lineage Business Glossary PII & Lifecycle Data Quality Ask Your Data

Govern quality across your whole estate — automatically.

See Davynci generate the rules, catch the anomalies, and explain the cause.

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Platform › Ask Your Data

Ask your whole estate anything. Safely.

Plain-English questions become governed SQL across every system — grounded in the graph, PII-masked, access-scoped, run in a sandbox, and fully audited.

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The power of ask-anything, with the guardrails a bank requires.

Anyone can ask — in plain English

“Is customer 12345 in CRM?”“Why was this loan declined?”“Top 100 customers by exposure”

Grounded

Grounded in the graph, not guessing.

It doesn't hand your question to an LLM and hope. Davynci links your words to real tables, walks the join paths in the graph, then writes SQL that's actually correct.

Entity linking fuses vector, keyword, and graph signals
Join paths taken from the real knowledge graph
SQL validated before it ever runs

Guardrails

Guardrails on every single query.

Every question passes the same gates: sensitive values masked, results scoped to the user's role, execution sandboxed, and the whole thing written to an audit trail.

PII masked in responses automatically
Access-scoped by role and domain
Sandboxed execution, never an unsafe source query
Immutable audit trail on every answer

For everyone

One layer for humans and agents.

The same governed layer serves a business user in the UI and an AI agent through one open interface — every answer carrying its meaning, lineage, and risk context.

Plain-English UI for any business user
One interface for Copilot, Claude, or your own agents
Schema, lineage, PII and quality context per answer

Under the hood

Everything the query agent does.

Hybrid entity linking

Vector, keyword, trigram and PageRank fused (RRF).

Graph join paths

Real relationships from the graph, not guessed joins.

Sandboxed execution

Runs in DuckDB — never an unsafe live-source query.

PII masking

Sensitive values masked in the response, by role.

Access-scoped

Answers bounded to what the user is allowed to see.

Full audit trail

Every question and answer logged, immutably.

“A business user asked ‘is customer 12345 in CRM?’ and had an answer in seconds — masked, logged, and scoped to exactly what they're allowed to see.”

— Head of data

One brain, many modules

Ask Your Data is one module on the semantic layer.

Metadata & Lineage Business Glossary PII & Lifecycle Data Quality Ask Your Data

Give everyone safe answers from all your data.

See a plain-English question become a governed, masked, audited answer.

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Outcomes

The regulatory work you have to do — with the evidence to prove it.

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.

Talk to us See the platform →

Built for the data estate behind BCBS 239 · RDARR · GDPR · COREP/FINREP · DORA

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.

BCBS 239 · RDARRLineage & evidence →

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

GDPR · Art. 30PII & lifecycle →

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

Search & discoveryThe semantic layer →

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

AI agents · GitHub CopilotAsk your data →

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.

Talk to us See the platform →