Turgon
TURGON AI · AI-NATIVE DATA MODERNIZATION · SAN FRANCISCO · DUBAI · STOCKHOLM · INDIA

Your data estate,
rebuilt by agents.

A multi-agent platform does 90% of the engineering: discovery, pipelines, models, tests, lineage. Our human in the lead validates every step. Legacy estates become AI-ready infrastructure in weeks.

See the strategy ↓Watch the agents workHow we engage
5x
faster than a traditional SI
4 wks
typical POC delivery
−40%
lower cost
100+
enterprise systems supported
agents · live feedlive
01 / The problem

Enterprises were built for humans. Agents can't work on them.

✕ ERR_SILOS
Fragmented sources

ERPs, CRMs, spreadsheets and legacy platforms that never agree on a single number.

✕ ERR_PIPELINE
Brittle pipelines

Hand-built ETL that breaks on every schema change and nobody wants to touch.

✕ ERR_SEMANTICS
Context in people's heads

What a metric means lives in tribal knowledge, not in anything an agent can query.

02 / What we do

AI as a strategy, not a tool.

Five layers take an enterprise from raw information to measurable outcomes — with trust and governance running through every one of them.

L1 / Information

A governed, multimodal data fabric

Can AI trust our data?

Agents discover, profile and migrate every source into an open medallion lakehouse, with quality and parity proven before cutover.

delivers
LakehouseData dictionaryQuality SLAs
runs on
SAPOracleYardiSalesforceDatabricksSnowflakeMicrosoft FabricIceberg/Delta
01Source discovery and profiling
02As-is map and data dictionary
03Ingestion and streaming
04Legacy platform migration (SAP DS, Informatica, SSIS, IBM i → open code)
05Medallion lakehouse
06Master data management
07Data quality and observability
08Parity-proven cutover
03 / Use cases / Real estate

Real estate intelligence, reimagined.

a leading GCC mall operator

A leading GCC mall operator ran leasing from static Power BI reports built on Yardi exports, with occupancy calculated differently from report to report. Turgon rebuilt the data on a governed Databricks lakehouse and delivered a Leasing Intelligence Platform in six weeks, where agents explain performance, rank assets against peers and flag expiring leases before they become vacancies.

real-estate.liveL1 → L4 · six weeks
before · Power BI MANUALafter · Leasing Intelligence Platform AGENT-RUN
Power BI·Leasing Report
GLA by mall'000 sqm
RiversideHarbourOasisAl Noor
Occupancy by class
62%
■ A■ B■ C
Leases expiringmatrix · manual export
Riverside Mall142—
Harbour Point98—
Oasis Centre61—
OverviewLeasesTenantsFinancialsLast refreshed: 14 days ago
■ LILeasing Intelligence Platformlive · refreshed 4 min ago

Portfolio occupancy is up 1.2 pts QoQ; class-A assets lead on rent per sqm. Three renewals need attention before Q4.

Occupancy
95.1%
▲ +1.2 pts
Revenue GLA
SAR 245.3m
▲ +3.4%
Rent / sqm
2,006
▲ +2.1%
Occupancy trend · 16 wksgold layer
Class-A peer ranking
1Riverside Mall97.4%
2Harbour Point96.0%
3Al Noor Mall94.8%
agent
›query gold.asset_occupancy
›join peers · class A
›rank by rent/sqm
›detect expiring leases
›draft summary
›validator · 6/6 tests pass
Approve change?
model: gold.asset_occupancy
✓ approved · N.M.
⇆
View the full demo
03 / Use cases / Finance

Finance intelligence, reimagined.

a multi-entity industrial group on four ERPs

A five-entity group consolidated receivables in Excel every week: manual exports, typed FX rates and broken lookups. Finance Intelligence syncs every ledger overnight, writes the findings, ranks collections by risk of slipping, refreshes the 13-week cash forecast and keeps an audit trail back to the source invoice.

finance.live4 ERPs → one model
before · ERP exports & Excel MANUALafter · Finance Intelligence AGENT-RUN
consolidation_FY26_Q3_v17_FINAL2.xlsxshared drive · 34 versions
SAP ECC · DE
Q3 close
12 days
stale
Oracle EBS · UK
Q3 close
9 days
stale
Dynamics · US
Q3 close
—
missing
Tally · IN
Q3 close
21 days
stale
Manual consolidation412 formulas · 6 broken links
=VLOOKUP(... #REF!
=SAP!B44+Oracle!C31+...
FX rates keyed by hand · monthly
Intercompany eliminations · manual journal
Weekly receivables consolidation
day 1day 7
⚠ numbers differ per ERP · no single source of truthlast saved by 3 people
FINGroup Finance Intelligencelive · 4 ERPs reconciled

Receivables synced overnight. US collections are most at risk of slipping; the 13-week cash forecast is refreshed with source-invoice lineage.

Revenue YTD
$1.24bn
▲ +6.1%
EBITDA margin
21.4%
▲ +0.8 pts
Close cycle
3 days
▲ −9 days
Cash forecast · 13 wksgold layer
Collection risk · by entity
1US · DynamicsHigh
2DE · SAPMedium
3UK · OracleLow
agent
›query gold.receivables
›sync 4 ERP ledgers
›rank collection risk
›refresh 13-week cash forecast
›trace source invoices
›validator · 8/8 tests pass
Approve change?
model: gold.receivables
✓ approved · N.M.
⇆
View the full demo
04 / teos runtime

Agents draft. Agents verify. Humans approve.

Every artefact is drafted by one agent, challenged by a paired validator, and promoted only after a named human signs it off in git.

teos · run 2214 · models/gold/asset_occupancy.sql
agents
discovery.agentdone
ingest.agentdone
modeling.agentdrafting
validator.agentwaiting
human.gatepending
validator

From six months to six weeks

traditional SI · ~6 months
Discovery workshops
Requirements & design
Build pipelines
Testing & UAT
Go-live
turgon · 6 weeks
Understand
Ingest
Reverse-engineer KPIs
Silver
Gold
Live
wk 0wk 13wk 26
W1
Understand

Agents profile sources and draft the as-is map.

W2
Ingest

Sources land in bronze with drift monitoring.

W3
Reverse-engineer KPIs

Existing reports decoded into metric definitions.

W4
Silver

Cleaned, conformed, tested entities.

W5
Gold

Business models with parity proven.

W6
Live

Applications and agents in production.

05 / deployments

In production with Fortune 500s and regional leaders.

Cenomi
Western Digital
Schneider Electric
atNorth
Saxonia-Franke
Penguin Solutions
LVT
McCarthy Tire Service
clientsector · regionlayersstackoutcome
Cenomi CentersRetail real estate · KSA
L1L2L3L4L5
Yardi Voyager, Ejar, Appian → Databricks · MCP agentsPower BI → CLIP in 6 weeks
atNorthData centres · Nordics
L1
Microsoft FabricPOC delivered · phase 2 adding service-management sources
Saxonia FrankeIndustrial manufacturing · Germany
L1L3
SAP → governed finance booksMaterial master data validation
Western DigitalTechnology · US
L1L3
LakehouseUnified data powering revenue analytics
Schneider ElectricEnergy · technology partner · Global
L1
Utilities data infrastructureUtilities data infrastructure
LVTTechnology · US
L1
Multiple systems → unified ERPERP data migration
McCarthy Tire ServiceIndustrial services · US
L1L3
IBM i → real-time analyticsLegacy modernised in 5 weeks
Penguin SolutionsTechnology · US
L1L3
Multiple CRMs → one real-time BICRM consolidation in 3 weeks
06 / partners

Partners who take us further, faster.

Consulting and integration partners that bring the transformation mandate and delivery scale, a technology partner in critical infrastructure, and investors who back the model.

Consulting & system integrator partners
PwC
PwC
Consulting partner
Burnt Oak Partners
Burnt Oak Partners
Consulting partner
Mastek
Mastek
System integrator partner
DynPro
DynPro
System integrator partner
Technology partner
Schneider Electric
Schneider Electric
Technology partner
Venture capital

Backed by investors who bet on the model.

Slow Ventures
Investor
Founder Collective
Investor
CIV
Investor
07 / engagement

Fixed fee per project. No surprises.

We bridge your as-is to your future state in steps. Each step has a defined scope, deliverable and price.

step 01

As-is and future state

  • — Agents map your current systems
  • — Target architecture agreed
  • — Gaps and priorities ranked
step_01.log
› sources: 6
› tables: 412
› target: lakehouse

A fixed fee, scoped to your project.

Each project is priced as a fixed fee, agreed before work starts. Where a platform fee applies, it is set per project and stated up front. No time and material, no hidden costs.

commercials.yaml
platform_fee: per_project
annual_subscription: none
saas_licence: none
time_and_material: false
pricing: fixed_fee_per_project
hidden_costs: 0
08 / Start

Transformation starts here.

Pick one system. Our agents map it, document it and build your data dictionary in weeks.

Start with one system →turgon.ai