Gravitas Foundry - accelerate trading platform delivery, modernization and upgrades
Gravitas Foundry is a metadata-driven product engineering platform that helps energy and commodity trading organizations implement, modernize, extend and validate ETRM and CTRM platforms faster - starting from a canonical product foundation instead of an empty sandbox. Business capabilities, features, rules, API contracts, datasets, test scenarios and documentation all originate from one governed metadata repository, so a change is configured rather than hand-coded, its impact is known before deployment, and the assets that prove it works are generated rather than assembled by hand.
Metadata-driven - Canonical product foundation - Upgrade-safe - Governed - AI-assisted
Gravitas Foundry in four points
- A canonical product foundation with production-ready business capabilities, not an empty implementation
- Metadata-driven configuration in place of hardcoded business logic
- A business capability and feature repository with full traceability to tests, APIs and documentation
- Upgrade engineering: know which capabilities, APIs, tests and reports a change affects before you deploy
- Canonical regression packs, datasets and automation generated from the same metadata
- Migration engineering that maps an existing platform to a canonical business model
Why we built it
Commodity trading platforms should be able to change at the speed the market changes. Foundry exists so that adding a commodity, a product or a regulatory requirement is a configuration and a governed release, not another multi-year programme.
What it delivers
To give trading organizations an engineering foundation - canonical product, metadata, capabilities, tests, automation and governance in one place - so implementation, upgrade and modernization work is faster, safer and fully traceable.
Trading platforms have become too expensive to change
Years of accumulated customizations mean nobody can say with confidence what a change will break, so every release carries risk that has to be bought down with manual effort.
Business knowledge is scattered across spreadsheets, tribal memory and documents that went stale the moment the last release shipped, and much of it is locked inside implementation partners rather than the organization that paid for it.
Regression testing is planned and executed by hand before every release, so the cost of proving nothing broke often exceeds the cost of the change itself.
Onboarding a new commodity or product is slow, impact analysis is guesswork, and the result is predictable: every enhancement becomes a project, and every upgrade becomes a major programme.
Build on a canonical foundation
Customers never start with an empty sandbox. Foundry ships a canonical product: reference data, trade capture, lifecycle, position, valuation, market and credit risk, scheduling, inventory, settlement, accounting, reporting, administration, security and workflow, as production-ready business capabilities you extend rather than invent.
Everything is metadata. Screens, forms, validation, business rules, menus, navigation, workflow, API contracts and reports are configured in a governed repository rather than hardcoded, which is what makes extensions upgrade-safe.
Every business capability is versioned and linked to its features, test cases, automation, documentation, APIs and datasets, so traceability from business requirement to running code and passing evidence is a property of the system, not a document somebody maintains.
Because the dependency graph is explicit, a proposed change tells you which features, APIs, tests, SQL, reports, screens, workflows and integrations it touches - before you deploy, not after.
Why this exists now
Commodity trading has never had to change faster. The energy transition is adding desks, products and structures that did not exist a few years ago; regulation keeps adding reporting obligations and the evidence to back them; and volatility makes a stale position or a late risk number genuinely expensive rather than merely embarrassing.
Meanwhile the platforms that actually run trading have accumulated a decade or more of customization. Each individual change was reasonable at the time. Collectively they have produced systems where the cost of changing anything safely has grown faster than the value of almost any single change - which is why so many enhancement requests quietly become projects and so many upgrades become programmes.
Foundry is a direct response to that gap. If change is expensive because impact is unknowable, make impact knowable. If documentation is stale because it is maintained by hand, generate it. If regression is unaffordable because it is planned from scratch each time, make it canonical and select it by impact. If customizations break on upgrade, express them as metadata against a canonical model instead of as code forks.
The principles behind it
Start from a canonical product
Never an empty sandbox. The industry has built these capabilities many times; you should not pay to build them again.
Configure, do not hardcode
Business logic belongs in governed metadata, not in code forks that break on the next version.
Trace everything
Every capability links to its features, tests, APIs, datasets and evidence, by construction rather than by discipline.
Know impact before deployment
A change should report what it affects while you can still act on it.
Generate, do not maintain
Documentation and automation produced from metadata cannot drift away from the system they describe.
Extensions must survive upgrades
If a customization cannot cross a version boundary, it is a liability rather than an asset.
The engineering pillars
Foundry is organised into six engineering pillars. Each is useful on its own; together they cover the full lifecycle of a trading platform, from first implementation to continuous upgrade.
The six pillars are not phases you must complete in order. Most organizations start where the pain is - an upgrade that has to be de-risked, a regression suite that has become unaffordable, a modernization that keeps slipping.
Because all six draw on the same canonical model and metadata repository, starting with one makes the others cheaper. The test repository knows about the capabilities; the impact analysis knows about the tests; the documentation knows about all of it.
Product Engineering
Customers never start with an empty sandbox. Foundry ships a canonical product covering reference data, trade capture, trade lifecycle, position, valuation, market risk, credit risk, scheduling, inventory, settlement, accounting, reporting, administration, security and workflow. These arrive as production-ready business capabilities you configure and extend rather than specify, build and test from nothing, which removes the first and most expensive phase of a traditional implementation.
Underneath sits a canonical data model with the layering a serious estate needs: raw, conformed and business-ready views, with versioning, metadata, lineage and explicit extension points. Every business entity - asset class, commodity group, commodity, product, instrument type, instrument, location, counterparty, portfolio, book, trader, currency, unit of measure, calendars, quality and benchmark - is modelled once and reused everywhere, with the full set of lifecycle capabilities: create, modify, activate, deactivate, archive, restore, search, import, export, each governed and exposed through an API.
Every capability is versioned and linked to its features, test cases, automation, documentation, APIs and datasets, and the whole set is browsable through a searchable feature explorer. The hierarchy is explicit - module, business entity, capability, feature, test cases, automation, evidence - so a question like 'what does trade capture for crude oil physical forwards actually cover, and how do we know it works' has an answer in the system rather than in somebody's memory. The organization ends up owning a described platform, not a folder of documents that went stale at go-live.
- Canonical product foundation
- Versioned business capabilities
- Searchable feature repository
Because starting from zero is the single largest avoidable cost in trading-platform delivery.
Quality Engineering
Regression is where trading programmes quietly lose their budget: planned by hand, executed by hand, and re-derived from scratch every release. Foundry holds a canonical test repository tied directly to the capabilities each test validates, so coverage is a fact you can query rather than a number somebody estimates in a status report.
The dataset repository underneath it carries reusable business datasets, representative production data, anonymized production extracts and synthetic generation, all versioned and shareable, which removes the other perennial excuse for slow testing: nobody could get realistic data. Automation spans the layers that matter - browser automation for screens, REST for contracts, SQL for state, JSON assertions for payloads, plus performance, security, smoke and full regression - and every run leaves execution evidence attached to the capability it proves.
Because the dependency graph is explicit, tests can be selected by impact rather than run exhaustively. That is what turns regression from a fixed tax on every release into a variable cost proportional to what actually changed, and it is usually the first place a programme sees Foundry pay for itself.
- Canonical regression packs
- Reusable datasets
- Impact-based testing
Because proving nothing broke often costs more than the change itself.
Migration Engineering
Modernization stalls when nobody can describe the legacy system precisely enough to leave it safely. The knowledge is distributed across customizations written years ago, spreadsheets maintained by three people, and partners who have moved on. Migration engineering attacks that directly by making description a machine task rather than an archaeology project.
The workflow is deliberate and governed: discover what the legacy platform contains, extract it, map it to the canonical business model, transform, validate, load, reconcile against the source, and certify the result. Each step produces evidence, so the migration can be defended to an auditor rather than merely asserted to a steering committee.
Once that canonical mapping exists it keeps paying: modernization can proceed capability by capability with reconciliation at each step instead of as a big-bang cutover nobody can de-risk, and the same map is what makes subsequent upgrade impact analysis possible. Describing the estate once unlocks every later change.
- Legacy metadata discovery
- Canonical mapping
- Reconciled, certified cutover
Because you cannot safely leave a system you cannot describe.
Upgrade Engineering
Every upgrade begins with the same question - what will this break? - and normally that question has no reliable answer, so the programme buys certainty the only way it can: months of manual regression and a long stabilisation period after go-live. The cost is not the upgrade; the cost is the uncertainty.
Foundry answers it structurally. The current platform's metadata is discovered and mapped to the canonical model, versions are compared against that map, and impact analysis walks the dependency graph to return exactly what is affected: business capabilities, features, APIs, tests, SQL, reports, screens, workflows, integrations and documentation. That list exists before deployment, while it is still cheap to act on.
From there the affected assets are regenerated from metadata rather than rewritten, regression is targeted at what actually changed, execution evidence is produced automatically, and the release is certified through a governed sign-off. An upgrade stops being a programme with an unknown scope and becomes a release with a known one.
- Version comparison
- Impact analysis
- Production certification
Because an upgrade whose scope is unknown at the start is a programme, not a release.
Governance
Trading platforms are audited, which means evidence cannot be produced retrospectively by whoever is free that week. Foundry treats governance as a property of the repository rather than a process laid on top of it: every artifact it holds is versioned, and every change moves through maker-checker approval with a full audit trail.
Change history, dependency tracking and electronic sign-off apply uniformly across capabilities, features, rules, contracts, datasets, tests and documentation, so the same discipline covers the business definition and the code that implements it. Approvals are role-based and escalation is configurable, which matters when the person who understands a change is not the person authorised to release it.
The result is traceability that is structural rather than clerical: from business capability, to feature, to test, to passing evidence, to the approval that let it ship. That chain is what an auditor asks for, and it is generated as a by-product of working rather than assembled in a panic before a review.
- Full audit trail
- Maker-checker approvals
- End-to-end traceability
Because regulators and auditors ask for evidence, not intentions.
AI Engineering
Generation is only as good as its grounding. A model asked to write test cases from a blank prompt produces plausible fiction; a model working from metadata that already describes the entities, capabilities, rules and contracts of a real trading platform produces assets a team can actually use. Foundry's metadata repository is exactly that grounding.
From it, Foundry generates features, test cases, automation, documentation, SQL assertions, API payloads, migration mappings and regression packs. Because the source is the same metadata that runs the system, generated assets stay consistent with reality instead of drifting away from it the moment the next change lands.
It also uses AI defensively rather than only generatively: duplicate detection surfaces where the estate has described the same capability three times under different names, and coverage analysis shows where a capability carries no meaningful test. Those two findings are usually more valuable than any amount of new generated content, because they tell you where the real risk is hiding.
- Generated engineering assets
- Duplicate detection
- Coverage analysis
Because generation grounded in real metadata is useful; generation from nothing is a demo.
Key differentiators
The difference is where you start and what you can prove. Traditional implementation begins with an empty system and accumulates custom code that nobody can safely change. Foundry begins with a canonical product and keeps every change in governed metadata, so the system can tell you what a change affects and generate the evidence that it works.
Canonical foundation, not an empty implementation
Traditional delivery begins with a blank system and rebuilds capabilities the industry has built a thousand times. Foundry starts from a production-ready canonical product.
Metadata-driven configuration, not custom development
Behaviour is defined as governed, versioned metadata rather than hardcoded logic, which is what makes an extension survive the next upgrade.
Generated documentation, not hand-maintained documents
Specifications, API docs, test docs and release notes are produced from the same metadata that runs the system, so they cannot silently go stale.
A canonical regression repository, not manual planning
Test scenarios, datasets and automation live with the capabilities they validate and are selected by impact rather than re-derived each release.
Dependency analysis, not guesswork
The metadata encodes an explicit dependency graph, so a proposed change reports what it affects before deployment rather than after.
Upgrade-aware engineering, not upgrade archaeology
Version comparison against the canonical map turns an upgrade from an investigation into a planned, evidenced release.
What changes for the business
Indicative shifts our clients target when they adopt Gravitas Foundry. Actual results depend on scope, data quality and starting maturity.
The outcomes Foundry targets are the ones that make trading platforms expensive: implementations that start from zero, upgrades whose scope is unknown until late, regression that must be planned by hand, customizations that break on the next version, and knowledge that lives in documents and people rather than the system.
Each is addressed structurally rather than with effort: a canonical foundation, an explicit dependency graph, generated assets, metadata-driven extensions, and one governed repository of record.
The honest measures are the ones a programme already tracks, not new ones invented to flatter a tool: how long a change takes from business requirement to production, what proportion of each release is regression effort, how often an upgrade surfaces something nobody predicted, how much of the estate is described in the system rather than in somebody's head, and how much of the evidence pack is generated rather than assembled.
Those are the numbers Foundry is built to move, and the architecture workshop is where we agree the baseline against your estate rather than assert an improvement against someone else's. A claim you cannot reproduce on your own platform is not worth making, so we would rather measure it with you than quote it at you.
Where teams use it
- Implement a new ETRM platform from a canonical foundation rather than an empty sandbox
- Plan and execute an Openlink Endur upgrade with impact analysis before deployment
- Modernize a legacy CTRM onto a canonical business model
- Standardize reference data across desks, regions and systems
- Reduce regression testing effort with canonical scenarios and generated automation
- Accelerate a cloud migration with governed metadata mapping
- Improve implementation governance and traceability for audit
- Create reusable enterprise engineering assets instead of per-project throwaway work
- Onboard a new commodity or product as configuration rather than a development project
Upgrade a trading platform
Discover metadata, map to canonical, compare versions, analyse impact, regenerate affected assets, run targeted regression, certify with evidence.
Modernize a legacy CTRM
Map the existing platform onto a canonical business model, then move capability by capability with reconciliation and certification at each step.
Implement a new platform
Start from the canonical product and configure to the business, rather than building the same capabilities from zero for the hundredth time in the industry.
Cut regression cost
Adopt canonical scenarios and generated automation, and select tests by impact rather than running everything before every release.
Who it is for
- CIOs and CTOs accountable for the total cost and risk of the trading estate, who need a defensible answer to why change costs what it does
- Enterprise architects designing a target platform and needing one canonical model the whole estate can be mapped against
- Solution architects who would rather configure and generate contracts than write bespoke code for every requirement
- ETRM product owners under pressure to deliver new commodities, products and regulatory capability predictably
- QA and test managers carrying a regression burden that grows with every release and never shrinks
- Delivery and engineering managers running implementation, upgrade or modernization programmes with scope they cannot see
- Implementation teams working on Openlink Endur, Allegro, RightAngle, TriplePoint and similar platforms who need to know what a change touches
What each team gets
CIO / CTO
A defensible answer to why the trading estate costs what it costs, and a credible path to changing it.
Enterprise architect
One canonical model the whole estate can be mapped against and reasoned about.
Solution architect
Configuration and generated contracts instead of bespoke code for every requirement.
ETRM product owner
New capability delivered as configuration, with its impact known before it ships.
QA manager
Canonical regression packs, generated automation and impact-based test selection instead of running everything.
Delivery manager
Scope known at the start, evidence generated rather than assembled, sign-off governed and auditable.
A worked example
A trading organization plans an upgrade of its incumbent platform. Traditionally the first question - what will this break? - has no reliable answer, because a decade of customizations is described only in the code itself and in the memories of people who may no longer be there. So the programme does the only thing it can: it buys certainty with months of manual regression and a long, expensive stabilisation window after go-live.
With Foundry the sequence is different. The current platform is connected and its metadata discovered automatically, then mapped onto the canonical business model. That mapping is the asset everything else depends on, and it is produced by the system rather than by an archaeology exercise.
A version comparison then runs against that map, and impact analysis walks the dependency graph. It returns the affected business capabilities, features, APIs, tests, SQL, reports, screens, workflows and documentation - before anything is deployed, while the information is still cheap to act on. The affected assets are regenerated from metadata rather than hand-rewritten, and regression is targeted precisely at what changed instead of running the whole suite on the assumption that everything might have.
The result is an upgrade whose scope is known at the start rather than discovered at the end, whose evidence is generated rather than assembled, and whose sign-off is a governed workflow with a complete audit trail. The same canonical map then makes the next upgrade cheaper still, which is the compounding effect the whole platform is built to produce.
How it fits your existing estate
Foundry is deliberately non-invasive. It connects to the trading platform you already run rather than asking you to replace it, reading metadata through databases and APIs and mapping what it finds onto a canonical business model.
From there it integrates with the tools your teams already use: source control and CI/CD for the assets it generates, issue tracking for the work it identifies, and your data platform for the datasets and evidence it produces.
What is inside
- Canonical product with production-ready business capabilities
- Metadata repository - no-code configuration of screens, forms, validation and navigation
- Business capability repository, versioned and traceable
- Feature repository with a searchable explorer
- Reference data management across the full commodity hierarchy
- Workflow engine with maker-checker, approvals and state machines
- Business rules engine - validation, eligibility, cross-field and calculation rules
- API contracts with OpenAPI support, generated per feature
- Dataset repository - representative, anonymized and synthetic data
- Automation repository spanning UI, REST, SQL and performance
- Impact analysis and dependency graphs
- AI-assisted generation of documentation, tests and automation
Canonical product
Reference data, trade capture, lifecycle, position, valuation, market and credit risk, scheduling, inventory, settlement, accounting, reporting, administration, security and workflow, shipped as production-ready business capabilities you extend rather than build.
Metadata repository
Screens, forms, validation, business rules, menus, navigation, workflow, API contracts and reports, all configured rather than coded, all versioned, all governed.
Capability and feature repositories
Every business capability is versioned and linked to its features, test cases, automation, documentation, APIs and datasets, with a searchable explorer over the whole set.
Quality engineering
A canonical test repository with datasets, generated automation, execution, coverage and evidence, plus regression packs and impact-based test selection.
Impact analysis
A dependency graph that answers, for any proposed change, which features, APIs, tests, SQL, reports, documentation, screens, workflows and integrations are affected.
AI engineering
Generation of features, test cases, automation, documentation, SQL assertions, API payloads, migration mappings and regression packs, with duplicate detection and coverage analysis.
How you run it
What it connects to
How it fits your technology estate
Foundry is deliberately non-invasive. It works with the trading platforms already in place - Openlink Endur, Allegro, RightAngle, TriplePoint, AspectCTRM, SAP IS-Oil and custom in-house systems - connecting through their databases and APIs to discover metadata rather than asking anyone to rip out a working system to find out what it does.
It reads and feeds the data platforms you already run, including Snowflake, Databricks, Oracle, PostgreSQL, SQL Server and ClickHouse, so datasets, evidence and generated artifacts land where your analytics and reporting already live rather than in another silo.
And it plugs into the engineering toolchain your teams work in every day - GitHub, GitLab, Azure DevOps, Jenkins and Jira - so generated automation, contracts and documentation flow into source control and the work it identifies lands in the backlog, instead of arriving as another portal somebody has to remember to check.
Enterprise controls
Where it is going
Extending the canonical model across additional commodity desks and structured products.
Expanding AI-assisted generation of migration mappings and regression packs from discovered metadata.
Widening coverage across trading platforms, data platforms and engineering toolchains.
How we engage
Delivery is a loop, not a line: connect, discover metadata, map to the canonical model, configure and extend, generate documentation and automation, validate, deploy, and repeat.
Each pass through the loop leaves the estate better described than it was, which is what makes the next change cheaper than the last.
Connect and discover
Foundry connects to the platform you already run and discovers its metadata through databases and APIs, without asking you to replace anything.
Map, configure and generate
What exists is mapped to the canonical business model; from there you configure and extend in metadata, and documentation, contracts and automation are generated from it.
Validate, deploy, repeat
Impact-targeted regression runs, evidence is produced, sign-off is governed, and the loop repeats - each pass leaving the estate better described than before.
Tangible deliverables
- A canonical product foundation covering the core trading modules
- A governed metadata repository for screens, rules, workflow, APIs and reports
- A versioned business capability and feature repository
- Canonical test scenarios and regression packs
- Generated automation and execution evidence
- Generated functional, technical, API and test documentation
- Impact analysis and dependency graphs across the estate
- Migration mappings from the legacy platform to the canonical model
Canonical mapping
Your platform expressed against a canonical business model, which is what makes impact analysis and migration possible.
Impact analysis
The dependency graph and, for any change, the full set of affected features, APIs, tests, reports and documentation.
Generated assets
Documentation, automation, contracts and regression packs produced from metadata rather than by hand.
Evidence and sign-off
Execution evidence, coverage and a governed approval trail suitable for audit.
Engagement-based and transparent
Foundry is licensed as an enterprise platform, typically alongside a defined programme - an implementation, an upgrade, or a modernization - and then retained as the engineering foundation for continuous change.
Engagements usually begin with an architecture workshop to size the estate honestly and agree the first pillar to stand up.
Who is behind Gravitas Foundry
Foundry is built by practitioners who have delivered and upgraded trading platforms rather than only advised on them.
That shows up in what the product refuses to do: it does not assume a greenfield, it does not pretend documentation stays current by discipline, and it does not treat regression as somebody else's problem.
- Built by people who have run ETRM implementations and upgrades
- Canonical model drawn from real trading estates, not a whiteboard
- Designed to work with incumbent platforms, not against them
- Governance and evidence treated as first-class, because audits are
Frequently asked questions
What is Gravitas Foundry?
A metadata-driven product engineering platform for commodity trading systems. It provides a canonical product foundation plus governed repositories for business capabilities, features, rules, API contracts, datasets, tests, automation and documentation, so implementation, upgrade and modernization work is configured and traceable rather than hand-built.
How is it different from an ETRM?
An ETRM runs your trading business. Foundry is the engineering platform you use to build, extend, validate, modernize and upgrade a trading platform. It sits alongside the ETRM rather than trading in it.
Does it replace Openlink Endur?
No. Foundry is designed to work with the platform you already run, including Endur. It connects, discovers metadata, maps it to a canonical business model, and gives you impact analysis, generated documentation and regression assets around it.
Can it work with our existing trading platform?
Yes. It is built to connect to existing platforms - Openlink Endur, Allegro, RightAngle, TriplePoint, AspectCTRM, SAP IS-Oil and custom systems - through their databases and APIs.
How does metadata-driven configuration work?
Screens, forms, validation, business rules, menus, navigation, workflow, API contracts and reports are defined as governed metadata rather than code. Changing behaviour means changing a versioned, approved configuration, and everything downstream - documentation, tests, contracts - regenerates from the same source.
Can we preserve our customizations?
That is the point of expressing extensions as metadata. Because a customization is configuration against a canonical model rather than a code fork, it can be carried across versions and its upgrade impact can be analysed rather than discovered at runtime.
Can we generate automation?
Yes. The automation repository generates and holds UI, REST, SQL and performance automation tied to the features and capabilities it validates, with execution evidence and coverage.
Can AI generate documentation?
Yes. Because the metadata already describes the system, functional and technical specifications, API documentation, test documentation, release notes and user guides can be generated from it rather than written and then allowed to go stale.
Can we migrate from a legacy platform?
Yes. Migration engineering runs a governed workflow - discover, extract, map, transform, validate, load, reconcile, certify - from the legacy system onto the canonical business model.
Can we connect existing APIs?
Yes. Foundry is API-first: it consumes existing REST APIs and publishes OpenAPI contracts for every feature it holds.
How are upgrades managed?
Upgrade engineering compares versions, analyses impact across the dependency graph, identifies affected features, APIs, tests, reports and documentation, regenerates the affected assets, runs regression and produces the evidence to certify the release.
Does it support multi-tenancy?
Yes, with strict tenant isolation, so an enterprise can run multiple business units or programmes on one instance without leakage.
Does it support governance workflows?
Yes: versioning, approvals, maker-checker, audit, change history, dependency tracking and electronic sign-off across every artifact.
How does Foundry reduce implementation cost?
By removing work rather than compressing it. You start from a canonical product instead of an empty sandbox, configure instead of writing custom code, generate documentation and automation instead of producing them by hand, and target regression by impact instead of running everything.
How long does onboarding take?
It depends on the size of the estate and the goal - a single upgrade programme is a different scope from an enterprise modernization. The first step is a short architecture workshop to size it honestly against your platform and data.
Can we extend the canonical model?
Yes. The canonical model is a starting foundation, not a straitjacket: you extend it with your own entities, capabilities and rules as metadata, which keeps those extensions governed and upgrade-aware.
Is it only for oil and gas?
No. The canonical model spans crude oil, refined products, natural gas, LNG, power, coal, metals, carbon, freight, agriculture, renewables and multi-commodity trading.
What does impact analysis actually show?
For a proposed change, the affected features, APIs, tests, SQL, reports, documentation, screens, workflows and integrations, drawn from the dependency graph the metadata already encodes.
Who is it for?
CIOs, CTOs, enterprise and solution architects, ETRM product owners, QA and delivery managers, and the implementation teams working on Endur, Allegro, RightAngle and similar platforms.
How do we start?
With an architecture workshop: we connect Foundry to a representative slice of your platform, discover its metadata, map it to the canonical model, and show you the impact analysis and generated assets against your own system.
How to begin
Book an architecture workshop. We size the estate honestly against your platform and data rather than against a reference architecture, and agree which pillar to stand up first based on where the cost and risk actually sit today.
Connect and discover. Foundry connects to a representative slice of the current platform and discovers its metadata, producing the first canonical mapping without disturbing anything in production.
See it against your own system. You review the canonical mapping, run an impact analysis against a real proposed change, and inspect the generated documentation and automation for your own capabilities before committing further.
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