Understand what you
inherited & built.
Salesforce orgs evolve for years, across changing teams, vendors and decisions. MetaLens360 analyses your metadata, code, dependencies, data and analytics, then explains what you inherited and built, how it all fits together, and where the risk sits — with the evidence behind every insight.
MEASURED — every figure above was read from a live connected Salesforce org. Nothing on this page is an illustrative number, and where a value is unknown we say so rather than round it to zero.
Seven questions your org
cannot answer about itself.
Deployment tools move metadata between environments. None of them tell you what the metadata means, what it touches, or what happens if you change it.
MetaLens360 answers these — and tells you which answer it measured, which it derived, and which it could not establish at all.
Every answer carries
its evidence.
An intelligence tool that cannot distinguish measurement from guesswork is a tool you have to double-check — which means it saved you nothing. MetaLens360 grades every claim it makes.
Measured directly
Read from the org, the repository, or the platform's own APIs. A row count, a last-run timestamp, a declared foreign key.
Computed from what was observed
A dependency found by parsing Apex. A blast radius walked across the graph. Reproducible, and shown with its inputs.
Pattern, not proof
A naming convention that usually means a reference. Labelled as inference so you weigh it accordingly.
Reported, never dropped
A rule that could not run. An asset the integration user cannot read. Absence is stated, because silence reads as safety.
Unknown never becomes zero.
This is the principle the product is built on. When a check cannot run, MetaLens360 says so rather than returning a clean result — because "we found no problems" and "we could not look" lead to opposite decisions, and only one of them is safe to act on.
Nine sources.
One connected picture.
Intelligence comes from the joins, not the inventory. A dependency matters because it crosses a boundary; a change matters because of what sits downstream of it.
Connected
Produced
Fourteen capabilities,
one connected model.
Grouped by the question each one answers rather than by how the product is built. Every one reads the same graph, so an answer in one is consistent with an answer in another.
It explains your org,
not Salesforce in general.
Generic Salesforce advice is free and worthless. MetaLens360 AI reads the assets, relationships and lineage discovered in your org — and states what it could not see.
sources: Opportunity (sObject) · Account (sObject) · FX_Rates (csv)
joins: 3 · computed fields: 11 · output: Account_Revenue_DS
consumed_by: 4 dashboards · 1 downstream recipe
last_run: 2026-08-21 02:14 · status: success · rows: 1,284,902
unreadable: 0
Account_Revenue_Recipe converts opportunity revenue into account-level totals, normalising currency through an uploaded FX table rather than the org's own conversion rates — so its figures will diverge from standard reports whenever the uploaded rates are stale.
It is load-bearing: four dashboards and one downstream recipe read its output. The FX source is the weak link — a manual upload with no refresh schedule.
What it writes
Functional and technical summaries, architecture and dependency explanations, impact and root-cause analysis, technical-debt findings, CRM Analytics summaries, and recommendations drawn only from assets that exist.
Your credentials, your spend
Bring your own provider keys — eleven supported, with automatic failover. Choose whether a workspace shares one enterprise credential or requires each person to bring their own. Usage is metered per person either way.
How the AI layer works →It declines to guess
When the evidence is thin, the answer says so. A summary generated from a partial retrieve is labelled as such rather than presented with the same confidence as a complete one.
The part nobody else reads.
Wave metadata is opaque, deeply nested, and usually undocumented. MetaLens360 parses recipes, dataflows, datasets, dashboards and lenses into a lineage you can follow end to end.
| Source | Kind | Transform | Target dataset | Consumed by | Evidence |
|---|---|---|---|---|---|
| Opportunity | sObject | Account_Revenue_Recipe | Account_Revenue_DS | 4 dashboards | observed |
| FX_Rates.csv | Manual upload | Account_Revenue_Recipe | Account_Revenue_DS | 4 dashboards | observed |
| Case | sObject | Service_Health_Dataflow | Service_Health_DS | 2 dashboards | observed |
| Account_Revenue_DS | Dataset | Exec_Rollup_Recipe | Exec_Summary_DS | 1 dashboard | derived from SAQL |
| Legacy_Pipeline_DS | Dataset | — | — | nothing | observed |
| Territory_Ext | External | not readable | unknown | unknown | access denied |
Lineage you can walk
Move upstream and downstream from any asset. Field-level where the transforms allow it.
KPIs as actually coded
Measures extracted from SAQL and compiled aggregates — what the org computes, not what a data dictionary claims.
Disposition, with a brake
Keep, fix, consolidate, retire or build — and nothing is marked for retirement while something still reads it.
One model, read nine ways.
Built to be handed
to a security review.
Read-only by design
MetaLens360 reads. It does not deploy, modify, or write back to your orgs.
Credentials encrypted at rest
AES-256-GCM with a key held in a managed secret store, never in the database it protects.
Workspace isolation
Every record is scoped to a workspace, and reads are filtered by it — including for administrators.
Roles and permissions
Per-workspace role definitions over a permission model, not a fixed set of tiers.
AI access controls
Decide whether AI runs on shared enterprise credentials or requires each person to bring their own.
Full call auditability
Every AI request, its provider path, and its cost are recorded — and nothing is silently pruned.
Point it at one org and see what comes back.
Connect read-only, let the first sync run, and read the org overview it produces. If it tells you something you did not already know, keep going.
Everything reads
one connected model.
Fourteen capabilities — listed in full below — all read the same graph of your metadata, code, data and change history. That shared foundation is the point: a dependency shown in impact analysis is the same dependency the architecture view drew, so two teams looking at the same org never end up arguing from different numbers.
Four stages, each inspectable.
Connect
OAuth to each org, read-only. Optionally connect the repositories that hold your source of truth.
Retrieve
Scheduled or on demand. Every run records what was requested, what arrived, and what failed — so a partial retrieve is never mistaken for a complete one.
Resolve
Parse code and configuration into a dependency graph, each edge labelled with how it was established.
Explain
Rules, comparisons and AI read the resolved model — never the raw dump — so every output traces back to a source.
The operational surface.
Sync & schedule center
Durable schedules, concurrency limits, per-org history, and the reason any run fell short.
Diagnostics console
Live logs, operation timelines, and AI-assisted log analysis for when a sync misbehaves.
Data model browser
The platform's own schema, read live — every table, column and relationship, labelled enforced or by convention.
Wave metadata,
made legible.
CRM Analytics is where Salesforce estates go undocumented. Recipes nest transformations several layers deep, dashboards bind to datasets by name, and the lineage exists only in the head of whoever built it — if they are still there.
What it reads
Recipes, dataflows, datasets, dashboards, lenses, templates and XMD — parsed from the real payloads rather than the documented schema, because the two differ.
What it produces
A dependency graph across the whole estate, field-level lineage where transforms permit, a KPI dictionary read from SAQL, health findings, and a disposition per asset.
From inventory to decision.
Architecture
The estate as a real dependency graph, not a component count.
Sources & targets
Every input traced to every output it reaches.
Recipes
Parsed transformations, joins, and computed fields — with the source visible.
Fields
Field-level lineage across three confidence tiers.
KPIs
Measures as actually coded in SAQL and compiled aggregates.
Lineage
Walk upstream and downstream from any asset, with history.
Health
Rules with earned severity — and the checks that could not run, listed.
Disposition
Keep, fix, consolidate, retire, build — gated on what is load-bearing.
Assessment
A generated implementation review grounded in the discovered estate.
Metadata in. Explanation out.
The same recipe, shown as the platform sees it and as it reads back to a human.
sources: Opportunity, Account, FX_Rates.csv
output: Account_Revenue_DS (1,284,902 rows)
downstream: 4 dashboards, Exec_Rollup_Recipe
schedule: daily 02:00 · last 30 runs: 30 success
Functional. Produces account-level revenue for executive reporting, converting opportunity amounts to a single reporting currency before rolling them up.
Technical. Fourteen nodes, three joins, eleven computed fields. Currency conversion uses an uploaded rate table rather than the org's conversion rates, which is the single largest source of divergence from standard reports.
Risk. The FX table is a manual upload with no discoverable refresh schedule. Four dashboards depend on this output, so a stale upload propagates silently.
Grounded, graded, and paid for
by whoever asked.
MetaLens360 AI is not a chat window bolted to a product. It reads the resolved model — the same graph every other surface reads — and writes explanations that cite what they were built from.
It reads your org, not the internet
Every prompt is assembled from assets discovered in your environment: component bodies, dependency edges, run history, row counts, drift results. No generic Salesforce advice.
It states its own limits
An explanation built from a partial retrieve says so. An asset the integration user could not read is named as unreadable rather than omitted from the picture.
Bring your own keys.
Eleven providers
Configure any subset. The chain tries them in your order and fails over automatically.
Adaptive routing
Ranking adjusts on live evidence — rate-limit windows, recent failure rates, latency — and every routing decision is explained.
Full call traces
Prompt, response, provider path, and cost for every call. Nothing pruned.
Shared credentials
Everyone in the workspace generates on the enterprise key. Nobody configures anything. Usage is still recorded per person, so spend stays attributable.
Personal credentials
Generating requires a key the individual owns. Nothing is inherited from an administrator or a colleague, so every token traces to whoever spent it.
Nine kinds of generated intelligence.
Functional summaries
What the org does, in business language, from what it contains.
Technical summaries
How it is built, where the complexity sits, and what that costs.
Architecture explanations
Layers, clusters, and where coupling concentrates.
Dependency explanations
Why this component touches that one, and through what.
Impact analysis
What a proposed change reaches, and how confidently.
Root-cause analysis
Applied to debug logs, failed runs, and drift.
Technical-debt insights
Dead code, duplicated logic, and automation nobody owns.
CRMA implementation summaries
What the analytics estate actually computes.
Recommendations
Drawn only from assets that exist — never a generic best-practice list.
One model,
nine different jobs.
Each role arrives with a different question. They resolve against one graph, which is why the answers do not contradict each other across teams.
Where it earns its place.
Inheriting an org nobody documented
The original team has moved on. Start from a generated functional and technical summary, then walk the dependency graph outward from whatever you were asked to change.
Planning a migration or consolidation
Establish what actually exists across environments, what is genuinely used, and what is safe to leave behind — with the evidence attached.
Reducing release risk
See what drifted between environments and what sits downstream of each change before it ships.
Cleaning up technical debt
Find dead code, orphaned automation and unread datasets — with a brake that refuses to mark anything load-bearing for removal.
Standing up a Center of Excellence
Compare orgs against each other, track drift over time, and give every team the same picture.
Scoping a consulting engagement
Produce an evidence-based assessment of an unfamiliar org in hours rather than weeks.
What MetaLens360 does
with your data.
Written to be read by a security reviewer, not a buyer.
Scoped to your estate,
not to seats alone.
What drives cost is the size of what you connect — orgs, repositories, and how much metadata they hold — rather than how many people read the results.
Connect one org
Run a full sync, read the generated overview, and walk the dependency graph. Enough to judge whether it tells you something you did not know.
StartMultiple orgs, scheduled
Continuous sync across environments, drift tracking, repository intelligence, and the full AI surface on your own provider keys.
Talk to usMulti-workspace
Workspace isolation, SSO, per-workspace roles, per-user AI credential enforcement, and cross-org administration.
Talk to usFour steps to
the first answer.
Nothing is deployed and nothing is modified. The connection is read-only throughout.
Connect an org
Browser OAuth, or an SFDX auth URL. A refresh token is required — pasted access tokens expire within hours.
Run the first sync
Retrieval and indexing. The run reports what arrived and what did not.
Add your AI key
Any of eleven providers. Optional — the platform is fully usable without AI.
Read the overview
Start with the generated org summary, then follow the dependency graph outward.
Talk to us
Tell us how many orgs you run and what you are trying to find out, and we will scope from there. Evaluations start with a single read-only connection.
hello@metalens360.io