How do you handle provenance and lineage?

Every answer links each claim back to the file, page, and version it came from, so lineage is inspectable end to end rather than reconstructed after the fact.

What happens when someone queries a table they don’t have row-level access to?

They get an answer built only from what they are entitled to see. Permission-aware retrieval runs at query time and inherits your access model, so scope by team, category, or project holds server-side rather than by convention — the restricted rows never reach the retrieval step.

Do you train on our data?

No. Your data is never used to train models, yours or anyone else’s. Merciv is SOC 2 Type II, and the sourcing methodology and controls behind that are documented for procurement review.

Which systems do you connect to today — live, not roadmap?

Snowflake, Databricks, Looker, and SAP connect directly, alongside the document systems your research already lives in. Merciv reads from them in place and inherits your access model, so there is no migration and nothing has to move somewhere new to be queried.

What’s the tenant isolation model?

Your tenant is a fully isolated instance built on least-privilege access — no commingling between customers, and no reach across teammates who haven’t been granted access. Combined with page-level lineage, you can reconstruct which sources produced an answer rather than taking it on trust.

Can we evaluate accuracy the way we would a model?

Yes. Confidence scores, page-level citations, and code-run lineage make accuracy, coverage, and drift testable, so you can hold Merciv to the same evaluation bar as any system you own.

Merciv for Data & Analytics

Make your data more usable, more securely

Serve every function from one governed layer, enforce the permissions you already set, keep answers anchored to your own definitions, and give the business AI access without a shadow stack.

Request a demo

Knowledge

One governed home for all of your company’s data

Research files, syndicated feeds, and warehouse tables live in one structured corpus with a single set of rules. You can see what’s in it, what’s current, and where two sources hold conflicting answers on the same question.

Permissions

Access enforced automatically, every time

Scope access by team, category, or project, and Merciv holds the boundary every time someone asks a question. Two users asking the same thing get two different answer sets — enforced server-side, not by convention.

Citations & lineage

The audit trail already exists

Every claim traces to its file, page, and version by default. When someone asks how a finding got in front of leadership, it takes a click, not an entire project, to produce the full audit trail.

Connectors

Reason over the systems you already run

Snowflake, Databricks, Looker, and SAP connect directly, so analysis runs against live data instead of a manual export. Nothing has to move somewhere new to be queried.

Retrieval & code execution

Retrieve evidence across your research library

Search by meaning and exact keyword across connected entities. Quantitative questions run as calculations against your data, with the underlying sources attached.

How data teams use Merciv

Give the business answers without losing control of the data
Permission-aware retrieval, so each person sees only what they’re already cleared to see.
Redirect shadow AI into something governed
A sanctioned place to ask, with an audit trail — instead of verbatims pasted into a public chatbot.
Show lineage for any answer
Which source, which document, which retrieval path — exportable when security asks.
Answer the AI security questionnaire once
Model provenance, retention policy, tenant isolation, and subprocessors, documented before procurement asks for them.
Inventory where AI touches consumer data
Classify it and log it, against the EU AI Act’s high-risk obligations.
Take repeat questions off the queue
Governed self-serve for recurring asks, so modeling stops sitting behind ad-hoc requests.
Feed cited inputs to your models
Structured, sourced consumer evidence your segmentation, forecasting, and MMM can actually consume.
Keep the internal build and add the layer
Your assistant keeps the archive. This adds the licensed external signal and the citation contract.

Knowledge systems don’t stay governed on their own. Access outlasts the project it was granted for, research goes stale, and different tools and sources contradict each other. Nobody notices until it’s a problem, and data & analytics teams waste time worrying about it.Merciv keeps your knowledge system unified, current, and governed automatically.

Common questions

How Merciv works with your data

We’ve built an internal RAG assistant. What does yours add?
Keep it. What an internal build can’t hold is the licensed external and live-category layer, permission-aware across every source at query time. Merciv adds that layer and coexists with what your team already runs.
Is there any data migration? I’m not moving anything out of the warehouse.
None. Snowflake, Databricks, Looker, and SAP are read in place, and Merciv inherits your access model rather than standing up a second one. Nothing has to move somewhere new to be queried.
Legal found teams pasting verbatims into ChatGPT. How does this stop that?
By making the governed path the faster one. Shadow AI shows up when the sanctioned route is slower than the unsanctioned one, so the fix is a tool people prefer: permission-aware, cited, and zero training on your data.

Your stack

Additive to the estate you already built

Merciv reads from the warehouse, BI, and research tools you already govern — in place, without copying your data somewhere new. It inherits the access controls you’ve already defined, so adopting it doesn’t mean standing up a parallel system to administer.

Reads where your data lives
Connects to Snowflake, Databricks, Looker, and SAP directly. No new pipeline, no duplicate copy to keep in sync
Inherits your access model
Permissions follow your existing identity provider and team structure — not a second set of rules to maintain by hand
Nothing new to operate
No infrastructure to provision, no migration project, no ongoing admin burden landing on your team

Built for how data teams govern AI

Visit the Trust Center

Every claim cited
Each answer links back to source material the reader is authorized to open
Confidence on every answer
Open the score — agreement, recency, coverage — and see where sources disagreed
Says so when it doesn’t know
Thin evidence is flagged, not filled in with something plausible
Zero training on your data
Your data is never used to train models, yours or anyone’s
Grounded in real data
Synthetic personas are evidence-grounded segments from your licensed data — not invented consumers
SOC 2 Type II
Independently audited controls, cleared for procurement

Request a demo

Redefine the role of data in your organization

Bring a question your analysts already field. We’ll run a working session on your own sources and show the provenance behind every claim.

Read case studies

FAQ

Questions data teams ask

Resources

Make your data more usable, more securely

One governed home for all of your company’s data

Access enforced automatically, every time

The audit trail already exists

Reason over the systems you already run

Retrieve evidence across your research library

How data teams use Merciv

Give the business answers without losing control of the data

Redirect shadow AI into something governed

Show lineage for any answer

Answer the AI security questionnaire once

Inventory where AI touches consumer data

Take repeat questions off the queue

Feed cited inputs to your models

Keep the internal build and add the layer

How Merciv works with your data

Additive to the estate you already built

Reads where your data lives

Inherits your access model

Nothing new to operate

Built for how data teams govern AI

Every claim cited

Confidence on every answer

Says so when it doesn’t know

Zero training on your data

Grounded in real data

SOC 2 Type II

Redefine the role of data in your organization

Questions data teams ask

Further reading for data and analytics teams

Build AI findings leaders can click through

A data-driven strategy leadership trusts

Why Merciv beats ChatGPT and Claude