The RelationalAI alternative with proved rules, on any SQL database
SynaLemma answers questions on your business data with rules that are proved safe and terminating before they run, then compiled to SQL on the databases you already run.
SynaLemma vs RelationalAI: key differences
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Every rule proved before it runs
A symbolic verifier proves each rule well-formed, safe and terminating. An invalid rule is rejected with the reason, before any SQL runs.
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Assertions that name the faulty rows
Keys, references, totals or transitivity are written in first-order logic. The engine searches the whole table and returns the rows that break them.
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Any SQL database, not one warehouse
Rules, recursive ones included, compile to SQL on PostgreSQL, BigQuery, Databricks, Trino, Presto, DuckDB and SQLite, with no engine to install next to the database.
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The agent never writes SQL
The agent writes rules, not SQL. The engine checks every rule, then writes SQL that only reads your tables: no raw SQL and no injection, formally proved.
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Same question, same SQL
The same question always compiles to the same SQL, and every figure cites the rule it came from. Your team can read, correct or lock that rule.
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Sovereign by construction, not by contract
Data, rules and model stay on your infrastructure. The engine compiles the SQL, so a small open model is enough, with no per-token fees. The Synalog engine is open source, under Apache-2.0.
Example: an assertion that catches a missing customer
Revenue per customer, with three assertions. Customer 12 is missing from the customers table; the engine names the row.
Order(order_id:, customer_id:, amount:, status:) :- orders(order_id:, customer_id:, amount:, status:);
Customer(customer_id:, country:) :- customers(customer_id:, country:);
@Assert(Revenue,
one_row_per_customer: "∀ c r s, Revenue c r → Revenue c s → r = s",
known_customer: "∀ c r, Revenue c r → ∃ k, Customer c k",
positive: "∀ c, Revenue c > 0");
Revenue(customer_id:, revenue? += amount) distinct :-
Order(customer_id:, amount:, status: "delivered");SynaLemma vs RelationalAI at a glance
| Criterion | SynaLemma | RelationalAI |
|---|---|---|
| How an agent answers | It writes a rule; the engine proves it and compiles the SQL | Curated PyRel queries (Snowflake Intelligence) or PyRel written by a coding agent |
| What checks the definitions | Every rule proved well-formed, safe and terminating before it runs | Type checks at compile time; no proof that rules are safe or terminate |
| Checks on the data | Assertions in first-order logic that return the rows breaking them | Requirements and constraints; failing rows not documented |
| Hierarchies and multi-hop chains | Native recursion at any depth, verified to terminate | Recursive rules and graph algorithms such as reachability |
| Definitions written in | Synalog, an open-source logic language with a compiler | Python, with the PyRel library |
| Where it runs | SQL on PostgreSQL, BigQuery, Databricks, Trino, Presto, DuckDB, SQLite | A native app in Snowflake; rules and queries also run on local DuckDB |
| Language model | The one you choose: a small open model is enough, no per-token fees | Cortex for Snowflake Intelligence; your coding agent through skills |
| Deployment | On-premises, on your infrastructure | Snowflake on AWS or Azure for production |
Why teams look for a RelationalAI alternative
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Type checks, no termination proof
PyRel checks types at compile time (compilation); no check that a rule is safe or terminates is documented, and the earlier Rel documentation warns about recursion that never converges (Rel recursion).
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No counterexamples
A failed requirement makes the query fail with an error; returning the rows that break it is not documented (require).
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Built for Snowflake
The documented production setup is a native app inside a Snowflake account; the graph, optimization and machine-learning reasoners need a Snowflake connection (set up).
RelationalAI alternative: FAQ
What is the best RelationalAI alternative for AI agents?
SynaLemma. Every rule is proved safe and terminating before it runs, assertions name the faulty rows, and rules compile to SQL on the databases you already run, not only Snowflake.
Does RelationalAI run outside Snowflake?
Its documented production setup is a native app inside a Snowflake account; rules and queries can also run on a local DuckDB database. Synalog compiles to SQL for PostgreSQL, BigQuery, Databricks, Trino, Presto, DuckDB and SQLite.
What does formal verification add to a rule engine?
It proves each rule is well-formed, safe and terminating before it runs, so an agent cannot add a rule that loops or returns unbounded results.
Can SynaLemma run with a local model?
Yes. A small open model on your own hardware is enough, because the engine compiles the SQL.
Sources
- RelationalAI docs: home
- RelationalAI docs: rules-based reasoning
- RelationalAI docs: compilation
- RelationalAI docs: require
- RelationalAI docs: graph reasoning
- RelationalAI docs: set up in Snowflake
- RelationalAI docs: templates
- Rel docs (previous version): recursion
- Synalog on GitHub (Apache-2.0)
Checked against each product's public documentation on the review date. To report an error: contact@synalinks.com.
Other comparisons
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