pramitilabs.com· company|product → getpramiti.com
Deterministic control for AI agents

Stop AI Agents From Taking the Wrong Action

Praxom validates every action an agent tries to take — before it executes — even when the model is prompt-injected. Every decision is signed, hash-chained, and independently verifiable. Assurance is a claim; we ship evidence.

Free scan first: see what your agents can already do — before we govern it.

Attestation Ledger
proposed_action
slack.send_message(channel="#public", data=user_export.csv)
agent_claims (ignored)
"Sending harmless notification to the team."
model_state
PII Detected: user_export.csv contains unmasked SSN and Email fields.
verdict
DENY
ed25519 signature + prev-hash
sig_5K2y8zP9L... · hash_prev: 8f9a2b...
Pre-exec
Action Decision
Allow / deny / rewrite — before anything runs
Ed25519
Signed Attestation
Tamper-evident, on every decision
Fail-Closed
By Default
Denies when policy is unreachable
0*
Wrong Answers Shipped
Internal eval: 6 runs, 2 model families — failures abstained. *Not a customer SLA.
How it works

The path of one action

01
Agent proposes

Any agent, any framework — LangGraph, AutoGen, CrewAI, raw MCP.

salesforce.bulk_export(...)
02
Gate decides

Deterministic policy against steward-confirmed state. Fail-closed.

ALLOW / DENY / REWRITE / ESCALATE
03
Evidence signed

Ed25519 signature, hash-chained, write-once storage.

sig 9f2c…e81a · prev 77b0…40dd
04
Anyone verifies

Open-source CLI — no trust in us required.

$ pramiti-mcp-verify check bundle.json
The Problem

You gave an AI agent write access. One bad action ends up in the news.

Made Up Answers
AI makes up wrong answers when it looks at raw, messy databases.
Slow Setup
Data teams spend months writing rules from scratch for every new tool.
Ignored Rules
Safety rules are stuck in manuals, so AI agents can easily break them.
One Platform. Three Planes.

Every AI interaction is either reading data or taking action. We govern both — and prove it.

Control Plane

Praxom

"Is this action allowed?"

Intercept, validate, and enforce every agent action before it executes — even when the agent is compromised.

Explore Praxom →
Audit Plane

Flight Recorder

"What happened, and can we prove it?"

Every agent action recorded, cryptographically signed, and exportable in OCSF format.

Learn more →
Context Plane

Epistom

"What does this data mean?"

Hallucination-resistant understanding of enterprise data — agents act on certified meaning.

Explore Epistom →
The actual product

Not a mockup. This is the console.

Live workspace capture: a prompt-injected agent tries to bulk-export sensitive claims — the gate denies it, every attempt signed into the ledger.

Evidence console: live attestation feed showing signed DENY decisions
Control console: registered agents with trust levels
Control — agents, trust tiers, kill switch
Explore console: governed data catalog
Explore — steward-approved catalog
Control Plane

Praxom

✓
Policy Decision Point
ALLOW / DENY / REWRITE / WARN / ESCALATE on every agent action. Fail-closed.
✓
JSON Predicate Engine
10 operators, compound rules, glob matching, 60s cache.
✓
Formal SHACL Constraints
Business rules as formally verifiable graph shapes.
✓
Action Rewrite Engine
Cap values, mask fields, set defaults before execution.
✓
Signed Attestation Store
Ed25519-signed, hash-chained. Append-only audit trail.
✓
Identity Adapter
AWS IAM, Azure MSI, and SPIFFE/SVID workload identity.
✓
FinOps Token Gating
Hard cost ceilings per agent, per workspace, per month.
✓
SDK Wrappers
Drop-in for LangGraph, AutoGen, and CrewAI.
Full Praxom details →
Context Plane

Epistom

✓
Virtual Knowledge Graph
OWL 2 QL ontology maps business concepts to database schemas.
✓
NLQ to SQL Pipeline
Five trust tiers - deterministic paths answer first; the LLM only runs where it earns its place.
✓
Verified Query Patterns
Certified SQL patterns across 22 industries. Zero LLM needed for known questions.
✓
SQL Validation Gate
Every table and column checked against live schema before execution.
✓
Semantic Drift Detection
Schema changes auto-degrade AI confidence scores.
✓
Calibrated Abstention
Says 'I don't know' instead of hallucinating.
✓
Multi-Turn Conversations
Entity tracking and pronoun resolution across a session.
✓
Self-Correcting Queries
Auto-retry on SQL errors with semantic anomaly detection.
Full Epistom details →
Setup

Connect in minutes. Govern forever.

1

Connect

Securely link your databases — Snowflake, Postgres, MongoDB, and more.

2

Understand

Epistom introspects your schema and proposes Object Types automatically.

3

Publish

Approve your Knowledge Model. MCP endpoint goes live instantly.

4

Query & Guard

Agents get verified answers. Praxom enforces every action. You get the audit trail.

Pricing

Start free. Scale as agents grow.

Free exploration · Growth at $2,500/mo · Enterprise custom

See full pricing →

Frequently asked questions

How is Epistom different from a vector database?
Vector databases retrieve similar documents. Epistom validates meaning — it knows that 'revenue' means net paid invoices only, not gross orders. It blocks hallucinated columns before SQL runs.
Does Praxom work if our AI model is compromised?
Yes. Praxom is deterministic and external to the model. Even if prompt injection manipulates the model, Praxom evaluates every proposed action against your policies independently.
Can I use my own AI model?
Yes. Epistom is model-agnostic. Claude is the default but you can configure GPT, Gemini, Llama, or any local model via the LLM_PROVIDER environment variable.
How long does onboarding take?
Connect a database in minutes. The auto-discovery agent proposes Object Types automatically. A typical SaaS schema is approved and live in under a day.
What happens if Praxom itself goes down?
Actions stop. The gate is fail-closed by design: if policy cannot be evaluated, the action does not execute. An availability escape hatch (break-glass) exists, but it is explicit, scoped, and every use is signed and logged.
Does the gate slow our agents down?
The decision core is deterministic and runs in-process before the action leaves - no LLM in the enforcement path. The expensive part of any agent step is the model call, not the gate.
How is this different from prompt guardrails?
Guardrails ask the model to behave. Praxom doesn't ask - it sits outside the model and evaluates the action itself against steward-confirmed facts about your systems. A perfectly jailbroken agent produces the same DENY as an honest one.
What do our auditors actually get?
A signed, hash-chained, append-only record of every decision, anchored to write-once storage, plus scored reports against SOC 2, DORA, ISO 42001, NIST AI RMF, and the EU AI Act - generated from the record itself, and verifiable with an open-source CLI.
Do you see our data?
Epistom connects read-only, PII is masked before any LLM call, and self-hosted deployment is available for regulated environments. The knowledge model your team approves belongs to you and exports to dbt, Looker, and Tableau.
Evidence, not assurances

Don't trust us. Verify us.

Signed, hash-chained decisions — checkable offline with open-source tools. Free MCP scan is the front door.

$ pip install pramiti-mcp-verify
$ pramiti-mcp-verify check attestations.jsonl
4,187 records · signatures OK · chain intact
$ uvx pramiti-mcp-gateway scan --config mcp.json
severity: critical 4 high 1 medium 1
[CRIT] payments.transfer_funds
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