Session-aware, deterministic safety for any LLM.
Most safety filters score one message at a time, so anything that builds up gradually over a session slips past them: grooming, staged jailbreaks, data pulled out a little at a time. PRAMANA reads the whole conversation instead, and stops it once the pattern is clear. No separate classifier, no judge model, no added inference. The only model call is the one already answering your user.
One message looks harmless. The session tells a different story.
Standard content filters evaluate each prompt in isolation. PRAMANA reads the entire session history, so attacks that escalate slowly across many turns get flagged before the model ever responds, all within a single LLM call.
Each turn below passes on its own. But the conversation follows a grooming playbook: isolation from guardians, boundary testing, private contact. PRAMANA flags the trajectory, not any single message.
Two ways to run it, no retraining
- or a rule-cited reject
Powers PRAMANA-Legal and PRAMANA-Code.
- or an audit-logged reject
Powers PRAMANA-. No weight access, no fine-tuning, no API changes.
Every wrong answer has an owner now.
LLMs are probabilistic pattern matchers with no built-in mechanism to verify their own output. When that output drives a legal brief, a loan decision, or a medical recommendation, the gap between prediction and fact becomes a liability.
PRAMANA closes that gap at inference time, catching hallucinated facts, flawed reasoning chains, and injected instructions before they reach downstream systems.
Airline chatbot invented a refund policy that didn't exist. The tribunal ruled the airline was liable for what its AI told the customer.
Attorneys relied on LLM-generated citations that turned out to be fabricated. The court sanctioned them for filing fictitious case law.
The question of who pays when an AI gets it wrong is no longer theoretical. It's settled case law.
EU AI Act
High-risk AI rules take effect August 2026, carrying fines of up to €35M or 7% of worldwide turnover.
SR 11-7 · OCC 2011-12
Regulated banks now need documented validation for every LLM-generated output, from internal memos to risk assessments.
Enterprise GenAI spend
Enterprise AI budgets jumped roughly 6× in one year. The buying criteria have shifted from raw capability to provable accuracy and audit readiness.
Liability is precedent
Recent rulings established that deploying organizations bear the cost when their AI fabricates facts or policies.
Product walkthroughs in under three minutes
Three products, all on one engine
Start with a free deliverable, then run a fixed-scope pilot on your own hardware
Every pilot starts with something we send back inside 24 to 48 hours: a contract risk audit, a Pramana deployment readout, or an audited code batch. You don't enter a card or sign anything until it's sitting in your inbox.
If it lands, the two-week paid pilot ships as a self-hosted container that runs on your own infrastructure. There's an air-gapped option too, so your data never leaves your network.
DOCX redline + risk report
Send us one contract - an NDA, MSA, employment, vendor, or lease. Within 24 hours you get a redline of the flagged clauses, the rule each one failed, the reasoning behind it, and a suggested fix.
Pramana deployment readout
Send your top three attack patterns or a 50-prompt/session set. Within 48 hours you get the block decision for each prompt/session, sample audit log entries, and a false-positive analysis.
Verified source + traceability
Send 50 lines from a real requirements doc. Within 48 hours you get verified source in your target language and a bundle mapping each line to its requirement. Re-compile it and the hash still matches.
QUANTUM-Translate.
PRAMANA's formal verification substrate also powers quantum chemistry. Feed it a plain-English molecule description and it returns ground-state energies computed on live IBM Heron QPUs. All 8 molecules land within chemical accuracy, even on today's noisy hardware.
Explore the quantum work ↗Gradpulse.
An MIT-licensed, differentiable pulse optimizer for superconducting gates. Hand it a qubit pair and a target gate and it returns the microwave and flux pulse that runs it. Optimized through a full open-system simulation, with every fidelity cross-checked by three independent solvers.
View the repo on GitHub ↗How PRAMANA scores on public benchmarks.
We run frozen models, open-source and frontier alike. A frozen 20B model picks up 10.65 points on LegalBench, and Gemini 2.5 Flash Lite with PRAMANA hits 85.4% - level with Claude Opus 4.7 at roughly 1/50th the per-token cost. Everything here reproduces from open scripts; the full methodology is available under NDA.
Gemini 2.5 Flash Lite with PRAMANA matches Opus 4.7 at roughly 1/50th the per-token cost, across 15,277 samples.
Full 4,314-input corpus · 96.9% 5-seed holdout CV · <0.4pp train/holdout gap.
4/4 indirect-injection splits, 2,108 cases - versus 16.22% undefended on the hardest split.
1,680 prompts across 8 harm categories on frozen LLMs - deterministic structural detection.
Convinced by the products?
Here's how it's built.
We're raising $500k–$1.5M pre-seed. You'll get the architecture, the prior art, and a live walkthrough of the engine. Invent a spec on the call and watch it run. No slides.
