CTGT is live · custom pricing · last verified 5 Nov 2026 · viability monitor
CTGT
CTGT constrains any LLM to your written policy at inference time using deterministic policy graphs and an attestable audit trail.
Few governance vendors publish claims you can check. CTGT does: 78% vs 39% on legal reasoning, 96% entity-resolution integrity, and a $380 vs $15,000 monthly token bill. If you're in SEC/FINRA compliance or insurance underwriting and every output must survive an audit, this belongs ahead of probabilistic filter layers that can't produce a deterministic conformance record.
Verdict generatedVerified 9d ago · liveness 58/100 · cite: rightaichoice.com/tools/ctgt
- Fortune 500 compliance and model-risk officers putting LLMs into audited workflows
- Financial institutions that need LLM outputs traceable to SEC and FINRA obligations
- Insurance underwriting and claims teams where entity-resolution errors change payouts
- Teams replacing frontier-model inference with governed open-source models at high token volume
- Teams with no compliance requirement and no enterprise budget
- Low-stakes chatbots where an occasional wrong answer costs nothing
- Creative and brainstorming work where output variety matters more than conformance
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Skip CTGT if you want a cheap content filter for a low-stakes chatbot, or if your output variety matters more than conformance — CTGT's product is deterministic aligning of each output to written policy.
The economic case CTGT makes is workload-based: on a 1B-token/month enterprise workload it puts a governed open-source model plus its Policy Engine at roughly $380 per month against about $15,000 per month for Claude 4.5 Opus alone. Budget for the governance layer on top of your existing model spend, not instead of it.
In short
CTGT — CTGT constrains any LLM to your written policy at inference time using deterministic policy graphs and an attestable audit trail. Best for Fortune 500 compliance and model-risk officers putting LLMs into audited workflows, Financial institutions that need LLM outputs traceable to SEC and FINRA obligations, Insurance underwriting and claims teams where entity-resolution errors change payouts. Pricing is on request (custom quotes).
What is CTGT?
CTGT — CTGT constrains any LLM to your written policy at inference time using deterministic policy graphs and an attestable audit trail. Best for Fortune 500 compliance and model-risk officers putting LLMs into audited workflows, Financial institutions that need LLM outputs traceable to SEC and FINRA obligations, Insurance underwriting and claims teams where entity-resolution errors change payouts. Pricing is on request (custom quotes).
CTGT is an applied interpretability lab selling a policy engine for regulated AI deployments. Written organizational policy gets converted into deterministic policy graphs, and the engine constrains frontier or open-source models at the moment of output, rather than acting as an input filter or a post-hoc review pass. The target buyer is compliance, model-risk, and legal teams in finance and insurance, plus media and CPG groups enforcing editorial or brand tone at volume. The vendor publishes hard numbers. Standard RAG dropped legal-reasoning accuracy to 39%; CTGT's Policy Engine acting as a logic compiler doubled it to 78%. In entity resolution, RAG introduced noise while CTGT held 96% integrity. At 1B tokens per month, the company's cost comparison puts Claude 4.5 Opus at roughly $15,000 per month against a governed open-source model (GPT-120B-OSS) plus the Policy Engine at about $380 per month, the basis for an up-to-80% inference-cost claim. HaluEval accuracy moved from 95.1% to 96.5% with CTGT applied. The product set covers policy-as-code conversion of written SOPs, real-time policy updates without manual setup, exportable audit reports mapped to the org chart, automated flag-and-fix remediation, and a human-in-the-loop review queue. Underneath sits mechanistic interpretability research: editing model behavior to add safety guarantees without retraining (patent-pending), feature-level work on bias and censorship modulation in DeepSeek-R1, and 2026 work on behaviorally fingerprinting model provenance. Insurance deployments are quoted at 30% better policy adherence and 20-40% lower total cost of ownership. The architectural contrast with probabilistic guardrail tools is the pitch: prompt-level filters sample and score, while CTGT sells deterministic conformance plus a cryptographic attestable record per output. That is heavier, and squarely built for audited workflows across SEC and FINRA obligations.
Key features
- Deterministic policy graph enforcement applied at LLM inference time
- Cryptographically attestable audit trail for every governed output
- Policy-as-code conversion of written SOPs into machine-readable rules
- Logic-compiler mode for legal reasoning (78% accuracy vs 39% with RAG)
- Entity resolution holding 96% integrity where RAG introduces noise
- Model behavior editing for safety guarantees without retraining (patent-pending)
- Mechanistic interpretability-based model editing instead of prompting
- Real-time policy updates applied without manual setup or downtime
- Governs frontier LLMs such as Claude 4.5 Opus
- Works with open-source model backends under policy constraints
- Governed open-source pairing reported at up to 80% lower inference costs
- Behavioral fingerprinting of model provenance through output analysis
- Exportable audit reports mapped to organizational structure
- Automated flag-and-fix remediation actions on policy violations
- Human-in-the-loop review queue for flagged outputs
Behind the verdict
Most AI guardrail tools ask you to trust a probability score. CTGT takes a different road: turn your written policy into a deterministic graph and enforce it at output time, with a cryptographic record per response. For a bank or insurer, that record is the whole point. An auditor doesn't want a 0.93 confidence that the output matched policy; they want a log that says it did, mapped to the org chart, exportable. Pick CTGT when regulation drives the deployment. SEC and FINRA workflows, insurance underwriting and claims where entity-resolution errors change payouts, or any team that must hand a regulator a per-output audit trail. The published insurance figures (30% better policy adherence, 20-40% lower TCO) are the numbers to test against your own workload. Pick it too if you're drowning in frontier-model token costs. The 1B-token/month comparison is concrete: about $15,000 for Claude 4.5 Opus against roughly $380 for GPT-120B-OSS plus the Policy Engine. If your volume is real and your policies are already written down, that math is hard to argue with. Pass when the stakes are low. A customer-support chatbot that occasionally misses tone doesn't need deterministic conformance and an attestable ledger. Creative and brainstorming work is the same story, because conformance is a feature there. And if your policies live in people's heads rather than in documents, there is nothing to compile yet. Where it bites in practice: the policy-as-code step is real work. Someone has to translate prose SOPs into machine-readable rules, and getting those rules wrong is a governance failure, not a bug. CTGT's real-time update capability and human-in-the-loop queue soften this, but the initial conversion is the project's riskiest phase. The closest alternative is a prompt-level
Models under the hood
as of 2026-11-09
Limitations
- CTGT's deterministic approach is a poor fit for work that needs creative or varied output, because conformance is the product.
- Policy-as-code presupposes your policies are written down and machine-readable, so teams running on tribal knowledge face a documentation project first.
- Integration effort into legacy systems can be substantial.
- Note that no pricing, documentation, changelog or integration page was reachable during this research pass, so this profile does not characterize CTGT's plans, API surface, release cadence or third-party integrations.
- The published accuracy and cost figures are vendor-reported on defined workloads, so validate them against your own traffic before committing.
as of 2026-10-11
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Is CTGT still active?
CTGT is live · custom pricing · last verified 5 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is CTGT? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about CTGT?
What people actually say about CTGT — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 4 sources (Hacker News, YouTube, Bluesky, Lemmy) · researched Nov 4, 2026.
Weighted by the 42 posts each of 4 sources contributed.
- +Governed build held up when HN users actively attempted jailbreaks in the public playground.
- +Deterministic policy graphs plus attestable per-output audit trail is a stronger story than probabilistic filters.
- +Vendor publishes methodology, prompts, and benchmark data openly on GitHub for lineage-eval.
- +Explicit architectural fit for SEC and FINRA audited workflows, which generic guardrails lack.
- +Cost comparison at 1B tokens/month ($380 vs $15,000) is concrete and verifiable in principle.
- −Oct 2026 HN thread argues deterministic inference does not yield a deterministic pipeline end to end.
- −Zero third-party validation of the 78%, 96%, or 80% cost claims in the scraped corpus.
- −Community discussion skews to research side, not the policy engine buyers actually license.
- −Opaque enterprise pricing forces a sales call before any cost estimate is possible.
- −No documented integrations — buyers shoulder custom plumbing to reach compliance systems.
- • Custom integration engineering to connect API output to existing SIEM/GRC tooling
- • Policy graph authoring and maintenance by skilled staff — likely professional services
- • Governed open-source server hosting and ops, since the $380/mo figure is inference-only
- • Proof-of-concept and compliance-review time before any production commitment
How we verified this
Every listed fact about CTGT — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 5 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified CTGT 13 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed who it suits, our verdict, description, summary
- — changed description, our verdict, who it suits, summary, who should skip it, models
- — changed who should skip it, description, our verdict, switching in, setup time, pricing fit, features, summary, limitations, models, hidden costs, switching out
- — changed our verdict, description, features, summary
- — changed limitations, switching out, hidden costs, models, features, who should skip it, pricing fit, our verdict, setup time, description, who it suits, use cases, summary, switching in
Showing the 6 most recent of 13 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does CTGT cost in 2026?
CTGT: Pricing is on request (custom quotes). Pricing last verified 5 Nov 2026.
Where the pricing makes sense
The company stage and team size where CTGT's pricing actually pencils out — and where peers do it cheaper.
The economic case CTGT makes is workload-based: on a 1B-token/month enterprise workload it puts a governed open-source model plus its Policy Engine at roughly $380 per month against about $15,000 per month for Claude 4.5 Opus alone. Budget for the governance layer on top of your existing model spend, not instead of it.
Has CTGT's pricing changed?
No plan price for CTGT has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick CTGT?
Pick CTGT if you are Fortune 500 compliance and model-risk officers putting LLMs into audited workflows, Financial institutions that need LLM outputs traceable to SEC and FINRA obligations, Insurance underwriting and claims teams where entity-resolution errors change payouts, Teams replacing frontier-model inference with governed open-source models at high token volume.
Look elsewhere if you are Teams with no compliance requirement and no enterprise budget, Low-stakes chatbots where an occasional wrong answer costs nothing, Creative and brainstorming work where output variety matters more than conformance, Organizations whose policies exist as undocumented tribal knowledge rather than written rules.
Skip it if Skip CTGT if you want a cheap content filter for a low-stakes chatbot, or if your output variety matters more than conformance — CTGT's product is deterministic aligning of each output to written policy.
GeneratedReal-world workflow fit
Concrete scenarios for the personas CTGT actually fits — and what changes day-one when you adopt it.
Convert the firm's written SEC and FINRA disclosure policy into a policy graph, attach it to the model generating disclosure drafts, and let flag-and-fix remediation catch violations before a human reviewer sees the draft.
Outcome: Each generated disclosure carries a cryptographically attestable conformance record that can be exported as an audit report mapped to the org chart.
Run claim-letter generation through CTGT so state-specific regulatory language is enforced at the moment of output, with entity resolution kept intact rather than degraded by a RAG retrieval step.
Outcome: Policy updates land in minutes rather than months, and entity-resolution integrity stays at the published 96% rather than the accuracy drop seen when RAG is layered on.
Apply brand and editorial tone policy across thousands of AI-assisted articles per day, routing only flagged outputs into the human-in-the-loop review queue.
Outcome: Tone conformance is enforced at generation time, and the review queue holds only the exceptions rather than the full volume.
Use cases
- Deploy policy-as-code so every AI-generated insurance claim letter complies with state regulations.
- Automatically remediate off-brand product descriptions across a CPG company's portfolio.
- Audit AI-generated financial disclosures against SEC requirements with a defensible log.
- Govern medical documentation outputs against HIPAA and internal clinical guidelines.
- Enforce editorial tone consistency for a media outlet producing thousands of AI-assisted articles daily.
- Run governed open-source models in place of frontier models where policy-constrained work allows it.
Setup time & first value
How long it actually takes to get something useful out of CTGT — broken out by persona, not the marketing-page minute.
Expect a documentation phase first: policy-as-code needs your rules written down and machine-readable before anything compiles. Once a policy graph exists, CTGT states real-time policy updates apply with no manual setup or downtime, so subsequent changes are fast. Legacy system integration is the variable that can stretch a rollout.
Switching to or from CTGT
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From prompt-level guardrail filters: replace sampled input/output scoring with a deterministic policy graph enforced at inference time.
- →From RAG-based policy retrieval: move policy handling out of the retrieval layer, where the vendor reports legal-reasoning accuracy dropped to 39%.
- →From a frontier-only inference stack: pair open-source models with the Policy Engine where policy-constrained work allows, which CTGT reports at up to 80% lower inference cost.
- ↗To a conventional guardrail or moderation API: you keep prompt-level filtering but lose the per-output attestable conformance record.
- ↗To manual human review of all outputs: you keep the audit trail but give up the automated flag-and-fix remediation volume.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “CTGT”, and we withheld 6: 6 could not be judged, because “CTGT” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about CTGT.
Official links
What are the best CTGT alternatives?
The closest alternatives to CTGT we list are Alma, asqav and Spectrum Labs. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Alma | Freemium | Pricing not published | — |
| asqav | Freemium | Pricing not published | — |
| Spectrum Labs | Contact Sales | Custom pricing | — |
Alternatives to CTGT
View all
Alma
Self-hosted AI agent governance with an access map, Cedar policy gates and a signed audit ledger for Claude Code, Codex and Gemini CLI
asqav
Asqav gives AI agents signed audit-trail receipts with ML-DSA-65 signing and verification that needs no Asqav account
Spectrum Labs
AI red teaming and custom guardrails that hold your models, apps, and agents to your own written policy
Tools that pair well with CTGT
Common stack mates teams adopt alongside CTGT, with the specific reason each pairing earns its keep.
Alma
Self-hosted AI agent governance with an access map, Cedar policy gates and a signed audit ledger for Claude Code, Codex and Gemini CLI
asqav
Asqav gives AI agents signed audit-trail receipts with ML-DSA-65 signing and verification that needs no Asqav account
Spectrum Labs
AI red teaming and custom guardrails that hold your models, apps, and agents to your own written policy
CTGT vs its closest rival
CTGT's closest head-to-head is with Audioeye.
Our CTGT vs Audioeye verdict written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
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