Perimattic

Perimattic AI Suite · AI observability platform

The AI observability platform for regulated enterprise AI

Trace every LLM call and agent step, score hallucinations on live traffic, attribute cost per model, agent and user, and turn it all into audit evidence for HIPAA, SOC 2, EU AI Act and DORA. All on OpenTelemetry.

Apply for early access See how it works

Perimattic AI Suite is in early access. Apply and a Perimattic engineer will follow up about your use case.

  • Built on OpenTelemetry
  • No proxy in your request path
  • Works alongside Datadog and Grafana

Example trace of a claims-processing agent: orchestrator, retrieval, tool call and LLM spans, with a hallucination flag, cost shown on the LLM span and an audit record ID.

Illustrative example with sample values. Not a product screenshot or customer data.

Built by the Perimattic engineering team, who have been running production DevOps and monitoring since 2018 for clients across the US, UK, EU, UAE, Singapore, Canada and India.

Production DevOps and monitoring
Since 2018
Open-standard instrumentation
OpenTelemetry
Regulations mapped on this page
8 frameworks

AI observability

What is AI observability?

AI observability is the practice of instrumenting LLMs, AI agents and RAG pipelines so teams can see what the system did and why. It works through traces, metrics, logs, evaluation scores and user feedback. Teams use that evidence to debug failures, control cost and prove compliance.

Last updated by the Perimattic AI Suite team

AI observability vs LLM monitoring

AspectLLM monitoringAI observability
AnswersThat something changedWhy it changed
SignalsLatency, token count, error rateFull agent traces, quality scores, feedback, audit logs
Good forA single internal chatbotMulti-agent systems and regulated workloads
Read the complete AI observability guide

The five signals

  1. TracesEvery step of a request, from the first prompt to the last tool call.
  2. MetricsLatency, tokens and cost per model, agent and user.
  3. LogsStructured records of inputs, outputs and decisions.
  4. Evaluation scoresFaithfulness, hallucination rate and relevance on live traffic.
  5. User feedbackRatings and annotations linked to the exact trace.

These signals are used to:

  • Debug
  • Cost
  • Compliance

Enterprise AI platform

The observability layer your enterprise AI platform is missing

An enterprise AI platform is the stack an organisation uses to build, deploy, govern and scale AI. It usually has five layers: models and gateways, data and retrieval, agent orchestration, the applications on top, and the infrastructure underneath.

Perimattic AI Suite doesn't replace Amazon Bedrock, Microsoft Foundry, Google Vertex AI or Databricks. It adds an observability and governance layer across the platforms your teams already build on, so every model call and agent step can be traced, evaluated, costed and audited.

Comparing platforms? Our enterprise AI platform buyer’s guide covers the main options and what to ask each vendor.

  1. Applications · Copilots, chatbots, internal agents
  2. Orchestration · Agent frameworks and custom agents
  3. Data & retrieval · Vector stores, RAG pipelines
  4. Models & gateways · Hosted LLM APIs and cloud model platforms
  5. Infrastructure · Kubernetes, cloud, on-premises
Perimattic AI Suite · observe · evaluate · govern
A typical AI platform for enterprise teams, with Perimattic AI Suite running across every layer.

5 questions to ask of any enterprise AI platform

  1. Can you trace a single user request across every agent, tool and model it touched?
  2. Do you score output quality on production traffic, not just on test sets?
  3. Can you attribute AI spend to a team, a feature and a user?
  4. Can you export evidence that an auditor will accept?
  5. Can your telemetry stay in the region your regulator requires?

If the answer to any of these is "no", your platform needs an observability layer.

Production AI

What breaks in production AI, and what you'll see instead

AI systems rarely crash. They fail quietly: a confident wrong answer, a cost spike, an agent stuck in a loop. Here's how each one shows up in Perimattic AI Suite, and why AI agent monitoring needs more than uptime checks.

  • Silent hallucinations

    Symptom
    Answers sound right but cite facts that aren't in your sources.
    What you see
    Faithfulness scores drop on live traffic, and the trace shows which retrieval step failed.
    Outcome
    You catch the problem before customers or auditors do.

  • Runaway token spend

    Symptom
    The monthly LLM bill jumps and nobody knows why.
    What you see
    Cost per model, per agent and per user, with budget alerts.
    Outcome
    AI spend is tied to a team and a feature.

  • Agents stuck in loops

    Symptom
    Requests time out, or an agent calls the same tool again and again.
    What you see
    A span-level trace with every retry, handoff and tool error.
    Outcome
    Engineers can go straight to the failing step instead of reading raw logs.

  • "Show us the evidence" audits

    Symptom
    Compliance asks for proof of how an AI decision was made.
    What you see
    A structured record of the request, mapped to the regulation that applies.
    Outcome
    Evidence comes from the telemetry you already collect, not a week of manual log exports.

Platform capabilities

Six capabilities for production AI systems

One OpenTelemetry-based platform, from agent tracing to compliance evidence.

  • Agent tracing & observability

    Trace every tool call, reasoning step, handoff and escalation across multi-agent workflows.

    Produces: End-to-end session traces, latency per span

    Explore AI agent observability

  • Hallucination detection

    Detect factual errors and unfaithful answers in production using LLM-as-judge and RAGAS scoring.

    Produces: Faithfulness score, hallucination rate

    See hallucination checks in LLM observability

  • Prompt injection monitoring

    Classify adversarial prompts and surface injection attempts before they turn into incidents.

    Produces: Injection attempts by source

    Explore prompt injection monitoring

  • LLM cost tracking

    Attribute token spend per model, per agent and per user for budget governance.

    Produces: Cost per user, cost per feature, budget alerts

    See LLM observability and cost tracking

  • RAG evaluation

    Score retrieval quality, faithfulness and answer relevancy across your RAG pipelines.

    Produces: Context precision, answer relevancy, faithfulness

    Explore RAG evaluation in production

  • Compliance audit trail

    Capture structured records of AI decisions, mapped to HIPAA, SOC 2, EU AI Act and DORA.

    Produces: Evidence exports, retention reports

    See the compliance hub

How it works

From first trace to audit evidence in four steps

If you already use OpenTelemetry, you're most of the way there. There's no proprietary agent and no proxy in your request path.

  1. Instrument

    Add the OpenTelemetry SDK and point the OTLP exporter at Perimattic.

  2. Ingest

    Traces, metrics, logs and eval scores flow into the Perimattic collector, across multiple models, agents and tenants.

  3. Analyse

    Dashboards show cost per agent, hallucination rate, injection attempts, p99 latency and RAGAS scores.

  4. Alert & prove

    Set threshold alerts, and export evidence packages for your compliance team.

Point your existing OTel exporter at Perimattic

export OTEL_EXPORTER_OTLP_ENDPOINT="<your-perimattic-otlp-endpoint>"
export OTEL_EXPORTER_OTLP_HEADERS="authorization=Bearer $AI_SUITE_KEY"
export OTEL_SERVICE_NAME="claims-agent"

Illustrative. Your endpoint and key are issued during early-access onboarding.

To first trace for a single-model app
Under 30 min
For multi-agent systems with compliance dashboards
1–2 days

Typical setup estimates. Your timeline depends on your stack.

Already on Datadog or Grafana? Send the same OpenTelemetry data to both. Your OTel Collector can fan out to Perimattic AI Suite and your existing APM at the same time.

Ready to see your own traces?

Apply for early access Generate your OTel setup

Compliance evidence

Audit evidence for 8 regulatory frameworks

Regulators increasingly expect you to show how an AI system behaved, not just say it was tested. Perimattic AI Suite turns production telemetry into structured evidence your compliance team can hand to an auditor, so enterprise AI governance rests on records rather than policy documents alone.

Key EU AI Act dates

  1. Article 50 transparency obligations apply
  2. Obligations for stand-alone high-risk systems (Annex III) apply
  3. Obligations for high-risk AI in regulated products (Annex I) apply

Dates reflect the AI Omnibus amendments in force since 27 July 2026. Last verified 28 September 2026 against the European Commission announcement. Source: European Commission

Evidence record

Illustrative
{
  "request_id": "req_7f3a…",
  "service": "claims-agent",
  "model": "<llm-provider/model>",
  "retrieved_sources": ["policy_2026_v4.pdf#p12"],
  "eval": { "faithfulness": 0.62, "flagged": true },
  "reviewer": "<compliance-reviewer>",
  "retention_policy": "<your-policy>"
}

Illustrative evidence record: request, model, retrieved sources, evaluation scores, reviewer and retention policy in one place. Sample values, not customer data.

Perimattic AI Suite supports your compliance programme. It is not legal advice and does not certify compliance.

How we compare

How Perimattic AI Suite compares to Langfuse, LangSmith and Datadog

How to choose an AI observability platform

  1. 1. StandardsDoes it ingest OpenTelemetry, so you aren't locked into one SDK?
  2. 2. DepthDoes it trace multi-agent sessions end to end, not just single LLM calls?
  3. 3. QualityDoes it evaluate live production traffic?
  4. 4. EvidenceCan it export records mapped to the regulations you answer to?
  5. 5. ControlCan you self-host it or pin data to a region?
DimensionLangfuseLangSmithDatadog LLM ObservabilityPerimattic AI Suite
OpenTelemetry ingestionYes: native OpenTelemetry integrationYes: OTLP traces from any app, not only LangChainYes: native support for the OTel GenAI semantic conventions (v1.37+)Yes: built on the OTel GenAI semantic conventions
DeploymentManaged cloud (US, EU, Japan and HIPAA regions) or self-hosted; open-source core under MITManaged cloud; self-hosting is an Enterprise plan add-onSaaS, part of the Datadog platformAgreed with each team during early access
Best forOpen-source, self-hosted LLM tracingTeams building on LangChain and LangGraphTeams standardised on Datadog APMRegulated teams that need telemetry turned into audit evidence

Competitor details last verified 28 September 2026 from each vendor’s own documentation:

  • Choose Langfuse if you want open-source, self-hosted tracing.
  • Choose LangSmith if your stack is built on LangChain.
  • Choose Datadog if your whole observability estate already lives there.
  • Choose Perimattic AI Suite if you need telemetry turned into evidence your auditors accept.

Industries

Built for regulated industries

Security

Deployment, security and data residency

Enterprise AI security starts with your telemetry, which contains prompts, outputs and sometimes personal data. Here's what the architecture guarantees today, and what we confirm with you before any production data flows.

By design

  • Open-source instrumentationYou instrument with the standard OpenTelemetry SDKs, not a proprietary agent.
  • Nothing in your request pathTelemetry is exported alongside your requests. There is no proxy between your app and your model.
  • No telemetry lock-inThe same OTLP data can go to your existing APM, so you keep a copy wherever you need it.

Confirmed in your security review

Deployment details are agreed with each early-access team and written into the engagement before production data is sent:

  • Hosting model and region
  • Data residency
  • PHI/PII redaction
  • Encryption in transit and at rest
  • SSO/SAML and role-based access
  • Audit-log immutability
  • Retention per project
  • Support terms
Request the security overview

Why Perimattic

Built by engineers who've kept production systems running since 2018

Started in DevOps and monitoring
2018
Delivery regions
7
Disciplines: full-stack, ERP and AI
3

Perimattic started in 2018 doing DevOps automation and monitoring: keeping other people's systems up at 2 a.m. We've since built full-stack, ERP and AI systems for clients across the US, UK, EU, UAE, Singapore, Canada and India.

Perimattic AI Suite brings that production discipline to AI. Every trace, alert and evidence export is designed by people who've been on call.

Early access

How early access works

Perimattic AI Suite is onboarding a limited number of early-access teams, so we can work closely with each one.

  1. Apply

    Tell us what you are building, your stack and the regulations you answer to.

  2. Scope

    A Perimattic engineer reviews your use case and agrees deployment and security details with you.

  3. Instrument

    We help you instrument your first production or pre-production AI system.

Pricing

Pricing isn't published yet. During early access, we discuss pricing directly with each team, based on the use case and deployment it needs.

How pricing and early access work Apply for early access

FAQ

Common questions about AI observability

What is AI observability?

AI observability is the practice of instrumenting LLMs, agents and RAG pipelines so teams can see what the system did and why. It combines traces, metrics, logs, evaluation scores and user feedback, and teams use that evidence to debug failures, control cost and prove compliance. Our complete AI observability guide covers it in depth.

How is AI observability different from LLM monitoring?

Monitoring tells you that something changed: latency, tokens or errors. Observability tells you why: it reconstructs full agent sessions, scores output quality and keeps audit-grade logs. A simple internal chatbot may only need monitoring, but multi-agent and regulated systems need observability. See LLM monitoring vs observability for the full comparison.

What is an enterprise AI platform, and where does observability fit?

An enterprise AI platform is the stack an organisation uses to build, deploy, govern and scale AI: models, data and retrieval, orchestration and applications. Observability is the layer that runs across all of it, recording what every model and agent did so it can be debugged, costed and audited. Perimattic AI Suite provides that layer alongside platforms like Amazon Bedrock, Microsoft Foundry and Databricks. It does not replace them. Our enterprise AI platform guide compares the main options.

How do I choose an AI observability platform for a regulated industry?

When comparing AI observability tools, check five things: OpenTelemetry support, end-to-end multi-agent tracing, evaluation on live traffic, evidence mapped to your regulations, and self-hosting or regional data residency. The last two matter most for healthcare and financial services.

What does OTel-native mean?

It means the platform ingests standard OpenTelemetry data, so you use the same SDKs and collectors you already run for your other services. Perimattic AI Suite follows the OpenTelemetry GenAI semantic conventions. Those conventions are still in development, and we track each release.

Which regulations require AI observability?

Several regulations expect you to monitor AI systems and keep records of how they behave. HIPAA requires audit controls for systems handling electronic PHI. DORA has applied since 17 January 2025 and covers ICT risk and incidents for EU financial firms, including third-party ICT providers such as LLM APIs. Under the EU AI Act, Article 50 transparency duties apply from 2 August 2026, and high-risk obligations apply from 2 December 2027 (Annex III) and 2 August 2028 (Annex I). BaFin, MAS FEAT, OSFI E-23 and India's DPDP rules add model-risk or data-processing record requirements.

How does Perimattic AI Suite compare to Langfuse, LangSmith and Datadog?

All four can ingest OpenTelemetry traces. Langfuse is a strong open-source, self-hostable option. LangSmith fits teams built on LangChain. Datadog suits teams already standardised on its APM. Perimattic AI Suite is built for regulated teams that need production telemetry turned into audit evidence mapped to HIPAA, SOC 2, EU AI Act and DORA. Read Perimattic vs Langfuse for a detailed example.

How much does Perimattic AI Suite cost?

Pricing isn't published yet. During early access, we discuss pricing directly with each team, based on the use case and deployment it needs. The pricing page explains how that works, or you can apply for early access to start the conversation.

Can I self-host it? Is there an open-source option?

Instrumentation uses the open-source OpenTelemetry SDKs, so your telemetry isn't locked in and can also go to other OTel-compatible tools. Deployment options, including whether a self-hosted or region-pinned setup fits your data-residency requirements, are agreed with each team during early access.

Which LLM providers and frameworks are supported, and how long does setup take?

Any provider or framework whose calls emit OpenTelemetry GenAI spans can send data, for example OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Vertex AI and Mistral. As a typical estimate, a single-model app reaches its first trace in under 30 minutes, and multi-agent systems with compliance dashboards take 1–2 days.

Go further

Related tools, guides and services

See every AI decision, and prove it.

Apply for early access and we'll help you instrument your first production AI system with a Perimattic engineer.

Apply for early access See how it works

Prefer email? sales@perimattic.com

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