SREPRIMER delivers unified AI observability across infrastructure, pipeline tracing, drift detection, security, and cost — helping enterprises see everything their AI systems are doing, before problems become incidents.
Latency spikes, token pressure and model degradation build invisibly. Users notice before engineering teams do.
Prompt injection, jailbreaks and PII exfiltration are plain text to firewalls and SIEMs. Traditional security tools detect 0% of LLM-specific attack patterns.
Models degrade silently as user behaviour shifts. Hallucination rates climb for weeks before anyone investigates.
Simple queries routed to expensive models. No budget visibility. Enterprises discover AI overspend on the invoice.
Multi-step chains and RAG retrievals are black boxes. No visibility into which step failed or what the model received.
SOC2, HIPAA and GDPR auditors ask what went into your AI. Most companies cannot answer this question.
SREPRIMER instruments every layer of your AI stack — from GPU utilisation to model behaviour, security threats and dollar costs — delivered as open-source tooling with enterprise support.
Every demo is fully functional on local infrastructure — no cloud dependency, no mock data. What you see is exactly what your clients get on day one.
A live Grafana dashboard showing Mistral-7B running on local infrastructure. Every inference request is measured — prompt processing time, generation speed, token throughput and queue depth — refreshing every 5 seconds. A load generator creates continuous realistic traffic and a stress tester produces dramatic latency spikes visible in real time.
A RAG application and multi-turn chatbot with every request traced end-to-end. From user query through vector retrieval, prompt construction, LLM inference to final response — every step is a measurable span. Two trace viewers show parallel views of the same pipeline, illustrating real distributed tracing for AI chains.
Three interactive HTML reports across a 14-day drift scenario. Report one shows input distribution drift as user topics shift. Report two shows output quality degradation — relevance, confidence and length collapsing. Report three is a live chart showing hallucination rate climbing from 4% to 35% after a day-seven inflection point.
A live security scanner processing 25 real attack payloads against Mistral-7B. Every request is scanned before reaching the model. Four attack classes are demonstrated — prompt injection, jailbreak attempts, PII exfiltration and toxic content. A dashboard shows block rate, risk scores, scanner attribution and latency refreshing every three seconds.
A dual-model routing system that automatically classifies queries as simple or complex and routes to the appropriate cost tier. Every request shows cost, token count, latency and savings. A production simulator generates a custom report showing real enterprise savings at the client's actual token volume across three deployment scenarios.
SREPRIMER speaks to every decision maker in the room — from the engineer debugging pipelines to the CFO reviewing the AI spend line.
SREPRIMER's detection, tracing, and security frameworks are built on the industry standards that CISOs, compliance teams, and regulators already trust — ensuring every capability maps directly to the frameworks your organisation uses.
Investors, enterprise teams, and AI engineering leaders — reach out to explore SREPRIMER AI Observability.