Groundedness
Verify every response against its source context. TRACE scores each claim, highlights unsupported spans, and routes decisions automatically.
Real-time safety for knowledge agents
TRACE protects knowledge agents from private-data leaks, unsupported answers, and wasted context - without replacing your stack.
- Plug into your existing agents and pipelines
RAG question
Refund policy
Which refund terms apply to enterprise contracts?
TRACE
Groundedness
0.45
Review
Privacy
Account number
Redacted
Context
62% dead weight removed
Decision
ReviewRepeatRelease
Enterprise AI scales with agents. So do their risks.
TRACE is for the moment a knowledge agent stops being a demo and starts touching customers, documents, and decisions.
Privacy
Detect and redact over 50 GDPR-defined entity types before data reaches the model or the user. No PII is ever stored.
Compression
Reduce retrieved context by up to 60% without losing answer quality. Lower token cost, lower latency, same results.
TRACE sits beside the agent and checks what matters while the work is happening.
HOW TRACE WORKS
Drop TRACE next to your agent. Keep the control loop yours.
TRACE lives next to your RAG pipeline or knowledge workflow. It checks inputs, context, and outputs in real time. Whether on autopilot or strict human-in-the-loop is up to you.
TRACE SDK
trace.pyreal API
result = trace.verify(
answer=agent_answer,
context=retrieved_context,
)
if result.decision == "review":
route_to_human(result.evidence)TRACE output
Trace wrong answers
decisionreview
groundedness0.45
unsupported_spans2
USE CASES
TRACE fits where agents already work.
Use the same safety layer for support workflows and legal review: grounded answers, context compression, private data handling, and audit-ready traces.
For customer-facing answers
Keep responses grounded
Catch ungrounded responses and automate decision making before users see weak or unsupported answers.
Bring humans in when needed
Pull in human reviewers for risky responses and turn transparency into trust with your users.
Improve retrieval over time
Log quality over time, detect dead weight in context, and improve your knowledge base and retrieval.
QUALIFIED PILOT PHASE
Pilot before deployment. We deliver proven quality only. Period.
Before you pick the deployment, we prove TRACE on your data and workflows.
Cloud
For testing and low-risk workloads
Fast managed deployment for trace tests, demos, and non-sensitive evaluation.
Dedicated
Managed with stronger isolation
Predictable capacity and stronger isolation for early production teams.
VPC
Inside your cloud boundary
TRACE runs inside your cloud with your network, storage, and controls.
On-prem / air-gapped
For regulated environments
Full local control when production data cannot leave your environment.
Same API. Same SDK. Same product behavior. Different deployment boundary. Cloud is for evaluation and low-risk workloads; sensitive production data belongs in Dedicated, VPC, or on-prem deployments.
PRICING
Pay for the deployment. Not every trace.
You pay for the deployment. Throughput depends on the selected runtime. No surprise per-trace billing.
Flat monthly pricing by deployment size. No per-request billing. Throughput depends on selected deployment capacity.
Swipe plans
No sensitive production data belongs in shared evaluation paths. Production use starts with a qualified pilot and the right deployment boundary.
About LatenceBuilt by one focused founder
Built by one focused founder
Deployed with you.
I'm Dennis, the founder and builder behind Latence. If you test TRACE, you work directly with the person designing, shipping, and deploying the system.
No handoff maze. No consulting theater. Just direct work on the agent risks your team actually has.
Not for you. With you.
Dennis DickmannFounder · Latence
OPEN WORK
Built in the open where it matters
Latence is built by shipping real infrastructure, not slideware. Explore the SDK, retrieval experiments, and deployment work behind TRACE.
SDK
trace-sdk
Lightweight Python interfaces for calling TRACE from existing agent and RAG systems.
View SDKRetrieval Engine
colsearch
Experimental infrastructure for retrieval research and optimization.
View projectDeployment stack
vllm-factory
Performance-focused serving experiments for private and high-throughput deployments.
See stackProtect your knowledge agents before they reach production risk
Send a few real RAG answers. TRACE will show where private data, unsupported claims, or wasted context appear.
FAQWhat teams
What teams
usually ask
The short answers most enterprise teams want before they start a pilot.
- 01Is this another eval framework?No. Evals happen after the fact, on a dataset. TRACE sits beside the live knowledge workflow and returns a decision your system can act on: continue, repair, review, or block.
- 02Do I have to switch my LLM or retriever?No. TRACE is designed to work beside your existing knowledge stack. You can keep your model, retriever, tools, orchestration framework, and deployment path.
- 03What does TRACE check?Private data, prompt attacks, unsupported answers, context waste, and auditability. Some guardrails are in private beta and are qualified during the pilot.
- 04What does groundedness mean here?For every answer, TRACE estimates whether the supplied context supports the claim being made. The result is not just a score; it is a decision intent your application can route into continue, repair, review, or block.
- 05Can we self-host?Yes, through a qualified design-partner path. Start hosted for testing, then move to Dedicated, VPC, or on-prem when prompts, code, customer data, or compliance requirements demand a private boundary.
- 06What about my users' data?TRACE is designed around short-retention cloud evaluation and private deployment for sensitive workloads. Redaction, logging, and retention are qualified before a pilot handles production data.
- 07How do I get started today?You can signup for free, generate an API key and get started. I recommend using our SDK for the simplest starting point. Alternatively you can test our playground at demo.latence.ai or get a quote for a TRACE audit where we analyze your current RAG system's performance and highlight why and where TRACE can make your RAG pipelines safer and more reliable.