Workflow reliability
Track successful completion, failed steps, retries, timeouts and tool side effects across the full workflow.
A successful model response is not the same as a successfully completed business task.
Services / AI Safety & Reliability
AI monitoring & visibility.Understand what happens in production.
See how your AI application behaves, where work breaks down and what it costs to operate. We connect traces, quality signals and business outcomes to the people who can act on them.
Correlated evidence. Clear ownership. Privacy-aware collection.
The business case
A working demo is not an operating picture.
Once AI reaches real users, inputs change, integrations fail and costs move. A model-provider dashboard only shows part of that story. You need visibility across the application and its actual business outcomes to decide what to improve, pause or investigate.
When this becomes useful
When an AI feature is entering production, users report inconsistent answers, workflows fail without a clear cause or spending is hard to attribute. It also helps teams operating multiple models, tools or collaborating agents understand what changed between releases.
What we build
An agreed instrumentation layer, focused dashboards, review signals, alert routes and runbooks. The goal is evidence your team can use, not a wall of charts or a claim that every unsafe response can be detected automatically.
What to observe
Six signals. One operating context.
We select measures around your workflows and risk. These are monitoring areas, not live customer statistics or universal performance targets.
Response quality
Connect sampled review, user feedback and targeted evaluators to the traces that produced an answer.
Automated scores are indicators, not ground truth; validate them against human-reviewed cases.
Latency & experience
Measure time to first response, end-to-end duration, queue delays and slow retrieval or tool calls.
Segment by workflow and use percentiles rather than hiding slow requests inside an average.
Cost & resource use
Attribute tokens, retries, tool usage and infrastructure consumption to features, tenants or workflow versions.
Provider usage can support estimates; reconcile with billing and agreed allocation rules.
Safety & authority
Capture policy rejections, permission failures, approval states and attempted out-of-scope actions.
A recorded check is useful evidence, but does not by itself prove that an action was authorised.
Sources & change
Record source freshness, retrieval outcomes, prompt versions and deployment identifiers where available.
An observed shift needs investigation; a changed metric does not establish its cause.
The observability path
From individual steps to accountable action.
Correlate workflow events, preserve the evidence that matters and surface it in operational views. An exception needs a trace and an owner, not just another counter. This illustration shows the approach rather than live monitoring data.
01
Collect deliberately
Capture useful timings, outcome states and version references at agreed boundaries. Define where content is necessary, where metadata is enough and what must never be recorded.
02
Connect the evidence
Link application steps to model calls, retrieved sources, tools and confirmed business outcomes. Make failed handoffs, missing records and sampling limits visible.
03
Close the loop
Route actionable failures to accountable owners, review uncertain quality signals and feed confirmed issues into regression tests. Verify the outcome of a fix rather than merely clearing an alert.
Instrumentation & tracing
Follow the request beyond the model call.
We instrument the application boundary, retrieval, model calls, tool execution and human handoffs. Correlated traces let your team investigate a slow or incorrect outcome without piecing together disconnected logs.
Request & workflow identity
Link spans with trace and run identifiers. Preserve parent-child relationships through queues and agent delegation without putting personal data or raw prompts in metric labels.
Versioned context
Attach model, prompt, configuration and deployment versions where supported. Record source references and tool contracts so a change can be compared under its actual operating conditions.
Confirmed action state
Distinguish a tool request from accepted execution, confirmed completion and reconciliation. Capture retries and idempotency outcomes so repeated attempts do not look like independent successes.
Telemetry health
Check dropped spans, collector failures, sampling coverage and delayed events. Missing evidence should be visible rather than silently interpreted as a healthy application.
Business & engineering views
Different owners need different answers.
We build focused views around the decisions your team makes. An operations lead needs exceptions and unresolved work; engineering needs causal traces; business owners need outcomes, adoption and cost with clear definitions.
Outcome definitions
Agree what counts as completed, abandoned, escalated or corrected. Use the business system's confirmed outcome when available instead of treating a generated answer as completion.
Meaningful segmentation
Compare workflow, environment, model and release cohorts. Control cardinality and access when segmenting by customer; avoid exposing one tenant's records to another.
Baseline & objectives
Establish representative traffic and agree service objectives for relevant journeys. Thresholds and error budgets come from your risk and operating needs, not universal AI benchmarks.
Honest coverage
Show sample sizes, time windows and missing observations. Separate inferred quality, reviewed outcomes and operational availability so a single score does not conceal uncertainty.
Quality & behaviour
Notice degradation. Investigate before deciding.
Production monitoring complements offline evaluation. Live inputs, changing sources and user behaviour expose conditions a test set may not contain, but not every production response has an immediately verifiable correct answer.
Targeted evaluators
Apply schema checks, groundedness indicators, task-specific rules and calibrated model judges to agreed samples. Record evaluator versions and cost, with human checks on false positives and negatives.
Feedback & review queues
Connect ratings, corrections and support escalations to the original run. Prioritise consequential cases and give reviewers enough context without collecting unnecessary sensitive content.
Change comparison
Compare releases using relevant cohorts and account for traffic mix and seasonality. A canary or controlled experiment may provide stronger evidence than an uncontrolled before-and-after chart.
Regression learning
Turn reviewed failures into redacted or synthetic test cases where permitted. Track ownership and remediation, then evaluate fixes before expanding a release.
Alerts & incident response
An alert needs an owner and a next action.
We connect meaningful signals to escalation and recovery procedures. Paging on every unusual answer creates noise; silently collecting failures without a response path leaves the underlying risk unchanged.
Actionable thresholds
Use sustained errors, latency changes, spend anomalies and confirmed boundary violations where appropriate. Tune thresholds against a baseline, with severity, deduplication and an accountable owner.
Triage context
Include safe trace references, affected releases and scope of impact. Keep sensitive prompts and outputs out of paging messages and broad-access collaboration channels.
Controlled recovery
Define fallbacks, workflow pause, model routing changes and rollback options. Any automated intervention needs explicit authorisation, tested conditions and safeguards against making an incident worse.
Runbooks & exercises
Test notification delivery and rehearse selected failure scenarios. Record decisions and follow-up work; on-call coverage and response commitments are agreed separately.
Privacy & access
Visibility without indiscriminate recording.
AI traces can contain customer records, internal documents and sensitive instructions. We define collection and access rules before instrumentation, including external processing paths and the limits of diagnostic capture.
Minimise at the source
Prefer operational metadata and source identifiers. Redact or omit sensitive content before export, never capture credentials, and test redaction rather than assuming a filter is sufficient.
Access & isolation
Use role-based access, environment separation and tenant-aware controls. Restrict diagnostic content more tightly than aggregate dashboards and record access to sensitive evidence where required.
Retention & processing
Agree storage regions, retention, deletion and approved vendors based on your obligations. Self-hosting can change the processing path but does not automatically establish compliance.
Audit evidence
Keep accountable records of consequential actions and approvals separate from sampled debug traces. Define completeness, integrity and retention requirements; observability storage is not automatically an immutable audit ledger.
Operating costs & reliability
The monitoring system also needs limits.
Instrumentation should help the application, not become its next bottleneck. We balance investigation needs with telemetry volume, evaluator costs and the reliability of the collection path.
Sampling strategy
Use deliberate sampling for diagnostic traces and retain agreed high-priority cases. Show the sampling policy; do not present sampled events as an exact count of all failures.
Bounded overhead
Measure instrumentation latency, export volume and storage growth. Use bounded queues and appropriate failure behaviour without weakening required action or approval records.
Cost allocation
Separate model consumption, evaluation, telemetry storage and infrastructure costs. Keep estimation assumptions visible and set explicit budgets for expensive review jobs.
Service ownership
Document dashboards, alert routes, collectors and access changes. Review thresholds as traffic evolves, and agree who maintains the system after handover.
Tools & frameworks
Fit the stack to your operating environment.
These are technology options, not a required bundle. We prefer extending your existing monitoring and incident tools when they provide the needed evidence, access controls and operating fit.
Application instrumentation
OpenTelemetryPython / TypeScript SDKsStructured application logs
Use trace context and consistent attributes across the application, retrieval and tools. Provider and framework support varies; verify available spans rather than assuming complete visibility.
AI traces & review
LangfuseLangSmithArize Phoenix
Evaluate tracing, prompt versioning, dataset and review capabilities against the actual framework and deployment. Hosted and self-hosted options have different licensing, access and processing constraints.
Metrics, logs & infrastructure
PrometheusGrafanaOpenTelemetry CollectorLoki / Tempo
Connect AI-specific events to service and infrastructure health. Choose storage and deployment options to match volume, operational ownership and retention.
Quality checks & regression
promptfooRagasDeepEvalpytest / Vitest
Use targeted evaluators and workflow tests rather than one generic score. Confirm metric applicability, calibrate judges and bound evaluation spend.
Events & reporting
PostgreSQLClickHouseQueue / event integrations
Choose operational or analytical storage based on event volume and queries. Preserve correlation and access rules when linking outcomes from CRM, support or workflow systems.
Alerting & response
Grafana AlertingPagerDuty / OpsgenieSlack / Microsoft TeamsCI/CD integrations
Route alerts into your existing incident process with safe links, tested notifications and release context. Tool selection does not imply a managed 24/7 response service.
Capabilities, deployment options and terms vary by vendor and version. Telemetry, automated judging and hosted storage must follow the approved data-processing rules; an observability tool does not confer security or compliance certification.
Delivery & outputs
Start with one workflow worth understanding.
We agree the operational question first, then instrument a bounded path and validate that the evidence supports a decision. Wider coverage follows measured usefulness, rather than collecting everything on day one.
- 01
Map journeys & decisions
Identify the AI workflows, business outcomes, owners and blind spots. Agree the collection boundary and the decisions monitoring should support.
Output: An instrumentation map, metric definitions and data-handling scope.
- 02
Instrument a critical path
Connect request, retrieval, model and tool events for an agreed workflow. Verify correlation, redaction, confirmed outcomes and telemetry failure behaviour.
Output: A usable end-to-end trace and verified collection controls.
- 03
Build views & establish a baseline
Create engineering and business views from representative traffic. Define samples, quality checks and relevant service objectives with your team.
Output: Dashboards, baseline observations and documented signal definitions.
- 04
Connect alerts & recovery
Tune actionable thresholds, assign owners and connect the existing incident process. Rehearse agreed failure and recovery scenarios.
Output: Tested alert routes, triage context and operating runbooks.
- 05
Validate & hand over
Check instrumentation overhead, access, retention and dashboard interpretations. Document limitations and the next set of workflows to cover.
Output: Code, configuration, operating documentation and an agreed ownership plan.
A bounded first sprint
Start with a paid two-week sprint and one agreed goal, without a required long-term commitment. A first scope can connect an important workflow and initial operating views. Longer observation periods, additional integrations and broader rollout may require further work.
What your team brings
An application owner, access to relevant code and events, representative workflows and the people responsible for incidents and data handling. We agree environments, processing permissions and sensitive-data restrictions before exporting telemetry.
Ownership & limitations
Leave clarity with the team running the system.
Monitoring can reveal evidence of a problem; it cannot guarantee answer correctness, prevent every incident or see inside every third-party model. We document collection gaps, sampling and assumptions alongside the operating views.
Handover that is usable
Your code belongs to you from day one. We hand over instrumentation, configuration, dashboard definitions and runbooks, subject to third-party licences. Your team should know how to follow a trace, interpret a metric and change an alert safely.
Ongoing operation is explicit
Storage, evaluation and infrastructure have continuing costs. Maintenance, on-call coverage and response commitments are agreed separately; configured notifications alone are not a managed incident-response service.
Related engineering & guides
Connect production evidence to better releases.
Pair operational visibility with evaluation, adversarial assessment and an intentional audit design.
Questions before instrumentation
The practical details.
How is monitoring different from AI testing and evaluation?
Testing and evaluation assess defined cases, often before release. Monitoring observes how the deployed application behaves with live traffic. Production review can reveal new regression cases, while offline evaluation helps verify changes before release. Neither replaces the other.
Can you add this to an application we already run?
Yes, subject to access and supported instrumentation. We start with a workflow map and available application, provider and business-system events. Some internals may remain inaccessible; we document coverage gaps rather than implying complete visibility.
Can you detect every hallucination or unsafe answer?
No. Groundedness checks, calibrated judges, feedback and sampled human review can surface useful signals, but they have error rates and coverage limits. High-consequence workflows also need enforced permissions, validation and human decision paths.
Do you need to store every prompt and response?
Not necessarily. Many operational metrics work with metadata alone. When content is needed for investigation, we agree minimisation, redaction, access, sampling and retention. Credentials should never enter telemetry, and sensitive diagnostic capture needs an approved processing path.
Can the monitoring tools run in our own environment?
Where the selected tools and licences support it, yes. We assess infrastructure, storage, upgrades and access controls alongside the software. Self-hosting does not remove security responsibilities or guarantee compliance.
Does this include 24/7 support or automatic remediation?
Not by default. The engagement can instrument workflows, configure alerts and prepare runbooks. On-call coverage, response times, ongoing operation and automated interventions require a separate explicit scope and accountable owners.
Will observability provide a compliance audit trail?
It can provide useful evidence, but sampled traces alone are not a complete audit ledger. Required action and approval records need their own coverage, integrity, access and retention controls. Legal or regulatory obligations should be confirmed with your responsible advisers.
How do we begin?
Start with a paid two-week sprint, one agreed goal and no required long-term commitment. A realistic first scope can instrument one important workflow and establish initial views; wider coverage and sustained baselines may need more time. Your code belongs to you from day one, subject to third-party licences.
Start with the blind spot
Bring the workflow.
Let's make its behaviour visible.
Tell us what your AI does, what is hard to investigate and who needs to act when something goes wrong. We'll define a useful first scope.