AI Development
LLMOps & Observability
Ship AI features with production discipline: tracing, evaluation tests, prompt/versioning, feedback loops, and cost/latency monitoring. We make failures visible and quality measurable so your AI system can improve safely over time. Delivery aligned to United States teams (PRO).
Overview
What this service is
LLMOps is the operational layer for LLM features: observability, evaluation, prompt/version control, and release discipline.
We add telemetry and tooling so you can debug issues, track quality regressions, and optimize latency and spend.
Delivery includes dashboards, alerting, and practical runbooks so your team can operate AI features confidently.
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
See where quality fails with traces and datasets
Prevent regressions with eval gates and CI checks
Reduce spend with caching and model routing
Improve latency with streaming and tuning
Operate safely with alerting and runbooks
Make improvements measurable, not subjective
Features
What we deliver
Tracing and request logs
End-to-end traces for prompts, retrieval, tool calls, and outputs to identify bottlenecks and failures.
Evaluation harness
Golden datasets, automated scoring, and regression checks for accuracy, relevance, and safety.
Prompt and config versioning
Version prompts, retrieval settings, and safety policies with safe rollouts and rollback paths.
Feedback loops
Collect user feedback and label failure modes to drive iterative improvements and prioritization.
Cost and latency monitoring
Dashboards for token spend, provider costs, latency distributions, and cache hit rates.
Alerting + runbooks
Alerts for spikes, failures, and quality drops with documented mitigation steps for fast response.
Process
How we work
Audit
Review current stack, failure modes, and metrics.
Instrumentation
Add traces, logs, and structured telemetry.
Evals
Create datasets and regression checks.
Optimization
Caching, routing, and latency improvements.
Ops handoff
Dashboards, alerts, and runbooks.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Stabilize a deployed chatbot
Add tracing, evals, and feedback loops to reduce bad answers and make failures visible.
Reduce AI spend
Introduce caching, routing, and prompt optimization to cut token usage without hurting UX.
Debug retrieval quality issues
Instrument retrieval and reranking to see what’s being fetched and why answers degrade.
Safe prompt changes
Add versioning and rollout discipline so prompt updates don’t break production behavior.
Enterprise rollout readiness
Add audit-friendly logging, alerting, and ops runbooks for team-scale adoption.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
FAQ
Frequently asked questions
If you’re shipping to real users, yes. Even lightweight telemetry and evals prevent silent regressions and make iteration faster.
Yes. We start with small golden datasets and expand coverage over time, focusing on the highest-impact failure modes.
We use caching, model routing, prompt optimization, and retrieval tuning—then validate changes with evals before rollout.
Yes. We can keep interfaces provider-flexible and track costs/quality per provider.
Yes. We instrument quality signals and set alerts for spikes in failures, escalations, or low-confidence responses.
Dashboards, runbooks, and notes on how to extend evals, tune retrieval, and roll out changes safely.
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Regional
Delivery considerations for your region
Data and risk discovery (United States)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (United States)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (United States)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (United States)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want help with LLMOps & observability?
Get a clear plan for United States teams—scope, timeline, and next steps. proposal currency confirmed before contracting.
We’ll review the context and reply with a practical next step.