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 (USD).
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 Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
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.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Secure Mobile Solution in Australian Defence Ecosystem
Problem: Secure data workflows were required in a regulated environment with strict access controls.
Solution: Hardened architecture with strict auth, encrypted storage, and audit-friendly engineering patterns.
Outcome: Deployed securely within a regulated ecosystem with clear handoff and operational guidance.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and results were inconsistent.
Solution: RAG with hybrid retrieval and reranking, plus grounded answers and safer fallback behavior.
Outcome: Reliable answers with <10s response times and measurable improvements on real queries.
Ops Automation with AI + n8n
Problem: Manual approvals and CRM syncing created delays and data inconsistencies across tools.
Solution: Event-driven automation with validation gates and AI-assisted classification where it improved routing.
Outcome: Reduced manual workload significantly with more reliable workflows and operator visibility.
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
Compliance & Data (US)
For US teams, we build with auditability in mind: clear access boundaries, least-privilege roles, and reviewable operational controls.
We can align delivery with SOC 2 / ISO-friendly practices (without claiming certification): evidence-ready logs, secure-by-default config, and clear ownership.
- SOC 2 / ISO-friendly implementation patterns (no certification claims)
- Least-privilege access and permission boundaries
- Security review checklists for auth, payments, and data flows
- PII-safe logging + incident response playbooks (on request)
- Retention and deletion flows where required
- NDA + vendor onboarding docs on request
Timezone & Collaboration (Americas)
We support teams across the Americas with meeting windows that work for EST/CST/MST/PST.
We keep delivery predictable with weekly milestones, concise async updates, and written decisions to reduce calendar load.
- Americas overlap with EST/PST-friendly windows
- Async-first updates with written decisions
- Weekly milestone demos + change control
- Fast turnaround on blockers and clarifications
- Clear owner per workstream and escalation path
Engagement & Procurement (US)
US-friendly engagement structure: clear SOWs, milestone billing, and invoice cadence that fits typical procurement workflows.
If you need vendor onboarding artefacts, we can provide security posture summaries and delivery process documentation.
- USD invoicing and milestone-based payment schedules
- SOW + scope lock options for fixed-scope work
- Time-and-materials for evolving requirements
- Procurement-ready documentation on request
- Optional paid discovery to de-risk delivery
Security & Quality (US)
We ship with a security-first checklist and performance budgets—so releases stay stable under real traffic.
Expect clean PRs, reviewable changes, and production-ready testing from day one.
- Threat-aware checks for auth, roles, and sensitive data flows
- CI-friendly testing: unit + integration + critical path smoke tests
- Performance budgets (Core Web Vitals-minded) and bundle checks
- Structured logging + error tracking hooks (Sentry-ready)
- Rollback-safe releases and clear release notes
Want help with LLMOps & observability?
Get a clear plan for United States teams—scope, timeline, and next steps. USD-based engagements.
Reply within 2 hours. No-pressure consultation.