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 Germany teams (EUR).
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 (EU)
For Germany/EU delivery, we keep GDPR-first patterns: data minimisation, purpose-limited storage, and explicit access boundaries.
We can work under a DPA (template available on request) and implement pragmatic retention/deletion flows when needed.
- GDPR-first architecture patterns (generic, no legal claims)
- DPA template available on request
- Retention/deletion and export flows where required
- Least-privilege access and safe logging defaults
- Documented data flows and access boundaries
Timezone & Collaboration (EU)
We align to EU working hours with CET-friendly collaboration windows and async progress updates.
We keep delivery predictable: weekly milestones, documented decisions, and clear scope control.
- EU overlap with CET-friendly windows
- Async-first delivery with written decisions
- Weekly milestone demos and progress checkpoints
- Clear change control to avoid surprises
- Escalation path for blockers and risks
Engagement & Procurement (EU)
We support procurement-friendly engagements with clear scopes, milestone plans, and documentation that stakeholders can review.
For EU teams, we can structure invoices and milestones for EUR-based engagements where appropriate.
- EUR-based engagements and invoicing options
- Discovery-first option to reduce delivery risk
- Milestone-based billing and scope sign-offs
- Vendor onboarding documentation on request
- Transparent change control and approvals
Security & Quality (EU)
We prioritise reliability: reviewable PRs, predictable releases, and tests that protect critical paths.
Performance budgets and clear release discipline keep the product stable as it grows.
- CI-friendly testing: unit + integration + smoke tests
- Performance budgets + bundle checks
- Release checklist + rollback-safe deployments
- Security checklist for auth and sensitive data flows
- Observability hooks (logs + error tracking) ready for production
Want help with LLMOps & observability?
Share your requirements for Germany delivery. EUR-based engagements.
Reply within 2 hours. No-pressure consultation.