AI Development
AI Maintenance & LLMOps Retainer
AI features need ongoing care: evals to prevent regressions, monitoring for failures, prompt/version control, cost controls, and security patching. This retainer keeps your LLM/RAG/agent system stable while you ship improvements safely.
Overview
What this service is
A monthly retainer that covers reliability and iteration for production AI systems (agents, RAG knowledge bases, automations, and LLM features).
We maintain a measurable quality loop: eval sets, regression checks, and monitoring so changes don’t break silently.
You get predictable support for incidents, cost tuning, and security hardening—plus documented changes your team can audit.
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
Prevent regressions with evals and regression checks
Keep costs predictable with routing and caching tuning
Improve reliability with traces, alerts, and runbooks
Ship safe iterations with prompt/version control
Reduce security risk with regular hardening
Get incident support when workflows fail
Features
What we deliver
Monitoring + alerting
Tracing, error monitoring, and key KPIs (latency, tool errors, cost) with actionable alerts.
Evals + regression suite
Maintain test sets and scorecards so releases improve quality instead of drifting.
Prompt + version management
Prompt changes tracked and reviewed with rollout notes and safe fallback behavior.
Cost + latency optimization
Caching, routing (small vs large model), retrieval tuning, and token discipline to control spend.
Security patching + guardrail updates
Prompt injection defenses, tool allowlists, RBAC boundaries, and safe action constraints kept current.
Monthly delivery notes
A clear summary of changes, metrics movement, and next-step recommendations your team can audit.
Proof
Built for production
Starter — $900/mo
Monitoring + weekly QA checks, prompt/version updates, and 1–2 small improvements per month.
Growth — $2,400/mo
Evals + regression suite maintenance, cost tuning, incident response support, and 4–6 improvements per month.
Scale — $4,800/mo
Multi-workflow support, permission/guardrail hardening, deeper observability, and prioritized delivery.
Process
How we work
Baseline
Audit current system, metrics, and failure modes.
Instrument
Monitoring, traces, and KPI dashboards.
Evaluate
Eval set + regression checks for core intents.
Iterate
Monthly improvements with release notes and rollbacks.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Stabilize a RAG knowledge base
Improve retrieval quality and reduce wrong answers with evals, tuning, and safer fallbacks.
Keep an agent safe as tools expand
Maintain allowlists, approvals, and RBAC boundaries as new tool actions are added.
Reduce LLM spend at scale
Add routing and caching so usage grows without cost surprises.
Incident support for workflow failures
Debug tool failures and broken webhooks quickly with traces and runbooks.
Continuous improvements
Ship small iterations safely each month with measurable outcomes and clear handoff.
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
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
No. We can take over maintenance for existing LLM/RAG/agent systems after a short baseline review.
Yes. The retainer includes support for production issues. Exact SLA can be agreed based on plan.
We maintain eval sets and regression checks that run before changes are released, then track metrics over time.
Yes. We optimize prompts, add caching, route requests between models, and tune retrieval to reduce tokens and latency.
Yes. We track prompt versions, implement safe rollouts, and document changes with measurable outcomes.
Yes. You receive delivery notes covering changes, metrics, and recommended next steps.
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