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. Delivery aligned to United States teams (PRO).
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 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
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.
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
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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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 AI maintenance retainer?
Book a service call with United States timezone overlap (Americas overlap (EST/PST-friendly)). proposal currency confirmed before contracting.
We’ll review the context and reply with a practical next step.