AI service desk demoLive operations workspace
Demo data
Agentic framework

Client challenge to production AI agent

A deeper portfolio view of how OpsDesk AI would be delivered with client teams: discovery, scalable agent design, LLM prototyping, RAG with pgvector, reliability, ROI, and reusable deployment components.

View system designInspect AI runs
Target deflection38%Repeat enquiries resolved with approved playbooks
Manual time saved11.4hEstimated weekly reduction from triage and drafting
RAG citation coverage94%Answers grounded in retrieved sources
ROI payback7 wksBased on service desk hours and avoided escalations

Delivery lifecycle

How the build moves from client discovery into production operations.

01
Domain discoveryClient teams + AI engineer

Interview operations, support, finance, and engineering stakeholders to capture domain challenges, constraints, and measurable success metrics.

Success metrics: first response time, manual touches, escalation rate, quote cycle time.
02
Solution translationTechnical advisor

Convert business requirements into an agentic architecture with clear tools, memory policy, retrieval scope, human approval gates, and audit events.

Output: agent blueprint, risk register, data contracts, guardrails.
03
Prototype agentsAI engineer

Build LLM agents with prompt structures, orchestration logic, RAG retrieval, vector search, deterministic validators, and replayable evaluations.

Stack: AI SDK boundary, pg-boss jobs, pgvector, Drizzle, structured outputs.
04
Production transitionMLE owner

Move from proof-of-concept to production by tracking reliability, latency, cost, feedback, rollout cohorts, and measurable ROI.

SLOs: 99.5% job completion, p95 under 2s for retrieval, zero autonomous commitments.
LLM agent system

Prompted workers with retrieval, memory, and validation.

The agentic framework is deliberately modular: each worker has a prompt contract, tool permissions, structured output schema, retrieval policy, and audit trail.

Intake classifierNormalises messy enquiries into ticket category, priority, sentiment, risks, and missing information.
Retrieval plannerBuilds query intents and searches pgvector knowledge chunks from policies, playbooks, tickets, and documents.
Drafting workerGenerates customer-safe replies and internal next actions using the retrieved evidence bundle.
Validation judgeChecks schema validity, confidence, citation coverage, compliance language, and forbidden actions.
Approval handoffRoutes drafts, document mismatches, and low-confidence decisions to a human owner before action.
pgvector retrieval

Knowledge that can be measured.

Production mode stores support playbooks, policies, ticket history, and document extracts as embedded chunks. Retrieval is logged with source metadata so every AI answer can cite the evidence it used.

HNSW cosine index1536-dimension embeddings with source and tenant filters.
Replayable contextInput hashes, retrieved chunk IDs, prompt versions, and output JSON stay linked.
Evaluation loopGolden scenarios score answer quality, citation coverage, latency, and cost.

Reusable deployment components

Framework pieces that make the next client deployment faster.

Prompt contractsVersioned system prompts, JSON schemas, model settings, and test fixtures for each AI action.
RAG pipelineSource ingestion, chunking, embeddings, pgvector HNSW search, metadata filters, and citation payloads.
Evaluation harnessGolden scenarios, regression checks, hallucination labels, latency budgets, and cost per resolution.
Advisor packDiscovery templates, ROI calculator, rollout checklist, adoption plan, and stakeholder success review.
Production AI/dataFull-stack service workflow with persisted tickets, background jobs, auditable runs, and human review.
GenAI hands-onLLM prompts, RAG, vector database design, agent orchestration, validators, and retrieval-aware drafting.
Python + MLEDesigned for model development, deployment, monitoring, evaluation, and reusable MLE workflows.
Trusted advisorFrames technical choices around client outcomes, adoption risk, success metrics, and measurable ROI.