WARQLINE
WQL.MODULE.AI// ONLINE
/ 02 — AI SOLUTIONS

AI that ships to production

Generative AI consulting and delivery for enterprises and startups — from strategy sprints to agents, RAG platforms, and LLMOps. Built by the same engineers who run your cloud.

// AI.STRATEGY[ 01 ]

GenAI strategy & readiness

Use-case discovery, ROI assessment, and governance frameworks. We map where generative AI actually moves your P&L — and where it doesn't.

// AI.AGENTS[ 02 ]

AI agents & agentic automation

Support agents, back-office copilots, and autonomous operations pipelines that execute multi-step workflows — with human-in-the-loop guardrails.

// AI.RAG[ 03 ]

RAG knowledge platforms

Secure retrieval over your enterprise data. Private LLMs, vector search, and access-controlled pipelines that keep proprietary data proprietary.

// AI.COPILOT[ 04 ]

Custom copilots & LLM apps

Chat assistants, document intelligence, and workflow copilots built into the tools your teams already use — shipped to production, not demoed.

// AI.MLOPS[ 05 ]

MLOps / LLMOps

Production ML pipelines on AWS Bedrock and SageMaker, GCP Vertex AI, and Azure OpenAI. Versioning, evals, monitoring, and cost control built in.

// AI.SEC[ 06 ]

AI security & compliance

Guardrails, evaluation harnesses, PII redaction, and EU AI Act readiness. Ship AI that survives your security review and your regulator's.

How AI engagements run

[ 01 ]2–3 WEEKS

Advisory sprint

Use-case discovery workshops, feasibility scoring, and a prioritized AI roadmap with cost estimates.

[ 02 ]4–8 WEEKS

Pilot build

One production-grade pilot — agent, RAG platform, or copilot — with evals and a go/no-go readout.

[ 03 ]ONGOING

Scale & operate

Hardening, LLMOps, evaluation and cost control as adoption spreads across teams.

AGENTOPS

Agents propose. People approve.

An agent with production credentials and no approval step is not automation, it is an incident with better marketing. Every agent we build stops at a human before anything changes.

  1. [ 01 / 04 ]

    Detect

    The agent identifies the problem and gathers the evidence for it.

  2. [ 02 / 04 ]

    Explain

    It states what it found, why it matters, and what it would touch.

  3. [ 03 / 04 ]

    Generate fix

    A concrete change, raised as a pull request — never applied directly.

  4. [ 04 / 04 ]

    Human approval

    An engineer reviews and merges. Validation and verification follow.

AI.INTAKE // OPEN

Have a use case? Pressure-test it.

Bring your AI idea to a free 45-minute scoping call. We'll tell you what's feasible, what it costs, and what we'd build first.

Talk to an AI engineer