Forward-deployed AI engineering

Production AI systems that think, act, and prove their work

Agentic workflows, RAG/GraphRAG, evaluation, and human control for enterprise teams.

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Agents

Research agentRetrieve and synthesize

Analysis agentReason and plan

Execution agentTake action

QA agentValidate and test

Business request10:24 AM

Analyze supplier risk exposure and recommend mitigations.

Memory

Supplier risk indexVector store

Tools

Web searchInternal dataGraphRAGPython sandboxEmail draft

Web searchCompleted

GraphRAG queryCompleted

Generate reportIn progress

Evaluation

Factual accuracy0.92

Groundedness0.88

Completeness0.91

Result: Pass

Human approval

Review generated report and recommended actions.

Action

Send to stakeholder and log execution.

Completed

Agentic workflows

Multi-agent systems that plan, reason, use tools, and deliver outcomes.

Knowledge systems

RAG / GraphRAG pipelines that ground answers in your data and relationships.

Evaluation & controls

Test, measure, and govern with human approval where it matters.

Operating system

Systems that move from request to controlled action

Leone Intelligence Systems builds the workflow, memory, evaluation, and approval surfaces around the real work so AI can ship as a production system instead of a disconnected demo.

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Work intake

Capture the business request, constraints, data boundaries, and approval points before any agent acts.

Knowledge grounding

Connect RAG and GraphRAG retrieval to the records, policies, and relationships that explain the work.

Tool execution

Let agents call tools, draft outputs, and prepare actions inside a measured execution path.

Human approval

Keep critical recommendations, external sends, and production changes behind explicit review.

Evidence stackProduction readiness

Architecture

Agent topology, integrations, data boundaries, and deployment plan.

Evaluation

Quality dimensions, regression harnesses, trace review, and acceptance gates.

Controls

Human approval, policy guardrails, audit logging, and escalation paths.

Deployment

Rollout states, monitoring, rollback planning, and operational handover.

Proof before scale

Every system has to prove why it should be trusted

The delivery path includes architecture evidence, evaluation results, controls, traces, and deployment proof. No fake logos, no inflated outcomes, no opaque automation.

View NDA-safe proof model

Forward-deployed AI engineering

Built with the people who own the workflow

A compact team works close to the operating context, then leaves behind a system your team can inspect, approve, monitor, and improve.

Diagnose

Embed with the team, map the workflow, and define the system boundary.

Build

Engineer the agentic workflow, retrieval layer, UI, and integration paths.

Evaluate

Run quality, safety, latency, cost, and human-review checks before release.

Operate

Deploy with monitoring, evidence logs, and a clear improvement loop.

Have a workflow where AI needs to reason, act, and still stay controlled?

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