AI engineering

Generative AI Services

Design secure AI assistants, retrieval systems, and workflow copilots that help teams answer, act, and improve faster.

Overview

We turn generative AI from a demo into a governed business capability: use-case discovery, data preparation, prompt systems, model routing, evaluation, guardrails, and rollout plans that fit real operations.

We design AI services around the work they must perform: ChatGPT-style assistants, knowledge-base search, RAG over PDFs and policies, model behavior, tool permissions, human review, and measurable quality. The goal is a reliable workflow that can answer, recommend, draft, escalate, and improve with evidence.

Use-case selection, RAG architecture, prompt systems, OpenAI or model routing, vector search, and tool-call design.
Evaluation suites for accuracy, refusal behavior, risk, latency, and cost before launch.
Governance layers for access control, source grounding, audit logs, and review loops.

AI command viewRAG quality, source confidence, and review status in one operating panel.
**RAG**Grounded answersEvalPrompt scoringAuditLogged actions

Live AI pattern

Prototype an assistant before it touches production.

A useful AI service starts with a small, governed workflow: retrieve the right knowledge, check confidence, draft the action, and leave a clean review trail.

Grounded retrieval

Connect policy files, CRM records, PDFs, and help content so answers cite approved sources instead of guessing.

Evaluation harness

Test model behavior against real tickets, risky prompts, refusal rules, latency targets, and cost limits before launch.

Human approval

Route sensitive actions through reviewers with notes, source trails, and escalation reasons preserved for audit.

Ready state

Retrieve policy sources

Ready. Click Run Demo to simulate the workflow.

const ticket = await classify(message)
const sources = await retrieve(ticket.topic)

return agent.draft({
  tone: "clear",
  sources,
  escalate: ticket.risk > 0.72,
})

Our Capabilities

AI agent discovery

Map work queues, policies, data access, escalation rules, and measurable moments where an agent can safely assist.

RAG knowledge systems

Connect documents, databases, and service content into grounded responses with citations and freshness controls.

Prompt and tool design

Build prompts, tool calls, approval flows, and human handoffs around the way teams already work.

Model evaluation

Score quality, latency, cost, and risk with repeatable test suites before production release.

AI governance

Add access control, logging, policy checks, and review dashboards for responsible adoption.

ChatGPT integration

Embed conversational workflows into portals, CRMs, knowledge bases, and internal operating tools.

Our Work in Generative AI

AI Lab

Knowledge assistant prototype

Retrieval, source ranking, confidence checks, and reviewer handoff for customer support and internal teams.

Governance

Responsible AI control room

Policy checks, prompt evaluations, audit trails, and release scoring before AI workflows reach production.

Automation

Agent workflow orchestration

Tool calls, escalation rules, ticket summaries, and quality review loops for repeatable business tasks.

Fueling Innovation

Rapid Labs

Prototype high-value workflows, integrations, and AI ideas before large-scale investment.

Rapid Ventures

Shape product bets, launch MVPs, and scale the systems that prove commercial traction.

45%Faster response drafting in pilot workflows

12+Agent use cases prioritized per discovery sprint

24/7Knowledge access with governed escalation paths

Insights

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Got questions? We've got answers.

A production AI service needs clean data access, tested prompts, security boundaries, monitoring, fallback paths, and a clear owner for continuous improvement.