CAPABILITY 08

LLM & Generative AI

Build assistants and agents that use your business knowledge and work within your access rules.

BUSINESS CONTEXT AND APPROACH

Build assistants and agents that use your business knowledge and work within your access rules.

An enterprise assistant must do more than generate fluent text. It must retrieve the right information, respect the user’s access and make its answer traceable. We start with a defined job and an evaluation set before selecting the model or interface.

Policies, contracts and operating procedures often sit across document stores and business applications. We design retrieval and agent workflows that preserve access controls, identify the source of an answer and route uncertain or consequential decisions to a person.

OUTCOME AND MEASURES

A tested AI application, an evaluation report and a clear route to production.

Before delivery starts we agree baselines for:

  • Answer quality against an approved test set
  • Source coverage and unsupported-answer rate
  • Latency, user feedback and cost per request
LLM & GENERATIVE AI DELIVERY ARCHITECTURE

Connect, retrieve, reason, review and operate.

A reference delivery architecture for assistants and agents that answer from approved knowledge, keep access rules intact and stay traceable in production.

ANIMATED SCENARIO

Find a policy answer with evidence

The scenario shows a question answered from approved policies, with permissions applied, sources cited and a reviewer in the loop.

REPRESENTATIVE USE CASES

Use cases shaped
around real work.

Representative patterns are tailored to your landscape, controls, data readiness and business priorities.

01

Policy knowledge assistant

Give employees cited answers from approved policies, with permission checks applied before retrieval.

02

Contract review support

Extract obligations and surface clauses for a reviewer, retaining references to the original document.

03

Service-desk assistance

Draft a response or proposed next step from known resolutions, with human review before customer-facing action.

WHAT WE DELIVER

Artifacts your teams can use.

  • Use-case discovery and prototypes with defined success measures
  • RAG assistants with source citations and permission-aware retrieval
  • LLM selection, prompt design and fine-tuning where justified
  • API-connected agents with approved tools and human review
AGREED BASELINES
01Answer quality against an approved test set
02Source coverage and unsupported-answer rate
03Latency, user feedback and cost per request
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