Governed AI for LRL staff — one pipeline, many front doors
Phase 0 — POC
2026-04-27
The problem
Staff already use public AI services — without audit, policy, or visibility.
No single answer to "who used what model on what content, and what did it cost?"
No defensible position for Legal, E&O, or a future client question.
No durable record when staff change roles, leave, or are asked what was sent.
If we can't see it, we can't govern it. If we can't govern it, we can't defend it.
Where this actually helps
Concrete tasks staff are already doing — most of them in a browser tab, outside any LRL system.
Scope a long RFP
80-page RFP → one-page summary of scope, deadlines, evaluation criteria. 5 minutes instead of an afternoon.
Draft a tricky email
Tone shifts on a difficult client thread. First-pass response that the engineer can edit, not start from scratch.
Find the right clause
"What does the OBC say about X" — pointed lookup mid-draft, without losing flow or trusting random web sources.
Field notes → report draft
Raw site notes transformed into a structured deliverable draft. Engineer reviews, doesn't retype.
Summarize a transcript
Hour-long meeting recording → action items, decisions, follow-ups. In the inbox before everyone is back at their desk.
Sanity-check a doc
Long deliverable → consistency, terminology, missing references flagged before it goes for senior review.
None of these need an "agent". They need a fast, governed text-in / text-out loop with curated defaults.
The same task, before and after
Scenario: a project manager scoping a new 80-page RFP that landed this morning.
Today — without the platform
Open a browser, sign in to a personal ChatGPT account.
Paste RFP excerpts — no scan, no redaction.
Get a summary, copy-paste it into a Word doc.
Cost is on someone's personal credit card.
If asked next month "what did you send?" — no answer.
If a client asks "is our material on an AI provider?" — no answer.
With the platform
Open LibreChat, already signed in via Entra.
Pick the "RFP Summary" preset.
Scanner checks the input before send; flags surface inline.
Summary returns; audit row written; cost lands on the dashboard.
"What did you send?" → one query against the audit DB.
"Is our material on a provider?" → provider, posture, retention all on record.
The visible product is the same: faster scoping. The invisible product is the audit trail.
What this is
A self-hosted, multi-interface, multi-model AI platform for LRL staff.
Built around LibreChat (the web UI) and a LiteLLM proxy (the gateway).
A dedicated governance & audit layer developed in-house — Postgres ledger, admin dashboard, pre-send scanner, preset library.
Single architectural invariant: every AI request, from every interface, passes through the gateway.
What this is not
Not an agent. Reads prompts, returns text. It does not send mail or modify files on your behalf.
Not a client-data playground. Pilot launches non-client-data only; broader postures need Legal sign-off.
Not a Microsoft 365 Copilot replacement. Complementary, not competitive.
Not public. Internal LRL/Englobe users only.
Not a fork of LibreChat. Configure and extend; never patch upstream.
Architecture
flowchart TB
A1[LibreChat]
A2[Tray]
A3[Outlook]
A4[CLI]
AUTH[Entra SSO]
L[LiteLLM gateway]
S[Scanner]
M1[Anthropic]
M2[Azure OpenAI]
M3[Ollama]
P[Audit DB]
D[Dashboard]
A1 --> AUTH
A2 --> AUTH
A3 --> AUTH
A4 --> AUTH
AUTH --> L
L --> S
L --> M1
L --> M2
L --> M3
L --> P
P --> D
The gateway choke point
Provider-agnostic audit ·
Interface-agnostic audit ·
Upgrade-safe ·
Key management in one file ·
Budget enforcement
sequenceDiagram
autonumber
participant U as User
participant UI as Interface
participant G as Gateway
participant P as Provider
participant DB as Audit DB
U->>UI: prompt
UI->>G: request
G->>G: scan + redact
G->>P: forward
P-->>G: response
G->>DB: audit row
G-->>UI: response
UI-->>U: rendered
If a feature needs to bypass the gateway, the feature needs to be rethought.
Right model for the right job
No single model is best at everything. Defaults matter more than menu width.
Provider
Use
Region
Phase
Anthropic Claude
Long-form drafting, document analysis, code
API / Bedrock Canada
0+
Azure OpenAI
General chat, lower-cost defaults, Office adjacency
Canada
1+
Local (Ollama / vLLM)
Confidentiality-sensitive, offline-capable
On-prem GPU
2+
Curated presets point staff at the right default — not a menu they have to think about.
Many front doors
Phase 0–1
LibreChat — primary web UI
Admin dashboard — governance
Phase 2
Windows tray — global hotkey, clipboard transforms
Developer CLI — terminal-native
Phase 3
Outlook add-in — draft, summarize, tone, actions
Browser extension — optional, demand-driven
Always
Entra ID single sign-on
Same gateway, same audit, same budget
Governance & audit
✓PostgreSQL audit DB — authoritative ledger: who, when, which model, what cost.
✓Admin dashboard — read-only activity, costs, and policy-event views.